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Radio Frequency IDentification (RFID) is intended to supplant legacy (optical) bar code scanning technology found in many logistic and retail applications. RFID is distinguished by inexpensive, low power and compact form factor tags, whose longevity and efficacy are predicated on using passive communication techniques and on-tag power harvesting. Such tags employ backscatter modulation, which does not require any active RF components. As a result, backscatter has become an attractive design choice for short-range communications in power constrained wireless sensor networking scenarios. The purpose of this work is two-fold. First, it aims to expose backscatter communication as an emerging topic to a communication systems-theoretic audience. Since backscatter modulation and on-tag power harvesting efficiency are coupled, it is necessary to re-examine notions of power and spectral efficiency from an energy-constraint perspective; this leads to novel coded modulation schemes for future RFID systems. Further, we investigate RFID MIMO systems where the channel fading encountered has different statistics than the classical Rayleigh fading model. In turn,the trade off between diversity order and spatial multiplexing gains are distinct from wide-area MIMO. [ABSTRACT FROM PUBLISHER]

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

This paper investigates different precoding strategies for rate splitting multiple access (RSMA) in the downlink of multi-cell visible light communication (VLC) networks. Since classical Shannon capacity formula does not hold for VLC, we first provide a lower bound on the channel capacity for RSMA in such interfering networks. Then, we formulate a spectral efficiency maximization problem to jointly find the optimal rate-splitting and transmit precoding. Beside that, since cell-edge users suffer from additional inter-cell interference, we propose to design the precoders of different RSMA signals utilizing coordinated beamforming (CB). Subsequently, aiming to improve the performance of the CB design for RSMA, while maintain a reduced complexity, we introduce two enhanced precoding strategies for RSMA. To the best of the authors’ knowledge such a contribution has not been considered before in the open literature. It is shown in the paper that the formulated optimization problem is non-convex and a sub-optimal, yet, a low complexity solution can be obtained efficiently using semi-definite relaxation combined with successive convex approximation. Through analytical results, we illustrate the flexibility and superiority of the proposed precoding strategies for RSMA over conventional coordinated space division multiple access and non-orthogonal multiple access for different scenarios and network loads. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach of utilizing the reconfigurable intelligent surface (RIS) for joint data modulation and signal beamforming in a multi-user downlink cellular network by leveraging the idea of backscatter communication. We present a general framework in which the RIS, referred to as modulating intelligent surface (MIS) in this paper, is used to: $i$) beamform the signals for a set of users whose data modulation is already performed by the base station (BS), and at the same time, $ii$) embed the data of a different set of users by passively modulating the deliberately sent carrier signals from the BS to the RIS. To maximize each user’s spectral efficiency, a joint non-convex optimization problem is formulated under the sum minimum mean-square error (MMSE) criterion. Alternating optimization is used to divide the original joint problem into two tasks of: $i$) separately optimizing the MIS phase-shifts for passive beamforming along with data embedding for the BS- and MIS-served users, respectively, and $ii$) jointly optimizing the active precoder and the receive scaling factor for the BS- and MIS-served users, respectively. While the solution to the latter joint problem is found in closed-form using traditional optimization techniques, the optimal phase-shifts at the MIS are obtained by deriving the appropriate optimization-oriented vector approximate message passing (OOVAMP) algorithm. Moreover, the original joint problem is solved under both ideal and practical constraints on the MIS phase shifts, namely, the unimodular constraint and assuming each MIS element to be terminated by a variable reactive load. The proposed MIS-assisted scheme is compared against state-of-the-art RIS-assisted wireless communication schemes and simulation results reveal that it brings substantial improvements in terms of system throughput while supporting a much higher number of users. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimization method for protograph-based spatially coupled low-density parity-check (SC-LDPC) codes under window decoding (WD). Previous works on constructing SC-LDPC codes for WD typically focused on optimizing asymptotic performance metrics such as the WD threshold. However, in this paper, it is observed that the WD threshold is not an appropriate metric to sufficiently explain the finite-length behavior of SC-LDPC codes under WD. Thus, we propose a new performance metric, called the window mean parameter, based on a scaling analysis to capture the WD performance more accurately and formulate a code optimization algorithm that optimizes the proposed performance metric. Since the proposed metric depends on the window size, the optimization algorithm can provide a code family of SC-LDPC codes optimized for various target window sizes. Simulation results confirm that the improvement in the proposed metric leads to a finite-length performance improvement, resulting in one to two orders of the frame error rate gain over the conventional SC-LDPC codes for a wide range of window sizes. Furthermore, we investigate structural characteristics of the proposed codes to provide a supplementary explanation for the performance improvement, which also promotes a better understanding of SC-LDPC codes for WD. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) can be deployed as flying base stations (BSs) to leverage the strength of line-of-sight connections and effectively support the coverage and throughput of wireless communication. This paper considers a multiuser communication system, in which a single-antenna UAV-BS serves a large number of ground users by employing non-orthogonal multiple access (NOMA). The max-min rate optimization problem is formulated under total power, total bandwidth, UAV altitude, and antenna beamwidth constraints. The objective of max-min rate optimization is non-convex in all optimization variables, i.e., UAV altitude, transmit antenna beamwidth, power allocation, and bandwidth allocation for multiple users. A path-following algorithm is proposed to solve the formulated problem. Next, orthogonal multiple access (OMA) and dirty paper coding (DPC)-based max-min rate optimization problems are formulated and respective path-following algorithms are developed to solve them. The numerical results show that NOMA outperforms OMA and achieves rates similar to those attained by DPC. In addition, a clear rate gain is observed by jointly optimizing all the parameters rather than optimizing a subset of parameters, which confirms the desirability of their joint optimization. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) technology is promising to enhance wireless communications services by providing smart radio environment. In this paper, we investigate the RIS aided cellular networks with device-to-device (D2D) users, and maximize the sum of the transmission rate of the D2D communications and the cellular networks from a new perspective. In addition to solving the typical resource allocation problems for D2D communications, this paper further optimize the wireless environment by adjusting the position and phase shift of the RIS. To solve this non-convex problem, we propose a novel decentralized double deep Q-network (D3QN) framework for the resource allocation at users and a centralized DDQN for RIS optimization at the base station (BS), which are verified to achieve the near-optimal performance with lower complexity and enhanced robustness. Simulation results illustrate that the proposed framework can achieve higher transmission rates compared to benchmarks, meanwhile meeting the quality of service (QoS) requirements at the BS and D2D users. [ABSTRACT FROM AUTHOR]

This paper proposes a novel scalable reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks. In most previous works on reinforcement learning for network optimization, the network topology is assumed to be fixed, and a different agent is trained for each transmission node—this limits scalability and generalizability. Further, routing and spectrum access are typically treated as separate tasks. Moreover, the optimization objective is usually a cumulative metric along the route, e.g., number of hops or delay. In this paper, we account for the physical-layer signal-to-interference-plus-noise ratio (SINR) in a wireless network and further show that bottleneck objective such as the minimum SINR along the route can also be optimized effectively using reinforcement learning. Specifically, we propose a scalable approach in which a single agent is associated with each flow and makes routing and spectrum access decisions as it moves along the frontier nodes. The agent is trained according to the physical-layer characteristics of the environment using a novel rewarding scheme based on the Monte Carlo estimation of the future bottleneck SINR. It learns to avoid interference by intelligently making joint routing and spectrum allocation decisions based on the geographical location information of the neighbouring nodes. [ABSTRACT FROM AUTHOR]

This paper presents novel numerical approaches to finding the secrecy capacity of the multiple-input multiple-output (MIMO) wiretap channel subject to multiple linear transmit covariance constraints, including sum power constraint, per antenna power constraints and interference power constraint. An analytical solution to this problem is not known and existing numerical solutions suffer from slow convergence rate and/or high per-iteration complexity. Deriving computationally efficient solutions to the secrecy capacity problem is challenging since the secrecy rate is expressed as a difference of convex functions (DC) of the transmit covariance matrix, for which its convexity is only known for some special cases. In this paper we propose two low-complexity methods to compute the secrecy capacity along with a convex reformulation for degraded channels. In the first method we capitalize on the accelerated DC algorithm which requires solving a sequence of convex subproblems, for which we propose an efficient iterative algorithm where each iteration admits a closed-form solution. In the second method, we rely on the concave-convex equivalent reformulation of the secrecy capacity problem which allows us to derive the so-called partial best response algorithm to obtain an optimal solution. Notably, each iteration of the second method can also be done in closed form. The simulation results demonstrate a faster convergence rate of our methods compared to other known solutions. We carry out extensive numerical experiments to evaluate the impact of various parameters on the achieved secrecy capacity. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of joint active and passive beamforming optimization for an intelligent reflective surface (IRS)-assisted multi-user downlink multiple-input multiple-output (MIMO) communication system under both ideal and practical IRS phase shifts. We aim to maximize the spectral efficiency of the users by minimizing the sum mean square error (MSE) of the users’ received symbols. For this, a joint non-convex optimization problem is formulated under the sum minimum mean square error (MMSE) criterion. Alternating minimization is used to break the original joint optimization problem into the separate optimization of the active precoding matrix for the base station (BS) and the matrix of phase shifts for the IRS. While the MMSE active precoder is obtained in closed-form, the IRS phase shifts are optimized iteratively using a modified version (developed in this paper) of the vector approximate message passing (VAMP) algorithm. Moreover, the underlying joint optimization problem is solved under two different models for the IRS phase shifts, namely by assuming $i$) a unimodular (i.e., ideal) constraint on the reflection coefficients and $ii$) a more practical reflection elements termination by a variable reactive load (which inherently introduces the phase-dependent amplitude attenuation in the IRS phase shifts). Simulation results are presented to illustrate the performance of the proposed method under both perfect and imperfect channel state information (CSI) and to show the effect of the practical constraint on the system throughput. The results validate the superiority of the proposed method over the state-of-the-art techniques both in terms of throughput and computational complexity. [ABSTRACT FROM AUTHOR]

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS. [ABSTRACT FROM AUTHOR]

Mobile-Edge Computing (MEC) is an emerging paradigm in the Internet of Vehicles (IoV) to meet the ever-increasing computation demands of smart applications. To provide satisfactory computation performance, it is of significant importance to conduct computation offloading in IoV. In this paper, we investigate a multi-vehicle IoV system assisted by MECs with limited computation resources, where vehicles with complex applications can offload their subtasks to MEC servers. Applications are modeled as interdependent subtasks with general random task graphs, different from existing works with independent ones. To maximize the average logarithmic data processing rate (LDPR), the computation offloading problem is formulated as a time-average optimization with long-term constraints, which results from variable vehicle number, various applications and time-varying communication channels. To reduce the cooperation overhead, we propose a multi-agent Proximal Policy Optimization algorithm (Ly-MAPPO) which requires local observation only to solve the subproblems achieved by Lyapunov optimization technique in real time. In addition, to improve the performance of the Ly-MAPPO algorithm, Graph Convolutional Neural Network (GCN) is introduced to extract inter-dependencies between subtasks. Extensive simulations show that the GCN embedded Ly-MAPPO outperforms other baseline algorithms, e.g., greedy algorithm and gene algorithm, etc., for different traffic loads and computation resources in MEC servers. [ABSTRACT FROM AUTHOR]

In this paper, we consider unmanned aerial vehicles (UAVs) equipped with a visible light communication (VLC) access point and coordinated multipoint (CoMP) capability that allows users to connect to more than one UAV. UAVs can move in 3-dimensional (3D) at a constant acceleration, where a central server is responsible for synchronization and cooperation among UAVs. The effect of accelerated movement in UAV is necessary to be considered. Unlike most existing works, we examine the effects of variable speed on kinetics and radio resource allocations. For the proposed system model, we define two different time scales. In the frame, the acceleration of each UAV is specified, and in each slot, radio resources are allocated. Our goal is to formulate a multi-objective optimization problem where the total data rate is maximized, and the total communication power consumption is minimized simultaneously. To handle this multi-objective optimization, we first apply the scalarization method and then apply multi-agent deep deterministic policy gradient (MADDPG). We improve this solution method by adding two critic networks together with two-stage resource allocation. [ABSTRACT FROM AUTHOR]

In this paper, a reconfigurable intelligent surfaces (RISs)-aided millimeter wave (mmWave) uplink (UL) rate-splitting multiple access (RSMA) system is investigated which targets to achieve better rate performance and enhanced coverage capability for multiple users. The considered UL RSMA model splits the rate for each user by dividing their message into multiple parts and hence exploits all the necessary degrees of freedom to achieve maximum capacity region and high user fairness. In particular, we focus on the sum-rate maximization for considered UL RSMA system subject to joint optimization of power allocation to the UL users and beamforming design, i.e., active receive beamforming at the base-station (BS) and passive beamforming at multiple RISs. To efficiently mitigate high inter-node interference in multi-user scenario, we first provided a low-complex user pairing scheme based on k-means clustering and then develop an effective low-cost alternating optimization framework to solve the joint optimization problem sub-optimally by decoupling the problem into different sub-problems of power allocation and beamforming design. Specifically, the sub-problems of power allocation and beamforming design are solved using successive convex approximation, Riemannian manifold and fractional programming techniques. Later, the unified solution based on block coordinate descent (BCD) algorithm is proposed. Extensive numerical simulations validate that the user-clustering effectively significantly improves the performance gain and the considered RSMA system outperforms the conventional multiple schemes in terms rate and user-fairness. Also, the exploitation of spatial correlation among each RIS elements i.e., non-diagonal phase-matrices at each RIS achieve better performance that conventional diagonal phase-matrices setting. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremely high dimensions of estimated channels. To mitigate this issue, a low-complexity algorithm via the two-timescale transmission protocol is proposed in this paper, where the joint beamforming at BSs and RISs is facilitated via alternating optimization framework to maximize the average weighted sum-rate. Specifically, the passive beamformers at RISs are optimized through the statistical CSI, and the transmit beamformers at BSs are based on the instantaneous CSI of effective channels. In this manner, a closed-form expression for the achievable weighted sum-rate is derived, which enables the evaluation of the impact of key parameters on system performance. To gain more insights, a special case without line-of-sight (LoS) components is further investigated, where a power gain on the order of $\mathcal {O}(M)$ is achieved, with $M$ being the BS antennas number. Numerical results validate the tightness of our derived analytical expression and show the fast convergence of the proposed algorithm. Findings illustrate that the performance of the proposed algorithm with two-timescale CSI is comparable to that with instantaneous CSI in low or moderate SNR regime. The impact of key system parameters such as the number of RIS elements, CSI settings and Rician factor is also evaluated. Moreover, the remarkable advantages from the adoption of the cell-free paradigm and the deployment of RISs are demonstrated intuitively. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is a promising technology which can be integrated with non-orthogonal multiple access (NOMA) to improve the secrecy performance. In this paper, we propose two IRS-aided schemes to enhance the security of NOMA networks for the internal and external eavesdropping, respectively. First, to deal with an internal untrusted user, the secrecy rate maximization problem is formulated by jointly optimizing the active and passive beamforming, while meeting the quality of service (QoS) demand of the untrusted user, decoding order constraints, and unit modulus constraints of IRS elements. Furthermore, considering a worse scenario with both internal and external eavesdroppers, we minimize the transmit power of legitimate signals with both users’ QoS demands satisfied. In this way, the confidential information leakage will be mitigated and more transmit power can be allocated as artificial jamming to attenuate the eavesdropping. To tackle the non-convex optimization, the original problem in each scheme is first decomposed into two subproblems, which are approximated into convex ones via the semidefinite relaxation (SDR). Then, by means of alternating optimization, the suboptimal solutions to the original problems can be obtained. Simulation results demonstrate the superiority of the proposed schemes against the challenging internal and external eavesdropping by combining IRS and NOMA. [ABSTRACT FROM AUTHOR]

This paper studies the unmanned aerial vehicle (UAV) based multiple intelligent reflecting surface (IRS) network, where the hovering UAV acts as a base station, and the IRS enhances signal transmission to across obstacle between users and UAV. To achieve the maximum sum rate of proposed communication scenario, a non-convex problem considering IRS association results, hovering altitude of UAV, and the phase shift design of multi-IRS is formulated. From the IRS association problem, we can find that the IRS association results are coupled to the decoding order of non-orthogonal multiple access (NOMA). To tackle this, a mathematical interference expansion scheme is developed to decouple it and transform it to convex by binary relaxation method. The non-convexity of hovering altitude optimization problem is solved by logarithm operation, approximation, and auxiliary matrices. For the phase shift optimization problem of multi-IRS, we propose a gradient approximation based initial scheme and develop a univariate optimization based approach on the basis to achieve the users sum rate improvement in multi-IRS. In the end, we compare the proposed scheme with baseline scheme to present the superiority of this work under various network settings. The internal reasons for the variation of simulation results are also analyzed. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes. [ABSTRACT FROM AUTHOR]

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV’s energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices’ data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives. [ABSTRACT FROM AUTHOR]

Cloud radio access networks (C-RAN) enable a network platform for beyond the fifth generation of communication networks (B5G), which incorporates the advances in cloud computing technologies to modern radio access networks. Recently, rate splitting multiple access (RSMA), relying on multi-antenna rate splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been shown to manage the interference in multi-antenna communication networks efficiently. This paper considers applying RSMA in C-RAN. We address the practical challenge of a transmitter that only knows the statistical channel state information (CSI) of the users. To this end, the paper investigates the problem of stochastic coordinated beamforming (SCB) optimization to maximize the ergodic sum-rate (ESR) in the network. Furthermore, we propose a scalable and robust RS scheme where the number of the common streams to be decoded at each user scales linearly with the number of users, and the common stream selection only depends on the statistical CSI. The setup leads to a challenging stochastic and non-convex optimization problem. A sample average approximation (SAA) and weighted minimum mean square error (WMMSE) based algorithm is adopted to tackle the intractable stochastic non-convex optimization and guarantee convergence to a stationary point asymptotically. The numerical simulations demonstrate the efficiency of the proposed RS strategy and show a gain up to 27% in the achievable ESR compared with state-of-the-art schemes, namely treating interference as noise (TIN) and non-orthogonal multiple access (NOMA) schemes. [ABSTRACT FROM AUTHOR]

This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users’ rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust secrecy transmission scheme for intelligent reflecting surface (IRS) aided communication systems. Different from all the existing works where IRS has already been deployed at a fixed location, we take the location of IRS as a variable to maximize the secrecy rate (SR) under the outage probability constraint by jointly optimizing the location of IRS, transmit beamformer and IRS phase shifts with imperfect channel state information (CSI) of Eve, where we consider two cases: a) the location of Eve is known; b) only a suspicious area of Eve is available. We show a critical observation that CSI models are different before and after IRS deployment, thus the optimization problem could be decomposed and solved via a two-stage framework. For case a), in the first stage, universal upper bounds of outage probabilities only related to the location of IRS are derived which can be optimized via successive convex approximation (SCA) method. In the second stage, we develop an alternative optimization (AO) algorithm to optimize beamformer and phase shifts iteratively. For case b), we propose a Max-Min SR scheme based on two-stage framework, where the location of IRS is optimized based on the worst location of Eve. Simulation results indicate the importance of the location of IRS optimization. [ABSTRACT FROM AUTHOR]

In this paper, we consider Internet of Things (IoT) based mobile edge computing (MEC) system. IoT devices are controlled by access points (APs), which do not have access to any licensed spectrum. This puts limitations on IoT devices’ offloading capability and hence effectively reduces the number of computed bits. Under such scenario, we consider spectrum sharing, where IoT networks get access to licensed spectrum by helping a primary network (owns licensed spectrum) in cooperative relaying. We aim to maximize sum of the primary network’s communication rate and IoT networks’ total number of computed bits by jointly optimizing relay selection, licensed spectrum allocation, local computation, and offloading sequence selection. Moreover, we consider important constraints, e.g., the guaranteed rate for the primary network, IoT devices’ available energy, and available licensed spectrum resource blocks (RBs). We observe that the optimization problem is combinatoric in nature, which becomes more complicated when IoT devices’ scheduling order comes into the formulation. From our analysis, we devise an optimal and computationally efficient algorithm. In the result section, we compare with relevant benchmark systems and show the efficacy of our proposed model. Moreover, we also show how IoT and primary networks benefit from spectrum sharing. [ABSTRACT FROM AUTHOR]

Physical layer security is a useful tool to prevent illegal wiretapping to confidential information. In this paper, we consider a generalized model of conventional physical layer security, referred as hierarchical information accessibility (HIA). A main feature of the HIA model is that a network has a hierarchy in information access, wherein decoding feasibility is determined by each user’s priority. Under this HIA model, we formulate a sum secrecy rate maximization problem with regard to precoding vectors. This problem is challenging since multiple non-smooth functions are involved into the secrecy rate to fulfill the HIA conditions and also the problem is non-convex. To address the challenges, we approximate the minimum function by using the LogSumExp technique, thereafter obtain the first-order optimality condition. One key observation is that the derived condition is cast as a functional eigenvalue problem, where the eigenvalue is equivalent to the approximated objective function of the formulated problem. Accordingly, we show that finding a principal eigenvector is equivalent to finding a local optimal solution. To this end, we develop a novel method called generalized power iteration for HIA (GPI-HIA). Simulations demonstrate that the GPI-HIA significantly outperforms other baseline methods in terms of the secrecy rate. [ABSTRACT FROM AUTHOR]

Magnetic random-access memory (MRAM) is a promising memory technology due to its high density, non-volatility, and high endurance. However, achieving high memory fidelity incurs high write-energy costs, which should be reduced for large-scale deployment of MRAMs. In this paper, we formulate a biconvex optimization problem to optimize write fidelity given energy and latency constraints. The basic idea is to allocate non-uniform write pulses depending on the importance of each bit position. The fidelity measure we consider is mean squared error (MSE), for which we optimize write pulses via alternating convex search (ACS). We derive analytic solutions and propose an alternating water-filling algorithm by casting the MRAM’s write operation as communication over parallel channels. Hence, the proposed alternating water-filling algorithm is computationally more efficient than the original ACS while their solutions are identical. Since the formulated biconvex problem is non-convex, both the original ACS and the proposed algorithm do not guarantee global optimality. However, the MSEs obtained by the proposed algorithm are comparable to the MSEs by complicated global nonlinear programming solvers. Furthermore, we prove that our algorithm can reduce the MSE exponentially with the number of bits per word. For an 8-bit accessed word, the proposed algorithm reduces the MSE by a factor of 21. We also evaluate MNIST dataset classification supposing that the model parameters of deep neural networks are stored in MRAMs. The numerical results show that the optimized write pulses can achieve 40% write-energy reduction for the same classification accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the resource allocation of mobile edge computing (MEC) in cognitive capacity harvesting networks (CCHNs) when non-orthogonal multiple-access (NOMA) technique is adopted. Different from traditional studies for NOMA-MEC networks, we aim at minimizing the total cost of CCHN while satisfying the quality-of-service (QoS) of secondary users (SUs). We adopt the mechanism of time division multiple access (TDMA) when several NOMA groups use the same spectrum, and consider both the waiting delay and transmission delay during data offloading with the optimization of transmission order of NOMA groups. We formulate the considered problem as a mixed integer non-linear programming (MINLP). We show that the transmit power and the allocated computing resource for each SU can be derived when the transmission time and transmission order of the NOMA groups are given. Based on this, the considered problem can be decomposed into a transmission time and order optimization subproblem, a cellular resource block (CRB) selection subproblem and a cognitive radio (CR) router selection subproblem. To solve the transmission time and order optimization subproblem, we first simplify the delay constraint via theoretic analysis, and then propose a binary segmentation (B-Seg) algorithm and a transmission order adjustment (TOA) algorithm to find the optimal transmission time and transmission order of NOMA groups, respectively. To solve the CRB selection subproblem and the CR router selection subproblem, a bigger requirement first (BRF) algorithm and a game-based iteration (GBI) algorithm are respectively proposed. Simulation results show that the proposed algorithms can significantly improve the system performance. [ABSTRACT FROM AUTHOR]

Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices, where the quantum approximation optimization algorithm (QAOA) constitutes a promising candidate for demonstrating tangible quantum advantages based on NISQ devices. In this paper, we consider the maximum likelihood (ML) detection problem of binary symbols transmitted over a multiple-input and multiple-output (MIMO) channel, where finding the optimal solution is exponentially hard using classical computers. Here, we apply the QAOA for the ML detection by encoding the problem of interest into a level- $p$ QAOA circuit having $2p$ variational parameters, which can be optimized by classical optimizers. This level- $p$ QAOA circuit is constructed by applying the prepared Hamiltonian to our problem and the initial Hamiltonian alternately in $p$ consecutive rounds. More explicitly, we first encode the optimal solution of the ML detection problem into the ground state of a problem Hamiltonian. Using the quantum adiabatic evolution technique, we provide both analytical and numerical results for characterizing the evolution of the eigenvalues of the quantum system used for ML detection. Then, for level-1 QAOA circuits, we derive the analytical expressions of the expectation values of the QAOA and discuss the complexity of the QAOA based ML detector. Explicitly, we evaluate the computational complexity of the classical optimizer used and the storage requirement of simulating the QAOA. Finally, we evaluate the bit error rate (BER) of the QAOA based ML detector and compare it both to the classical ML detector and to the classical minimum mean squared error (MMSE) detector, demonstrating that the QAOA based ML detector is capable of approaching the performance of the classical ML detector. [ABSTRACT FROM AUTHOR]

This paper proposes an effective and novel multi-agent deep reinforcement learning (MADRL)-based method for solving the joint virtual network function (VNF) placement and routing (P&R), where multiple service requests with differentiated demands are delivered at the same time. The differentiated demands of the service requests are reflected by their delay- and cost-sensitive factors. We first construct a VNF P&R problem to jointly minimize a weighted sum of service delay and resource consumption cost, which is NP-complete. Then, the joint VNF P&R problem is decoupled into two iterative subtasks: placement subtask and routing subtask. Each subtask consists of multiple concurrent parallel sequential decision processes. By invoking the deep deterministic policy gradient method and multi-agent technique, an MADRL-P&R framework is designed to perform the two subtasks. The new joint reward and internal rewards mechanism is proposed to match the goals and constraints of the placement and routing subtasks. We also propose the parameter migration-based model-retraining method to deal with changing network topologies. Corroborated by experiments, the proposed MADRL-P&R framework is superior to its alternatives in terms of service cost and delay, and offers higher flexibility for personalized service demands. The parameter migration-based model-retraining method can efficiently accelerate convergence under moderate network topology changes. [ABSTRACT FROM AUTHOR]

Although 5G networks have enabled mobile users to get a better experience, task scheduling remains challenging for massive Internet of Things (IoT) devices in remote areas. This paper investigates the task scheduling problem for delay-aware IoT applications in civil aircraft-augmented space-air-ground integrated networks (CAA-SAGIN), where the normalized sky access platforms (SAPs) can collect and forward the terrestrial tasks. Specifically, we first propose an access control scheme for a non-preemptive priority queuing system and a transmission control scheme with cross-layer optimization. Secondly, considering the uncertain distribution of the transmission numbers and generated data, we formulate a robust two-stage stochastic optimization problem of delay minimization. With the proposed robust task scheduling with risk aversion (RTS-RA) algorithm, the original problem can be decomposed into two subproblems, which can be further transformed into tractable semi-definite program (SDP) problems respectively. Simulation results show that the cross-layer optimization scheme can achieve a good tradeoff between delay and throughput. Also, the RTS-RA algorithm outperforms the exiting offloading schemes in terms of end-to-end delay, transmitted data, and energy consumption with lower computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we study a quantized feedback scheme to maximize the goodput of a finite blocklength communication scenario over a quasi-static fading channel. It is assumed that the receiver has perfect channel state information (CSI) and sends back the CSI to the transmitter over a resolution-limited error-free feedback channel. With this partial CSI, the transmitter is supposed to select the optimum transmission rate, such that it maximizes the overall goodput of the communication system. This problem has been studied for the asymptotic blocklength regime, however, no solution has so far been presented for finite blocklength. Here, we study this problem in two cases: with and without constraint on reliability. We first formulate the optimization problems and analytically solve them. Iterative algorithms that successfully exploit the system parameters for both cases are presented. It is shown that although the achievable maximum goodput decreases with shorter blocklengths and higher reliability requirements, significant improvement can be achieved even with coarsely quantized feedback schemes. [ABSTRACT FROM AUTHOR]

Wireless powered multi-access edge computing (MEC) has emerged as a promising paradigm to enable high-performance computation of energy-constrained wireless devices (WDs) in internet of things (IoT) systems. However, to overcome the severe path loss of both energy transfer and data communications, wireless powered MEC suffers from high operating power consumption. To achieve sustainable and economic system operation, this paper focuses on developing energy-efficient online data processing strategy for wireless powered MEC systems under stochastic fading channels. In particular, we consider a hybrid access point (HAP) transmitting RF energy to and processing the sensing data offloaded from multiple WDs. Under an average power constraint of the HAP, we target at maximizing the long-term average data sensing rate of the WDs while maintaining task data queue stability. To this end, we formulate a multi-stage stochastic optimization problem to control the energy transfer and task data processing in sequential time slots. Without the knowledge of future channel fading, it is very challenging to determine the sequential control actions that are tightly coupled by the battery and data buffer dynamics. To solve the problem, we propose a Lyapunov optimization-based online algorithm named LEESE, which decomposes the multi-stage stochastic problem into per-slot deterministic optimization problems. We show that each per-slot problem can be equivalently transformed into a convex optimization problem. To facilitate online implementation in large-scale MEC systems, instead of solving the per-slot problem with off-the-shelf convex algorithms, we propose a block coordinate descent (BCD)-based method that produces a close-to-optimal solution in less than 0.04% of the computation delay. Simulation results demonstrate that the proposed LEESE algorithm can provide 18% higher data sensing rate than the representative benchmark methods considered, while incurring sub-millisecond computation delay suitable for real-time control under fading channel. [ABSTRACT FROM AUTHOR]

In this paper, we present the computation-efficient resource allocation (RA) schemes for millimeter-wave mobile edge computing (mmWave-MEC) system with the aid of reconfigurable intelligent surface (RIS), which is used to assist the uplink communication from the users to the base station (BS). By means of the theoretical analysis, the achievable rate and computation efficiency (CE) are derived. Then, the optimization problem for the CE maximization under the constraints of the minimum rate, maximum power consumption and local CPU frequency is formulated, where the joint design of the hybrid beamforming at the BS and the passive beamforming at the RIS as well as the local resource allocation of each user is carried out. An effective iterative algorithm based on the penalized inexact block coordinate descent (BCD) method is proposed to obtain the computation-efficient RA scheme. Next, a low-complexity suboptimal RA scheme based on the BCD method is proposed, and corresponding algorithm is presented. Simulation results show that the proposed schemes are effective, and high CE can be attained. Moreover, the second scheme can achieve the CE performance close to the first scheme but with lower complexity. Besides, it is effective to deploy the RIS scheme in mmWave-MEC system, which can strike a balance between the CE and energy consumption when compared to the conventional relay schemes. [ABSTRACT FROM AUTHOR]

Plane spiral orbital angular momentum (PSOAM) mode-groups (MGs) and multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) serve as two emerging techniques for achieving high spectral efficiency (SE) in the next-generation networks. In this paper, a PSOAM MGs based multi-user MIMO-NOMA system is studied, where the base station transmits data to users by utilizing the generated PSOAM beams. For such scenario, the interference between users in different PSOAM mode groups can be avoided, which leads to a significant performance enhancement. We aim to maximize the energy efficiency (EE) of the system subject to the constraints of the total transmission power and the minimum data rate. This designed optimization problem is non-convex owing to the interference among users, and hence is quite difficult to tackle directly. To solve this issue, we develop a dual layer resource allocation algorithm where the bisection method is exploited in the outer layer to obtain the optimal EE and a resource distributed iterative algorithm is exploited in the inner layer to optimize the transmit power. Besides, an alternative resource allocation algorithm with Deep Belief Networks (DBN) is proposed to cope with the requirement for low computational complexity. Simulation results verify the theoretical findings and demonstrate the proposed algorithms on the PSOAM MGs based MIMO-NOMA system can obtain a better performance comparing to the conventional MIMO-NOMA system in terms of EE. [ABSTRACT FROM AUTHOR]

Symbol-level precoding (SLP), which converts the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol-error-rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol precoder design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop efficient design algorithms for typical max-min fairness and power minimization problems. In particular, after dividing all users into several groups, the precoders for each group are separately designed on a symbol-by-symbol basis by only utilizing the symbol information of the users in that group, in which the intra-group MUI is exploited using the concept of constructive interference (CI) and the inter-group MUI is also effectively suppressed. In order to further reduce the computational complexity, we utilize the Lagrangian dual, Karush-Kuhn-Tucker (KKT) conditions and the majorization-minimization (MM) method to transform the resulting problems into more tractable forms, and develop efficient algorithms for obtaining closed-form solutions to them. Extensive simulation results illustrate that the proposed G-SLP strategy and design algorithms dramatically reduce the computational complexity without causing significant performance loss compared with the traditional SLP schemes. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has emerged as a promising technique to enhance wireless communication performance cost-effectively. The existing literature has mainly considered IRS being deployed near user terminals to improve their performance. However, this approach may incur a high cost if IRSs need to be densely deployed in the network to cater to random user locations. To avoid such high deployment cost, in this paper we consider a new IRS aided wireless network architecture, where IRSs are deployed in the vicinity of each base station (BS) to assist in its communications with distributed users regardless of their locations. Besides significantly enhancing IRSs’ signal coverage, this scheme helps reduce the IRS-associated channel estimation overhead as compared to conventional user-side IRSs, by exploiting the nearly static BS-IRS channels over short distance. For this scheme, we propose a new two-stage transmission protocol to achieve IRS channel estimation and reflection optimization for uplink data transmission efficiently. In addition, we propose effective methods for solving the user-IRS association problem based on long-term/statistical channel knowledge and the selected user-IRS-BS cascaded channel estimation problem. Finally, all IRSs’ passive reflections are jointly optimized with the BS’s multi-antenna receive combining to maximize the minimum achievable rate among all users for data transmission. Numerical results show that the proposed co-site-IRS empowered BS scheme can achieve significant performance gains over the conventional BS without co-site IRS and existing schemes for IRS channel estimation and reflection optimization, thus enabling an appealing low-cost and high-performance BS design for future wireless networks. [ABSTRACT FROM AUTHOR]

Online fountain codes (OFCs) have many advantages, such as low overhead, online feedback and optimal encoding, due to the feedback of the instantaneous decoding state. This paper analyzes the characteristics of underwater acoustic networks (UANs) as well as the issues of existing OFCs applied in UANs. Aiming at these issues, two optimization objectives of OFCs are put forward for UANs. In addition, a recursive OFC with limited feedback (ROFC-LF) is presented for UANs. The ROFC-LF reduces the consumption of bandwidth and energy caused by the transmission of useless coding packets. Through limited feedback, the problem of low channel utilization in UANs with half-duplex communication is solved. Furthermore, a data transmission mechanism based on the ROFC-LF for UANs is presented. The theoretical analysis and simulation results show that the proposed transmission mechanism based on the ROFC-LF scheme outperforms the existing OFC schemes in terms of overhead, computational complexity, coding efficiency and energy consumption. Consequently, the ROFC-LF is suitable for UANs with constrained resources. [ABSTRACT FROM AUTHOR]

To accommodate the stringent requirements of massive connectivity and ultra-high throughput, both reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) have been perceived as the key techniques for future communication networks. In this paper, a versatile framework is conceived to boost transmit power efficiency for RIS-enabled multi-group NOMA networks in the presence of coordinated multi-point (CoMP) reception and imperfect successive interference cancellation (SIC). Particularly, a group-level SIC (GSIC) method is proposed to eliminate the decoded group’s interference together with the well-designed transceivers to mitigate the aggregated interference, including the intra-group interference, the residual interference caused by imperfect SIC, and the interference of NOMA users decoded later. According to the novel framework, a power minimization problem is formulated by collaboratively optimizing the transmit power and the phase shifts. To render the problem tractable, an alternating scheme is developed to optimize the transmit power and the phase shifts iteratively. Specifically, the transmit powers for the users in the same group are devised by a parallel iteration algorithm, whilst the phase shifts are optimized by a sequential rotation method. In simulations, it is shown that the proposed scheme requires less transmit power than various benchmark methods under the constraint of each user’s quality of service. [ABSTRACT FROM AUTHOR]

Mobile services are becoming more diverse, making them have different demands on communications, computing, and caching (3C) resources in mobile systems. Unlike the traditional work that considers only one type of service, this paper designs a unified framework to characterize the different kinds of services, and jointly optimizes the 3C resources of the base station (BS) and mobile devices to provide differentiated quality of service (QoS) for diverse services. In the proposed framework, we model the task required by the mobile device to be generated at the BS, the mobile device, or both of them, which means the requested tasks are served through different paths, consuming different bandwidth, computing and caching resources. Since diverse services have different QoS, we formulate a multi-objective programming (MOP) to optimize the allocation of the 3C resources for minimizing the total delay while maximizing the number of executed tasks requested by the mobile devices. We transform the MOP problem as a multi-objective Markov decision process (MO-MDP) and design a multi-objective proximal policy optimization (MO-PPO) algorithm to solve the MO-MDP. The proposed MO-PPO first trains two sub-policies separately for the two objectives, and then combines them to search for Pareto dominating solutions. By alternately perform the separate training and the combination, we can finally obtain a set of Pareto optimal solutions and the corresponding Pareto front. Simulation results show that the proposed MO-PPO outperforms traditional methods in finding a higher-quality set of Pareto optimal solutions and can more appropriately allocate 3C resources to different types of services. [ABSTRACT FROM AUTHOR]

In this paper, we leverage reinforcement learning (RL) and cross-layer network coding (CLNC) for efficiently pre-fetching requested contents to the local caches and delivering these contents to requesting users in a downlink fog-radio access network (F-RAN) with device-to-device (D2D) communications. In the considered system, fog access points (F-APs) and cache-enabled D2D (CE-D2D) users are equipped with local caches that alleviate traffic burden at the fronthaul and facilitate rapid delivery of the users’ contents. To this end, the CLNC scheme optimizes the coding decisions, transmission rates, and power levels of both F-APs and CE-D2D users, and RL scheme optimizes caching strategy. A joint content placement and delivery problem is formulated as an optimization problem with a goal to maximize system sum-rate. The problem is an NP-hard problem. To efficiently solve it, we first develop an innovative decentralized CLNC coalition formation (CLNC-CF) switch algorithm to obtain a stable solution for the content delivery problem, where F-APs and CE-D2D users utilize CLNC resource allocation. By considering statistics of channel and users’ content request into account, we then develop a multi-agent RL algorithm for optimizing the content placement at both F-APs and CE-D2D users. Simulation results show that the proposed joint CLNC-CF-RL framework can effectively improve the sum-rate by up to 30%, 60%, and 150%, respectively, compared to: 1) an optimal uncoded algorithm, 2) a standard rate-aware-NC algorithm, and 3) a benchmark classical NC with network-layer optimization. [ABSTRACT FROM AUTHOR]

The problem of maximizing secrecy rate of multiple-input multiple-output multiple-eavesdropper (MIMOME) channels with arbitrary numbers of antennas at each node is studied in this paper. First, the optimization problem corresponding to the secrecy capacity of the MIMOME channel is converted to an equivalent optimization based on Givens rotations and eigenvalue decomposition of the covariance matrix. In this new formulation, precoder is a rotation matrix which results in a positive semi-definite (PSD) covariance matrix by construction. This removes the PSD matrix constraint and makes the problem easier to tackle. Next, a Broyden-Fletcher-Goldfarb-Shanno (BFGS)-based algorithm is developed to find the rotation and power allocation parameters. Further, the generalized singular value decomposition (GSVD)-based precoding is used to initialize this algorithm. The proposed rotation-BFGS method provides an efficient approach to find a near-optimal transmit strategy for the MIMOME channel and outperforms various state-of-the-art analytical and numerical methods. In particular, the rotation-BFGS precoding achieves higher secrecy rates than the celebrated GSVD precoding, with a reasonably higher computational complexity. Extensive numerical results elaborate on the effectiveness of the rotation-BFGS precoding. The new framework developed in this paper can be applied to a variety of similar problems in the context of multi-antenna channels with and without secrecy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the same performance as computationally complex dirty paper coding, where the quasi-degradation condition is satisfied, conditioned on the users’ channels fall in the quasi-degradation region. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method. [ABSTRACT FROM AUTHOR]

In this paper, we consider an intelligent reflecting surface (IRS)-assisted cognitive radio system and maximize the secondary user (SU) rate by jointly optimizing the transmit power of secondary transmitter (ST) and the IRS’s reflect beamforming, subject to the constraints of the minimum required signal-to-interference-plus-noise ratio at the primary receiver, the ST’s maximum transmit power, and the unit modulus of the IRS reflect beamforming vector. This joint optimization problem can be solved suboptimally by the non-convex optimization techniques, which however usually require complicated mathematical transformations and are computationally intensive. To address this challenge, we propose an algorithm based on the deep deterministic policy gradient (DDPG) method. To achieve a higher learning efficiency and a lower reward variance, we propose another algorithm based on the soft actor-critic (SAC) method. In these proposed algorithms, a reward impact adjustment approach is proposed to improve their learning efficiency and stability. Simulation results show that the two proposed algorithms can achieve comparable SU rate performance with much shorter running time, as compared to the existing non-convex optimization-based benchmark algorithm, and that the proposed SAC-based algorithm learns faster and achieves a higher average reward with lower variance, as compared to the proposed DDPG-based algorithm. [ABSTRACT FROM AUTHOR]

The integration of aerial platforms to provide ubiquitous coverage and connectivity for densely deployed terrestrial networks is expected to be a reality in the emerging sixth-generation networks. Energy-effificient and secure transmission designs are two important components for integrated terrestrial-aerial networks (ITAN). Inlight of the potential of reconfigurable intelligent surface (RIS) for significantly reducing the system power consumption and boosting information security, this paper proposes a multi-layer RIS-assisted secure ITAN architecture to defend against simultaneous jamming and eavesdropping attacks, and investigates energy-efficient hybrid beamforming for it. Specifically, with the availability of imperfect angular channel state information (CSI), we propose a block coordinate descent (BCD) framework for the joint optimization of the user’s received decoder, the terrestrial and aerial digital precoder, and the multi-layer RIS analog precoder to maximize the system energy efficiency (EE) performance. For the design of the received decoder, a heuristic beamforming scheme is proposed to convert the worst-case design problem into a min-max one and facilitate the developing a closed-form solution. For the design of the digital precoder, we propose an iterative sequential convex approximation approach via capitalizing the auxiliary variables and first-order Taylor series expansion. Finally, a monotonic vertex-update algorithm with a penalty convex-concave procedure (P-CCP) is proposed to obtain the analog precoder with satisfactory performance. Numerical results show the superiority and effectiveness of the proposed optimization framework and architecture over various benchmark schemes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) assisted emergency communication is an important technique for future B5G/6G scenario. The UAV is usually considered as a mobile relay to forward information from the macro base station (MBS) to the users in emergency area. In this paper, the MBS power allocation, the UAV service zone selection, and the user scheduling are jointly investigated to enhance the sum spectrum efficiency. We formulate the MBS power allocation and UAV service zone selection problem as an Markov Decision Process (MDP) in the delay ignored system (DIS) and propose a deep reinforcement learning (DRL) algorithm based on Q-learning and Convolutional Neural Networks (CNN). Then the proposed DRL-based scheme is extended in time delay system (TDS) to estimate the current optimal action with the outdated channel information. We also formulate the user scheduling as a 0-1 optimization problem and solve it by dividing into sub-problems. Simulation results demonstrate that the proposed DRL-based resource scheduling scheme can effectively improve the spectrum efficiency compared with the existing schemes. [ABSTRACT FROM AUTHOR]

Owing to the factors such as controllable mobility, ready-to-use technology, low cost, easy implementation, and so on, unmanned aerial vehicle (UAV) possesses tremendous potential to be one of the primary candidates for next-generation (6G) wireless networks. This paper presents a UAV-assisted multiuser communication network where a multiple antenna UAV base station (BS) serves multiple single antenna ground users (GUs). UAV-BS uses a laser source-based charging mechanism to fulfill its power requirement and applies simultaneous wireless information and power transfer (SWIPT) in the downlink in order to provide desired power to energy-constrained GUs. Also, a clustering-based hybrid multiple access technique is used that combines both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) signaling to transmit the information to all GUs, simultaneously. Due to the involved analytical complexity corresponding to the multiple antennas and users, we use a hybrid beamforming method for efficient communication. Next, we analyze the performance of the proposed framework in terms of user outage probabilities, their respective throughput, and average power harvested considering non-linear energy harvesting and derive expressions of these performance metrics. Moreover, we formulate an optimization problem where the throughput of one GU is maximized by optimally choosing the power allocation parameter while ensuring the desired target throughput at other GU in each cluster. We also illustrate how crucial is the optimal selection of the target rates to maximize the network performance. Simulation results are provided to validate the accuracy of derived expressions and to highlight the dominance of hybrid beamforming and hybrid NOMA compared to conventional methods on the performance of the considered network. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has been regarded as a promising and revolutionary technology for future wireless communication systems owing to its capability of tailoring signal propagation environment in an energy/spectrum/ hardware-efficient manner. However, most existing studies on IRS optimizations are based on a simple and ideal reflection model that is impractical in hardware implementation, which thus leads to severe performance loss in realistic wideband/multi-band systems. To deal with this problem, in this paper we first propose a more practical and more tractable IRS reflection model that describes the difference of reflection responses for signals at different frequencies. Then, we investigate the joint transmit beamforming and IRS reflection beamforming design for an IRS-assisted multi-cell multi-band system. Both power minimization and sum-rate maximization problems are solved by exploiting popular second-order cone programming (SOCP), Riemannian manifold, minimization-majorization (MM), weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results illustrate the significant performance improvement of our proposed joint transmit beamforming and reflection design algorithms based on the practical reflection model in terms of power saving and rate enhancement. [ABSTRACT FROM AUTHOR]

Pliable index coding (PICOD) has recently been regarded as a promising solution that exploits the coding advantage to improve communication efficiency of content-type systems (e.g., recommendation system), where clients are pliable and are interested in receiving any new message that they do not have. PICOD aims to find an effective coding strategy that satisfies the demands of all clients with the minimum number of transmissions. However, most of the previous works mainly provided theoretical understanding on PICOD in special instances based on greedy algorithms. In contrast, in this paper, we present a flexible sparse and low-rank matrix modeling approach to minimize the number of transmissions for the general PICOD problems. This is achieved by establishing generalized pliable alignment conditions to guarantee the requirements of all clients. As the resulting non-convex problem is highly intractable, we further develop an alternating pursuit framework to detect the rank of the matrix to be recovered by using the rank-increasing strategy. To address the feasibility-detection issues in the existing methods, we propose an alternating projection algorithm, which admits closed-form expressions and avoids excessive sparsity inducing. Moreover, we establish the global convergence of the alternating projection algorithm with random initial points. Simulation results demonstrate that the proposed alternating pursuit algorithm significantly reduces the number of transmissions compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we consider precoding and receiving matrices optimization for a two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system with a decision feedback equalizer (DFE) at the destination node in the presence of the direct source-destination link. By adopting the minimum mean-squared error (MMSE) criterion, we develop two new transceiver design algorithms for such a system. The first one employs an iterative procedure to design the source, relay, feed-forward, and feedback matrices. The second algorithm is a non-iterative suboptimal approach which decomposes the optimization problem into two tractable subproblems and obtains the source and relay precoding matrices by solving the two subproblems sequentially. Simulation results validate the better MSE and bit-error-rate (BER) performance of the proposed algorithms and show that the non-iterative suboptimal method has a negligible performance loss when the ratio of the source node transmission power to the relay node transmission power is small. In addition, the computational complexity analysis suggests that the second algorithm and one iteration of the first algorithm have the same order of complexity. As the first algorithm typically converges within a few iterations, both proposed algorithms exhibit a low complexity order. [ABSTRACT FROM AUTHOR]