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In concurrent spectrum sharing scenarios between radars and wireless communication systems, one strict approach to protect the radar from undesired interference is locating the base stations outside a guard (or exclusion) zone of the radar. As a result, users inside that zone experience bad communication coverage. This paper uses Reconfigurable Intelligent Surfaces (RISs) to improve the base station coverage of users in the radar exclusion zone. The paper shows, through analysis and simulation, that joint optimization of base station and RIS locations improves the probability of coverage in the exclusion zone over the shared spectrum without disturbing the radar operation. [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]

In this paper, we study the estimation of a rank-one spiked tensor in the presence of heavy tailed noise. Our results highlight some of the fundamental similarities and differences in the tradeoff between statistical and computational efficiencies under heavy tailed and Gaussian noise. In particular, we show that, for $p$ th order tensors, the tradeoff manifests in an identical fashion as the Gaussian case when the noise has finite $4(p-1)$ th moment. The difference in signal strength requirements, with or without computational constraints, for us to estimate the singular vectors at the optimal rate, interestingly, narrows for noise with heavier tails and vanishes when the noise only has finite fourth moment. Moreover, if the noise has less than fourth moment, tensor SVD, perhaps the most natural approach, is suboptimal even though it is computationally intractable. Our analysis exploits a close connection between estimating the rank-one spikes and the spectral norm of a random tensor with iid entries. In particular, we show that the order of the spectral norm of a random tensor can be precisely characterized by the moment of its entries, generalizing classical results for random matrices. In addition to the theoretical guarantees, we propose estimation procedures for the heavy tailed regime, which are easy to implement and efficient to run. Numerical experiments are presented to demonstrate their practical merits. [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]

This paper considers the downlink of an ultra-reliable low-latency communication (URLLC) system in which a base station (BS) serves multiple single-antenna users in the short (finite) blocklength (FBL) regime with the assistance of a reconfigurable intelligent surface (RIS). In the FBL regime, the users' achievable rates are complex functions of the beamforming vectors and of the RIS's programmable reflecting elements (PREs). We propose the joint design of the transmit beamformers and PREs to maximize the geometric mean (GM) of these rates (GM-rate) and show that this approach provides fair rate distribution and thus reliable links to all users. A novel computational algorithm is developed, which is based on closed forms to generate improved feasible points. Simulations show the merit of our solution. [ABSTRACT FROM AUTHOR]

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. Moreover, the expression of optimal phase shifts is derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS. [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]

The Spreading Projection Algorithm for Rapid K-space sampLING, or SPARKLING, is an optimization-driven method that has been recently introduced for accelerated 2D MRI using compressed sensing. It has then been extended to address 3D imaging using either stacks of 2D sampling patterns or a local 3D strategy that optimizes a single sampling trajectory at a time. 2D SPARKLING actually performs variable density sampling (VDS) along a prescribed target density while maximizing sampling efficiency and meeting the gradient-based hardware constraints. However, 3D SPARKLING has remained limited in terms of acceleration factors along the third dimension if one wants to preserve a peaky point spread function (PSF) and thus good image quality. In this paper, in order to achieve higher acceleration factors in 3D imaging while preserving image quality, we propose a new efficient algorithm that performs optimization on full 3D SPARKLING. The proposed implementation based on fast multipole methods (FMM) allows us to design sampling patterns with up to ${10}^{{7}}$ k-space samples, thus opening the door to 3D VDS. We compare multi-CPU and GPU implementations and demonstrate that the latter is optimal for 3D imaging in the high-resolution acquisition regime ($600\mu $ m isotropic). Finally, we show that this novel optimization for full 3D SPARKLING outperforms stacking strategies or 3D twisted projection imaging through retrospective and prospective studies on NIST phantom and in vivo brain scans at 3 Tesla taking the particular case of ${T}_{{2}}$ *-w imaging. Overall the proposed method allows for 2.5-3.75x shorter scan times compared to GRAPPA-4 parallel imaging acquisition at 3 Tesla without compromising image quality. [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]

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]

Caching popular contents at edge networks (such as small-cell base stations) has been proposed to deal with the ever-growing mobile traffic. At the meantime, recommendation system is able to shape user demands for further prompting caching gain. In this paper, we study a multi-antenna multi-cell edge network employing transmit beamforming with caching-aware recommendation and user association. We first establish a framework for the joint problem of beamforming, user association, content caching and recommendation to minimize the content transmission delay of mobile users, by specifying a set of necessary conditions for all four component functions of the network. The resulting optimization problem corresponds to a non-convex, multi-timescale, and mixed-integer programming problem, which is hard to handle. To deal with the difficulty in solving the joint optimization problem by the direct formulation, we equivalently decompose it into three sub-problems. Then, we develop a computationally-efficient iterative algorithm to obtain the sub-optimal solution, where the three subproblems are tackled iteratively. Simulation results are conducted to demonstrate that the proposed algorithm can obtain lower transmission delay than baseline schemes. [ABSTRACT FROM AUTHOR]

The design of fusion engines is a subject of great importance in a variety of fields. In this paper, we focus on the problem of linear fusion at the feature level for multiple signal matrices with noises, with the features being extremal eigenvectors. When given multiple similarity matrices, the objective is to find an estimate of the latent signal eigenspace. The concentration result for the inner product of features from different matrix samples is developed, utilizing the random matrix theory. Based on of the theoretical results, we proposed an efficient algorithm, EigFuse, to solve the constrained data-driven optimization problem with different level of noises. Our method is of high efficiency by comparing it with state-of-the-art baseline approaches with multiple noise levels. Comprehensive experiments on several synthetic as well as real-life networks demonstrate our method’s superior performance. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) assisted non-orthogonal multiple access (NOMA) transmission can effectively improve the energy/spectrum efficiency in wireless networks. This paper designs a low-complexity scheme to achieve the balanced tradeoff between the sum-rate and power consumption in a RIS-assisted NOMA system, which can be measured by energy efficiency. To solve the formulated problem effectively, the original non-convex problem is first decomposed into two subproblems, i.e., beamforming optimization and phase shift optimization. Alternating optimization is proposed to solve these two subproblems iteratively. In particular, successive convex approximation (SCA) is utilized to convert the non-convex constraints to convex ones. The provided simulation results demonstrate that the proposed scheme can achieve superior performance on energy efficiency compared to the random phase shifts and orthogonal multiple access (OMA) schemes. [ABSTRACT FROM AUTHOR]

As the number of antennas increases, the massive multiple-input multiple-output (MIMO) system can precisely point to user equipments (UEs) with narrow beams. Accurate and timely channel state information (CSI) feedback is crucial to keep UEs in service. Mobile UEs, however, may suffer from the narrow beam nature of the massive MIMO system since UEs can move out of the beam coverage. When intelligent reflecting surface (IRS) is applied to the massive MIMO system, the adjustment of the IRS cannot be frequent as the IRS is controlled remotely by the base station (BS). Limiting the number of CSI feedback and the number of both BS and IRS adjustments significantly reduces the overhead of transmission and computation to the system. In this paper, we consider the UEs’ mobility adaptation problem in an IRS assisted multiuser massive MIMO downlink system with infrequent CSI feedback. We propose a beam control method that adapts to UEs’ mobility. The problem is constructed as a sum rate problem where both the BS and IRS are taken into account to jointly optimize the beamforming matrices. Simulation results show that our proposed algorithm can converge quickly and provide satisfactory performance for mobile UEs. At the same time, our proposed algorithm reduces the frequency of updating the beamforming matrices effectively both at the BS and at the IRS. [ABSTRACT FROM AUTHOR]

The coexistence of multiple types of users in vehicular networks results in diversified service requirements and multiscale content differences, which poses new challenges to reliable content transmission. This paper proposes a hierarchical coded caching framework coupled with coded caching, vehicle-to-everything communications, and non-orthogonal multiple access techniques. We quantify the user request hit ratio under different communication modes and study the hit ratio maximization problem under the constraint of limited communication and caching resources. We propose a two-layer matching based optimization algorithm, including a user association layer in distributed content sharing and a user grouping layer in centralized content requesting. Simulation results verify that the proposed scheme can achieve a balance between hit ratio and transmission time, and meet the requirements of the content sharing scenario with multiscale content files and scalable user density. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is an emerging cost-efficient technology to enhance communication performance by implementing a large number of passive reflecting elements with tunable phases in wireless systems. In this paper, we propose a general framework for the IRS-aided MIMO system designs under both single-user and multi-user setups, in which the diverse performance metrics including weighted mutual information and weighted MSE, and the realistic multiple weighted power constraint are taken into consideration. Leveraging the alternating optimization approach, the optimal IRS phase shifts are obtained in semi-closed forms. Specifically, based on the matrix-monotonic optimization theory, it is found that optimizing IRS phase shifts is essentially equivalent to tuning the eigenvalues and the corresponding eigenvectors of the MSE matrix. Then the proposed general framework is extended to a multi-user system by introducing a majorization-minimization (MM)-based method for IRS phase shift optimization. Simulation results show that our proposed optimal design brings significant enhancement on the chosen performance metric compared to the traditional MIMO systems without the IRS, and also significantly outperforms various benchmark designs in both single-user and multi-user systems. [ABSTRACT FROM AUTHOR]

This paper considers the finite-order filter design problem for the source and multiple full-duplex (FD) relays in a cooperative communication system under frequency-selective fading channels. The goal is to optimize the source and relay filters such that the end-to-end signal-to-interference-plus-noise ratio (SINR) of minimum mean-squared error decision-feedback equalizer (MMSE-DFE) can be maximized. The resultant design problem is very difficult since we need to deal with self interference (SI), inter-relay interference (IRI), and inter-symbol interference (ISI) at the same time. Novel designs are then proposed to overcome the difficulty in this work. Transforming the signals into the frequency domain and using some optimization techniques, we first theoretically derive the power spectrum of the source filter and the spectrums of the relay filters. Then, the finite-order design is developed to approach the derived spectrums. Based on the weighted least-square (WLS) criterion, the Steiglitz-McBride method is exploited to obtain the filter coefficients in finite lengths. Numerical results demonstrated that complete removal of SI may not be a good strategy; preserving a certain amount of SI at each relay instead provides better SINR performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cost-efficient beamforming optimization algorithm for multi-user wireless communication systems associated with the intelligent reflecting surface (IRS). From the baseline successive refinement algorithm, which gives a sub-optimal solution for the power minimization problem under the signal-to-interference-plus-noise-ratio (SINR) constraint at each user, several optimization techniques are proposed to reduce the computation complexity while maintaining the algorithm-level performance. To reduce the number of required multiply-accumulate (MAC) operations, we first simplify the complicated matrix inversion by utilizing the channel hardening effect. We also present the two-phase refinement process for the group-level optimization of phase-shift elements, further relaxing the computation complexity as well as the processing latency. Applying the proposed optimization techniques, as a result, numerical results show that the fully-optimized algorithm can reduce the computational costs by up to 89.4% while showing less than 1 dB power loss, leading to the practical solution for the next-generation IRS-aided communication. [ABSTRACT FROM AUTHOR]

In the present paper a closed-form framework is derived for the analysis and optimization of the coverage probability (Pcov) and of the area spectral efficiency (ASE) in long-range (LoRa) networks. The proposed framework exploits stochastic geometry tools to associate the Pcov and the ASE to the end device (ED) transmit power and to the ED density. The analysis reveals the trends of the Pcov and of the ASE curves, with respect to both of the two parameters, while the robustness of the framework holds even at the asymptotic cases. Building upon the derived framework, the analysis demonstrates that no joint global optimum exists that jointly maximizes the Pcov over both parameters, suggesting that the optimization of the Pcov must be performed separately, for the two key network parameters considered. As opposed to that, the analysis demonstrates that a set of global optima exists that jointly maximize the ASE over both parameters, and these global maxima are subsequently derived in closed form. Thus, the derived framework fully characterizes the performance of LoRa networks, while defining in closed form the optimal points of operation that can be proven of significant value, for the transceiver and network design, of practical LoRa networks. [ABSTRACT FROM AUTHOR]

In Multiple-input multiple-output (MIMO) broadcast channels (BCs), the transmitter simultaneously broadcasts signals to multiple receivers at same frequency band, resulting in that the communication capacity is affected by both interference, channel fading and random noise. Although channel fading can be mitigated by transceivers, the signal-to-noise-ratio (SNR) of the practical communication system is still dynamically changing due to the randomly changing noise. Traditional MIMO transceiver optimization algorithms can not flexibly adapt to the dynamic changes of SNR, resulting in large performance degradation. In this paper, we comprehensively consider signal power and signal space dimensions of the received signal in MIMO BCs, and propose two transceiver optimization algorithms which can dynamically adapt to the variance of SNRs. In the proposed algorithms, SNR is adopted to be an adjustment factor to cope with its variance. When SNR is low, i.e, large noise, the algorithm parameters are automatically adjusted so that the signal power is preserved as much as possible to combat the loss of communication capacity caused by large random noise. Correspondingly, under the condition of high SNR environment, the algorithm parameters are adjusted automatically to effectively compress the inter-user-interference (IUI) and intra-user-inter-stream-interference (ISI) by optimizing signal space dimensions. Simulation results show that in different SNR environments, the proposed algorithms can automatically adjust the focus of optimization, so that the optimization of signal power and signal space dimensions can automatically adapt to different SNRs. Compared with traditional transceiver optimization algorithms, the proposed algorithms can improve the communication capacity within a large dynamic range of SNR. [ABSTRACT FROM AUTHOR]

The combination of time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), referred to as hybrid TDMA-NOMA system, is considered as a potential solution to meet the unprecedented requirements for future wireless networks. While recent resource allocation techniques aiming to individually maximize either spectral efficiency (SE) or energy efficiency (EE), this paper considers an SE-EE trade-off based technique for a hybrid TDMA-NOMA system. This design offers an additional degree of freedom in resource allocation. The proposed design is formulated as a non-convex multi-objective optimization (MOO) problem. The MOO framework is reformulated as a single-objective optimization (SOO) problem by combining the multi-objectives through a weighted-sum objective function. With this, each of the original objectives is assigned with a weight factor to reflect its importance in the design. Then, the sequential convex approximation (SCA) and a second-order cone (SOC) approach are jointly utilized to deal with the non-convexity issues of the SOO problem. Simulation results reveal that the proposed trade-off based design strikes a good balance between the objective functions, while meeting the instantaneous requirements of the system. [ABSTRACT FROM AUTHOR]

The explosion of mobile communication devices and diversification of emerging media content put forward higher requirements for mobile communication networks in various aspects such as overall throughput, fairness and user experience. Fortunately, non-orthogonal multiplexing (NOM) techniques such as layered-division-multiplexing (LDM) and the flexible compatibility of physical layer design in fifth generation mobile networks (5G) open up the possibility to tackle the challenges. In this paper, we propose to use LDM-based layered multicast to improve network performance, where the multicast content is delivered with differentiated quality in different LDM signal layers. For various performance optimization purposes such as maximum throughput, proportional fairness, minimum dissatisfaction index, and maximum service satisfaction index, a unified analysis framework is developed. Under this framework, we formulate a joint optimization problem of layer-user pairing and power allocation. In order to solve this problem, an optimization algorithm based on subproblem decomposition is proposed, which arranges each multicast subscriber of the multicast content to receive the most suitable LDM layer, and finds the optimal transmit power and data rate for each layer. Simulation results demonstrate the superiority of LDM-based layered multicast over existing multicast approaches while guaranteeing the coverage and the lower limit of user experience. [ABSTRACT FROM AUTHOR]

In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold. [ABSTRACT FROM AUTHOR]

This paper focuses on the non-convex joint optimization with a dynamic resource of multi-user for an intelligent reflecting surface-enhanced mmWave system, where all users have individual rates or quality of service requirements. Firstly, the objective function of the above non-linear problem is converted into a quadratic programming form under the quality of service constraints. Further, a multi-blocks alternating optimization framework with dynamic power allocation is proposed to obtain the maximum sum-rate, where the relaxed ADMM algorithm is adopted to tackle the optimal full-digital precoder and the corresponding passive reflecting matrix is obtained by the gradient-projection. The numerical results verify that beam optimization should be emphasized in high SNR, but joint dynamic resource allocation can further improve system performance even if the hardware dimensions reaches the limit. [ABSTRACT FROM AUTHOR]

In this paper, we consider a multi-user multiple-input multiple-output (MIMO) system aided by multiple intelligent reflecting surfaces (IRSs) that are deployed to increase the coverage and, possibly, the rank of the channel. We propose an optimization algorithm to configure the IRSs, which is aimed at maximizing the network sum-rate by exploiting only the statistical characterization of the locations of the mobile users. As a consequence, the proposed approach does not require the estimation of either instantaneous channel state information (CSI) or second-order channel statistics for IRS optimization, thus significantly relaxing (or even avoiding) the need of frequently reconfiguring the IRSs, which constitutes one of the most critical issues in IRS-assisted systems. Numerical results confirm the validity of the proposed approach. It is shown, in particular, that IRS-assisted wireless systems that are optimized based on statistical position information still provide large performance gains as compared to the baseline scenarios in which no IRSs are deployed. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is deemed as a promising solution to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we consider a wireless network where multiple base stations (BSs) serve their respective users with the aid of distributed IRSs in the downlink communication. Specifically, each IRS assists in the transmission from its associated BS to user via passive beamforming, while in the meantime, it also randomly scatters the signals from other co-channel BSs, thus resulting in additional signal as well as interference paths in the network. As such, a new IRS-user/BS association problem arises pertaining to optimally balance the passive beamforming gains from all IRSs among different BS-user communication links. To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power control, respectively. We also characterize the scaling behavior of user ASAINRs with the increasing number of IRS reflecting elements to investigate the different effects of IRS-reflected signal versus interference power. Moreover, to solve the two ASAINR balancing problems that are both non-convex optimization problems, we propose an optimal solution to the problem without BS power control and low-complexity suboptimal solutions to both problems by applying the branch-and-bound method and exploiting new properties of the IRS-user associations, respectively. Numerical results verify our performance analysis and also demonstrate significant performance gains of the proposed solutions over benchmark schemes. [ABSTRACT FROM AUTHOR]

This paper studies the performance and key structural properties of the optimum location-based relay selection policy for wireless networks consisting of homogeneous Poisson distributed relays. The distribution of the channel quality indicator at the optimum relay location is obtained. A threshold-based distributed selective feedback policy is proposed for the discovery of the optimum relay location with finite average feedback load. It is established that the total number of relays feeding back obeys a Poisson distribution and an analytical expression for the average feedback load is derived. The analytical expressions for the average rate and outage probability with and without selective feedback are obtained for general path-loss models. It is shown that the optimum location-based relay selection policy outperforms other common relay selection strategies notably. It is also shown that utilizing location information from five relays on average is enough to achieve almost the same performance with the infinite feedback load case. As generalizations, isotropic Poisson point processes and heterogeneous source-to-relay and relay-to-destination links are also studied. [ABSTRACT FROM AUTHOR]

Employing multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has emerged as a powerful transceiver strategy for multi-antenna networks. In this paper, we design RS precoders for an overloaded multicarrier multigroup multicast downlink system, and analyse the error performance. RS splits each group message into degraded and designated parts. The degraded parts are combined and encoded into a degraded stream, while the designated parts are encoded in designated streams. All streams are precoded and superimposed in a non-orthogonal fashion before being transmitted over the same time-frequency resource. We first derive the optimized RS-based precoder, where the design philosophy is to achieve a fair user group rate for the considered scenario by solving a joint max-min fairness and sum subcarrier rate optimization problem. Comparing with other precoding schemes including the state-of-the-art multicast transmission scheme, we show that the RS precoder outperforms its counterparts in terms of the fairness rate, with Gaussian signalling, i.e., idealistic assumptions. Then we integrate the optimized RS precoder into a practical transceiver design for link-level simulations (LLS), with realistic assumptions such as finite alphabet inputs and finite code block length. The performance metric becomes the coded bit error rate (BER). In the system under study, low-density parity-check (LDPC) encoding is applied at the transmitter, and iterative soft-input soft-output detection and decoding are employed at the successive interference cancellation based receiver, which completes the LLS processing chain and helps to generate the coded error performance results which validate the effectiveness of the proposed RS precoding scheme compared with benchmark schemes, in terms of the error performance. More importantly, we unveil the corresponding relations between the achievable rate in the idealistic case and coded BER in the realistic case, e.g., with finite alphabet input, for the RS precoded multicarrier multigroup multicast scenario. [ABSTRACT FROM AUTHOR]

In Multimedia Broadcast/Multicast Service(MBMS), Multicast/Broadcast Single Frequency Network (MBSFN) and Single-cell Point-to-multipoint Network (SC-PTM) are two essential ways to organize networks to provide multicast services. MBSFN shows its unique advantage of enhancing signals at the boundary of two cells, however, highly requiring synchronization of symbols. SC-PTM shows the advantage of the flexibility on the network deployment. The comparison and complementarity of these two modes are explored. In this paper, we analyze the reception performance of MBSFN and SC-PTM from multiple perspectives. We first compare the successful transmission probability (STP) of MBSFN with that of SC-PTM. Then, we consider optimal power control problems in MBSFN and SC-PTM. The corresponding power control optimization problems are with fraction and max-min forms and solved by the Dinkelbach algorithm. Furthermore, we propose a joint mode-selection and power-control method in the hybrid mode of MBSFN and SC-PTM, where each cell could select the multicast mode from MBSFN and SC-PTM. This is a mixed-integer nonlinear programming (MINLP) problem and solved by the concave-convex procedure (CCCP) and the Dinkelbach algorithm. To further improve the throughput, the appropriate successful reception proportion is selected under the opportunistic multicast. Finally, the numerical simulations show the performance of our algorithms and compare the throughput of two modes, which verify our analysis. [ABSTRACT FROM AUTHOR]

The energy detection technology is recommended in the licensed assisted access (LAA) scheme by 3GPP because of its simplicity and low cost. However, due to its inherent limitation, there may exist imperfect channel detection, which can lead to the decrease of the channel utilization efficiency and the deterioration of fairness. The imperfect detection can generally be represented by the detection probability and false alarm probability, which depend on detection time, signal to noise ration (SNR), sampling rate and energy threshold. However, among the parameters, only the energy threshold can be dominated by LAA small base stations (SBSs) in the LAA scheme. Therefore, the energy threshold should be dynamically adjusted in the changeable channel environment such that the detection accuracy can improved as high as possible. Consider the fact that the optimization theory cannot be used to optimize the energy threshold since the expressions of performance indexes about the energy threshold are extremely complex, a Q-learning based energy threshold optimization algorithm (QLET) is thus proposed in the paper, where LAA SBSs act as the agent, the energy threshold is defined as the agent action, the different combinations of fairness and throughput are defined as the agent states, and the fairness and the reward function are also redefined. In order to ensure the smooth implementation of the proposed QLET algorithm, the information exchange mechanism, where the sending-confirmation mechanism and 1- persistent CSMA are used, is also proposed. Based on the proposed QL framework, the agent can learn the optimal energy threshold by repeatedly interacting with the environment, which enables the coexistence system to obtain the best coexistence performance. A large number of simulation results show that the proposed QLET is superior to the traditional fixed energy threshold scheme (FET) in terms of the fairness, WiFi collision probability and transmission delay, and that QLET is almost the same as FET in term of throughput. [ABSTRACT FROM AUTHOR]

This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we jointly optimize beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem. Simulation results verify the effectiveness of the IRS, which can significantly improve the system EE as compared to conventional benchmark schemes and also unveil a trade-off between convergence and performance gain for the two proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper studies linear distributed estimation of an unknown random parameter vector in a bandwidth-constrained multisensor network. To meet the bandwidth limitations, each sensor converts its observation into a low-dimensional datum via a suitable linear transformation. Then, the fusion center estimates the parameter vector by linearly combining all the received low-dimensional data, aiming at minimizing the estimation mean square error. The main purpose of this paper is to jointly determine the compression dimension of each sensor (referred to as dimension assignment) and design the corresponding compression matrix when the total compression dimensions is limited. Such a joint design problem can be formulated as a rank-constrained optimization problem and it is shown to be NP-hard for the first time. In addition, successive quadratic upper-bound minimization (SQUM), SQUM-block coordinate descent (SQUM-BCD) and nuclear norm regularization (NNR) methods are developed to solve it approximately. Furthermore, we show that any accumulation point of the sequence generated by the SQUM method satisfies the Karush-Kuhn-Tucker conditions of the rank-constrained optimization problem, and the Phase II algorithm of the SQUM-BCD and NNR methods (both are two-phase algorithms and have the same Phase II algorithm) guarantees convergence at least to a stationary point. Numerical experiments illustrate the advantages of the proposed methods compared with the existing method. [ABSTRACT FROM AUTHOR]

Fog radio access network (Fog-RAN), which pushes caching and computing capabilities to the network edge, is capable of efficiently delivering content to users by using carefully designed caching placement and content replacement algorithms. In this paper, the transmission scheme design and coding parameter optimization will be considered for coded caching in Fog-RAN, where the reliability of content delivery, i.e., content outage probability, is used as the performance metric. The problem will be formulated as a complicated multi-objective probabilistic combinatorial optimization. A novel maximum $b$ -matching approach will then be proposed to obtain the Pareto optimal solution with fairness constraint. Based on the fast message passing approach, a distributed algorithm with a low memory usage of $O(M+N)$ is also proposed, where $M$ is the number of users and $N$ is the number of fog access points (Fog-APs). Although it is usually very difficult to derive the closed-form formulas for the optimal solution, the approximation formulas of the content outage probability will also be obtained as a function of coding parameters. The asymptotic optimal coding parameters can then be obtained by defining and deriving the outage exponent region and diversity-multiplexing region. Simulation results will illustrate the accuracy of the theoretical derivations, and verify the outage performance of the proposed approach. Therefore, this paper not only proposes a practical distributed Fog-AP selection algorithm for coded caching but also provides a systematic way to evaluate and optimize the performance of Fog-RANs. [ABSTRACT FROM AUTHOR]

The direct-to-home satellite digital video broadcasting has been fast developed in the past decades, and the continuous phase modulation (CPM) is an alternative modulation scheme with the advantages of constant envelope and high spectral efficiency for the band-limited channel and non-linear amplifiers of the satellite transponder. In this paper, CPM cascaded with irregular low density parity check (LDPC) code is studied. A practical system model with global interleaving is proposed for the optimization of degree distribution of the LDPC codes, and code word design is obtained with a modified progressive edge-growth algorithm. Simulation results combined with theoretical analysis clarify the effect of signal-to-noise ratios and two important CPM parameters (modulation index ${h}$ and correlation length ${L}$ ), providing guidance for system design. Simulation on bit error rate performance show the validity of the proposed algorithm. This paper systematically solves the optimal system design of cascade irregular LDPC-CPM and is of great significance for future satellite broadcasting. [ABSTRACT FROM AUTHOR]

Technically, the security performance of generalized spatial modulation (GenSM) networks can be enhanced by dynamically adjusting the precoder allocated to the legitimate signal as communication channel varies. For this purpose, our paper proposes a secure transmission strategy upon designing both digital and analog precoders for hybrid GenSM systems, where an eavesdropper is taken into account. The concept of the hybrid GenSM system has arose to improve the spatial multiplexing (SMX) gain for remedying the shortcoming of the limited number of radio frequency chains in traditional GenSM systems. However, this may lead to a great deal of security degradation since the SMX gain of the unintended receiver will be also improved. To this end, we develop a secrecy enhancement scheme by devising both analog and digital precoders for hybrid GenSM networks. Specifically, we derive an efficiently closed-form alternative to the original secrecy rate (SR) expression for reducing the excessive computational complexity of the joint optimization problem over the hybrid precoder. Then, by using this alternative as our cost function, an iterative algorithm is proposed. In particular, we elaborately conceive a pair of concave maximization problems in order to optimize the digital and analog precoders, respectively. Our proposed strategy not only utilizes semi-positive definite relaxing technique over the analog precoder but also invokes a lower bound of the alternative to further simplify the optimization over the vectored digital precoder. Subsequently, both the convergence and computational complexity of the proposed alternating iteration algorithm are analyzed. Compared to existing designs, our proposed strategy strikes a compelling role in balancing the SR performance and complexity. Finally, our simulation results confirm the efficiency of the proposed algorithm in terms of the SR performance achieved. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic-robust constructive interference (CI) precoding scheme for downlink multi-user MISO systems, assuming that channel state information (CSI) at the transmitter side (CSIT) is contaminated by Gaussian-distributed uncertainties. Our objective is to minimize the total transmit power under users’ quality-of-service constraints: formulating CI at each user with high probabilities for a given target signal-to-noise ratio (SINR). We first analyze the probability of CI under imperfect CSIT. A series of approximations are then developed, transforming the intractable stochastic CI constraints into determined convex constraints. The non-convex stochastic-robust CI power minimization (CIPM) problem is then converted into second-order cone programming. We show that we could create tightened or relaxed approximations by changing the parameters, enabling us to find upper-bounds and lower-bounds for the original stochastic CIPM problem. The best parameter values corresponding to the tightest upper and lower bounds are also discussed and obtained. Simulation results show that the proposed methods reasonably approximate the stochastic CIPM problem. Using the given parameter values, it can guarantee the required probability of CI for each user under acceptable channel uncertainties and outperform the existing robust CI precoding in terms of both transmit power and feasibility rate. The small gap between the upper and lower bounds also shows that the proposed method does not cause too much performance loss. [ABSTRACT FROM AUTHOR]

Terahertz (THz) band has attracted considerable interest recently due to its superior high frequency and large available bandwidth. THz could act a vital part in the sixth generation (6G) mobile communication networks. In this paper, we introduce the downlink non-orthogonal multiple access (NOMA) technology into THz band small cell networks, where the total performance is optimized considering the two key enabling technologies. In order to decrease the energy consumption triggered by increasing of wireless services, we pay great attention to energy efficiency (EE) optimization and resource allocation in the THz-NOMA downlink systems by solving the subchannel assignment and power optimization. We first exploit a channel model for the THz-NOMA downlink system by using the key features of THz-NOMA networks. Then we utilize Dinkelbach-style algorithm to solve the resource allocation problem and decompose it into two subproblems. A subchannel assignment algorithm and a power optimization based on alternative direction method of multipliers (ADMM) algorithm are developed to get the solution. Finally, to embody the strengths of THz-NOMA performance, we compare our proposed schemes against the conventional schemes. Simulation results yield substantially higher EE and further prove the availability of our proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, we focus on intelligent reflecting surface (IRS) assisted multi-antenna communications with transceiver hardware impairments encountered in practice. In particular, we aim to maximize the received signal-to-noise ratio (SNR) taking into account the impact of hardware impairments, where the source transmit beamforming and the IRS reflect beamforming are jointly designed under the proposed optimization framework. To circumvent the non-convexity of the formulated design problem, we first derive a closed-form optimal solution to the source transmit beamforming. Then, for the optimization of IRS reflect beamforming, we obtain an upper bound to the optimal objective value via solving a single convex problem. A low-complexity minorization-maximization (MM) algorithm was developed to approach the upper bound. Simulation results demonstrate that the proposed beamforming design is more robust to the hardware impairments than that of the conventional SNR maximized scheme. Moreover, compared to the scenario without deploying an IRS, the performance gain brought by incorporating the hardware impairments is more evident for the IRS-aided communications. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical framework to analyze the performance of an integrated unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) relaying system in which the IRS provides an additional degree of freedom combined with the flexible deployment of full-duplex UAV to enhance communication between ground nodes. Our framework considers three different transmission modes: (i) UAV-only mode, (ii) IRS-only mode, and (iii) integrated UAV-IRS mode to achieve spectral and energy-efficient relaying. For the proposed modes, we provide exact and approximate expressions for the end-to-end outage probability, ergodic capacity, and energy efficiency (EE) in closed-form. We use the derived expressions to optimize key system parameters such as the UAV altitude and the number of elements on the IRS considering different modes. We formulate the problems in the form of fractional programming (e.g. single ratio, sum of multiple ratios or maximization-minimization of ratios) and devise optimal algorithms using quadratic transformations. Furthermore, we derive an analytic criterion to optimally select different transmission modes to maximize ergodic capacity and EE for a given number of IRS elements. Numerical results validate the derived expressions. The solutions obtained from the proposed optimization algorithms are compared with those obtained through exhaustive search. Insights are drawn related to the different communication modes, optimal number of IRS elements, and optimal UAV height. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of cooperative networks based on non-orthogonal multiple access (NOMA) with multiple full-duplex decode-and-forward relays. Taking into account rate fairness between the users and assuming imperfect successive interference cancellation (SIC), the problem is formulated so that power allocation is performed adaptively by the multiple relays in order to maximize the minimum users’ achievable rate. We demonstrate that the proposed optimization problem is convex and a closed-form expression for the power allocation factor is derived. The attained results show that the proposed scheme achieves satisfactory results in terms of achievable rate, fairness, and outage probability when compared to other adaptive power allocation strategies. [ABSTRACT FROM AUTHOR]

Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency. In this paper, we focus on an IRS-assisted NOMA network and propose an energy-efficient algorithm to yield a good tradeoff between the sum-rate maximization and total power consumption minimization. We aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming at the BS and the reflecting beamforming at the IRS. Specifically, the transmit beamforming and the phases of the low-cost passive elements on the IRS are alternatively optimized until the convergence. Simulation results demonstrate that the proposed algorithm in IRS-NOMA can yield superior performance compared with the conventional OMA-IRS and NOMA with a random phase IRS. [ABSTRACT FROM AUTHOR]

This two-part paper develops novel methodologies for using fractional programming (FP) techniques to design and optimize communication systems. Part I of this paper proposes a new quadratic transform for FP and treats its application for continuous optimization problems. In this Part II of the paper, we study discrete problems, such as those involving user scheduling, which are considerably more difficult to solve. Unlike the continuous problems, discrete or mixed discrete-continuous problems normally cannot be recast as convex problems. In contrast to the common heuristic of relaxing the discrete variables, this work reformulates the original problem in an FP form amenable to distributed combinatorial optimization. The paper illustrates this methodology by tackling the important and challenging problem of uplink coordinated multicell user scheduling in wireless cellular systems. Uplink scheduling is more challenging than downlink scheduling, because uplink user scheduling decisions significantly affect the interference pattern in nearby cells. Furthermore, the discrete scheduling variable needs to be optimized jointly with continuous variables such as transmit power levels and beamformers. The main idea of the proposed FP approach is to decouple the interaction among the interfering links, thereby permitting a distributed and joint optimization of the discrete and continuous variables with provable convergence. The paper shows that the well-known weighted minimum mean-square-error (WMMSE) algorithm can also be derived from a particular use of FP; but our proposed FP-based method significantly outperforms WMMSE when discrete user scheduling variables are involved, both in term of run-time efficiency and optimizing results. [ABSTRACT FROM AUTHOR]

Fractional programming (FP) refers to a family of optimization problems that involve ratio term(s). This two-part paper explores the use of FP in the design and optimization of communication systems. Part I of this paper focuses on FP theory and on solving continuous problems. The main theoretical contribution is a novel quadratic transform technique for tackling the multiple-ratio concave–convex FP problem—in contrast to conventional FP techniques that mostly can only deal with the single-ratio or the max-min-ratio case. Multiple-ratio FP problems are important for the optimization of communication networks, because system-level design often involves multiple signal-to-interference-plus-noise ratio terms. This paper considers the applications of FP to solving continuous problems in communication system design, particularly for power control, beamforming, and energy efficiency maximization. These application cases illustrate that the proposed quadratic transform can greatly facilitate the optimization involving ratios by recasting the original nonconvex problem as a sequence of convex problems. This FP-based problem reformulation gives rise to an efficient iterative optimization algorithm with provable convergence to a stationary point. The paper further demonstrates close connections between the proposed FP approach and other well-known algorithms in the literature, such as the fixed-point iteration and the weighted minimum mean-square-error beamforming. The optimization of discrete problems is discussed in Part II of this paper. [ABSTRACT FROM AUTHOR]

This paper studies robust energy optimization for the cloud radio access network (C-RAN). The objective of this paper is to jointly minimize network power consumption through optimizing the base station (BS) mode, multi-user (MU)-BS association, and beamforming vectors given imperfect channel state information (CSI). To solve this non-trivial problem, we first transform the problem to a semi-definite programming (SDP) one using the S-lemma with the aid of the semi-definite relaxation technique, and then propose a SDP-based group sparse beamforming approach to solve it iteratively. Since the computational complexity of solving SDP problems is intractable, we propose to translate the uncertainty in the CSI to the uncertainty in its covariance matrix, and then recast the original problem as a mixed-integer second-order cone programming problem. We further propose a two-stage rank selection framework to determine the BS mode and MU-BS association separately and successively. Simulation results demonstrate the convergence of our proposed algorithms, and validate the effectiveness of the proposed algorithms in minimizing the network power consumption of the C-RAN. [ABSTRACT FROM PUBLISHER]

This paper presents a generic methodology to optimize constellations based on their geometrical shaping for bit-interleaved coded modulation (BICM) systems. While the method can be applicable to any wireless standard design it has been tailored to two delivery scenarios typical of broadcast systems: 1) robust multimedia delivery and 2) UHDTV quality bitrate services. The design process is based on maximizing the BICM channel capacity for a given power constraint. The major contribution of this paper is a low complexity optimization algorithm for the design of optimal constellation schemes. The proposal consists of a set of initial conditions for a particle swarm optimization algorithm, and afterward, a customized post processing procedure for further improving the constellation alphabet. According to the broadcast application cases, the sizes of the constellations proposed range from 16 to 4096 symbols. The BICM channel capacities and performance of the designed constellations are compared to conventional quadrature amplitude modulation constellations for different application scenarios. The results show a significant improvement in terms of system performance and BICM channel capacities under additive white Gaussian noise and Rayleigh independently and identically distributed channel conditions. [ABSTRACT FROM PUBLISHER]

It is known that superposition signaling in Gaussian interference networks is capable of improving the achievable rate region. However, the problem of maximizing the rate gain offered by superposition signaling is computationally prohibitive, even in the simplest case of two-user single-input single-output interference networks. This paper examines superposition signaling for the general multiple-input multiple-output broadcast Gaussian interference networks. The problem of maximizing either the sum rate or the minimal user’s rate under superposition signaling and dirty paper coding is solved by a computationally efficient path-following procedure, which requires only a convex quadratic program for each iteration but ensures convergence at least to a locally optimal solution. Numerical results demonstrate the substantial performance advantage of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we present systematic micromagnetic modeling investigation on recording process of segmented thin-film media with circularly polarized magnetic field at microwave frequencies. This paper provides insightful understanding about the segmented medium stack design in microwave-assisted magnetic recording (MAMR) by exploring the impact of signal-to-noise (SNR) ratio and recording track width. By utilizing segmentation of grains with exchange breaking layers, the ac magnetic field generated from spin torque oscillator can be best exploited. Via optimized medium stack design, MAMR is able to achieve both high SNR and areal density gain with the proposed notched-structure medium (top and bottom segments have the strongest crystalline anisotropy) compared with conventional graded medium (with gradually increasing anisotropy from top to bottom). By tuning crystalline anisotropy strength in top and bottom segment, we studied the MAMR behavior of SNR and track width under different ac frequencies. This provides a novel view for future segmented media design. [ABSTRACT FROM AUTHOR]

Precoder optimization with full channel state information for finite alphabet signals over multiple-input multiple-output random channels is investigated in this paper. The precoder is represented by a product of power allocation matrix and constellation-forming matrix. There was an optimal algorithm introduced in the literature to globally maximize the channel mutual information by iteratively optimizing these two matrices. However, the computational complexity of the optimal algorithm is painfully high, especially when it is used with the high-order modulation and the high-data stream number. In this paper, we propose a novel sub-optimal low-complexity precoding algorithm and compare it with the optimal one. The new algorithm proceeds in two steps. First, the constellation-forming matrix is fixed in order to maximize the minimum Euclidean distance between the received symbols, which ensures high channel mutual information. Then, given the constellation-forming matrix, an iterative algorithm searches for the power allocation matrix that maximizes the channel mutual information. Since optimizing only one matrix instead of two, the new algorithm not only achieves a lower computational complexity but also avoids the use of initial values, which must be carefully selected for each channel and signal-to-noise ratio for fast convergence. Another advantage of the new algorithm is that the resulting precoder has a fixed form of received constellation thanks to the fixed constellation-forming matrix. This allows us to optimize the symbol mapping on the received constellation. Simulation results show that the proposed low-complexity precoder achieves error-rate performance that is close to performance of the optimal one when the conventional mapping is used. In addition, the new precoder used with optimized mapping at received constellation shows significant error-rate performance improvement. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the two-way communication between two users assisted by a reconfigurable intelligent surface (RIS). The scheme that two users communicate simultaneously over Rayleigh fading channels is considered. The channels between the two users and RIS can either be reciprocal or non-reciprocal. For reciprocal channels, we determine the optimal phases at the RIS to maximize the signal-to-interference-plus-noise ratio (SINR). We then derive exact closed-form expressions for the outage probability and spectral efficiency for single-element RIS. By capitalizing the insights obtained from the single-element analysis, we introduce a gamma approximation to model the product of Rayleigh random variables which is useful for the evaluation of the performance metrics in multiple-element RIS. Asymptotic analysis shows that the outage decreases at $\left ({\log (\rho)/\rho }\right)^{L}$ rate where $L$ is the number of elements, whereas the spectral efficiency increases at $\log (\rho)$ rate at large average SINR $\rho $. For non-reciprocal channels, the minimum user SINR is targeted to be maximized. For single-element RIS, closed-form solution is derived whereas for multiple-element RIS the problem turns out to be non-convex. The latter one is solved through semidefinite programming relaxation and a proposed greedy-iterative method, which can achieve higher performance and lower computational complexity, respectively. [ABSTRACT FROM AUTHOR]