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Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, the design of a multiple-input multiple-output (MIMO) optical wireless communication (OWC) link based on vertical cavity surface emitting laser (VCSEL) arrays is systematically carried out with the aim to support data rates in excess of 1 Tb/s for the backhaul of sixth generation (6G) indoor wireless networks. The proposed design combines direct current optical orthogonal frequency division multiplexing (DCO-OFDM) and a spatial multiplexing MIMO architecture. For such an ultra-high-speed line-of-sight (LOS) OWC link with low divergence laser beams, maintaining alignment is of high importance. In this paper, two types of misalignment error between the transmitter and receiver are distinguished, namely, radial displacement error and orientation angle error, and they are thoroughly modeled in a unified analytical framework assuming Gaussian laser beams, resulting in a generalized misalignment model (GMM). The derived GMM is then extended to MIMO arrays and the performance of the MIMO-OFDM OWC system is analyzed in terms of the aggregate data rate. Novel insights are provided into the system performance based on computer simulations by studying various influential factors such as beam waist, array configuration and different misalignment errors, which can be used as guidelines for designing short range Tb/s MIMO OWC systems. [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]

Indoor optical wireless communication (IOWC) is a key technology complementing radio communication in 6G. Typically, the downlink of IOWC is implemented using visible light and uplink is implemented using infrared. Though the downlink of IOWC has been studied well, the uplink of multiple-access IOWC requires further research. In this paper, power allocation strategies in the uplink of IOWC with multiple non-cooperative users is studied. Optimizing power allocation in IOWC that maximize sum-rate with power constraints becomes a non-convex problem. First, this paper proposes an alternating maximization algorithm to compute near optimal power allocation policies for small number of users. Next, a phenomenon of invariance is observed when the number of users in the system is large. Under invariance, the optimal power allocation policy of a user becomes independent of other users, thereby facilitating fast and decentralized computation of power allocation strategies. From simulations, it was observed that the invariance can occur even with five to ten users. For the first time, a rigorous analysis of the invariance phenomenon is provided. An algorithm to compute the optimal power allocation at invariance is proposed; this algorithm is proven to achieve the global maxima in sum-rate without the channel knowledge of other users. [ABSTRACT FROM AUTHOR]

This paper studies a multiple-input–single-output (MISO) broadcast channel (BC) featuring simultaneous wireless information and power transfer, where a multiantenna access point (AP) delivers both information and energy via radio signals to multiple single-antenna receivers simultaneously, and each receiver implements either information decoding (ID) or energy harvesting (EH). In particular, pseudorandom sequences that are a priori known and therefore can be cancelled at each ID receiver are used as the energy signals, and the information-theoretically optimal dirty paper coding is employed for the information transmission. We characterize the capacity region for ID receivers by solving a sequence of weighted sum-rate (WSR) maximization (WSRMax) problems subject to a maximum sum-power constraint for the AP, and a set of minimum harvested power constraints for individual EH receivers. The problem corresponds to a new form of WSRMax problem in MISO-BC with combined maximum and minimum linear transmit covariance constraints (MaxLTCCs and MinLTCCs), which differs from the celebrated capacity region characterization problem for MISO-BC under a set of MaxLTCCs only and is challenging to solve. By extending the general BC–multiple-access-channel duality, which is only applicable to WSRMax problems with MaxLTCCs, and applying the ellipsoid method, we propose an efficient iterative algorithm to solve this problem globally optimally. Furthermore, we also propose two suboptimal algorithms with lower complexity by assuming that the information and energy signals are designed separately. Finally, numerical results are provided to validate our proposed algorithms. [ABSTRACT FROM PUBLISHER]

The prospects of using a Reconfigurable Intelligent Surface (RIS) to aid wireless communication systems have recently received much attention from academia and industry. Most papers make theoretical studies based on elementary models, while the prototyping of RIS-aided wireless communication and real-world field trials are scarce. In this paper, we describe a new RIS prototype consisting of 1100 controllable elements working at 5.8 GHz band. We propose an efficient algorithm for configuring the RIS over the air by exploiting the geometrical array properties and a practical receiver-RIS feedback link. In our indoor test, where the transmitter and receiver are separated by a 30 cm thick concrete wall, our RIS prototype provides a 26 dB power gain compared to the baseline case where the RIS is replaced by a copper plate. A 27 dB power gain was observed in the short-distance outdoor measurement. We also carried out long-distance measurements and successfully transmitted a 32 Mbps data stream over 500 m. A 1080p video was live-streamed and it only played smoothly when the RIS was utilized. The power consumption of the RIS is around 1 W. Our paper is vivid proof that the RIS is a very promising technology for future wireless communications. [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]

The intelligent omni-surface (IOS) is a dynamic metasurface that has recently been proposed to achieve full-dimensional communications by realizing the dual function of anomalous reflection and anomalous refraction. Existing research works provide only simplified models for the reflection and refraction responses of the IOS, which do not explicitly depend on the physical structure of the IOS and the angle of incidence of the electromagnetic (EM) waves. Therefore, the available reflection-refraction models are insufficient to characterize the performance of full-dimensional communications. In this paper, we propose a complete and detailed circuit-based reflection-refraction model for the IOS, which is formulated in terms of the physical structure and equivalent circuits of the IOS elements, as well as we validate it with the aid of full-wave EM simulations. Based on the proposed circuit-based model for the IOS, we analyze the asymmetry between the reflection and transmission coefficients. Moreover, the proposed circuit-based model is utilized for optimizing the hybrid beamforming of IOS-assisted networks and hence improving the system performance. To verify the circuit-based model, the theoretical findings, and to evaluate the performance of full-dimensional beamforming, we implement a prototype of IOS and deploy an IOS-assisted wireless communication testbed to experimentally measure the beam patterns and to quantify the achievable rate. The obtained experimental results validate the theoretical findings and the accuracy of the proposed circuit-based reflection-refraction model for IOSs. [ABSTRACT FROM AUTHOR]

In this paper, two new families of asynchronous channel-hopping (CH) sequences—Symmetric Maximum-Length CH (SML-CH) Sequences and Relaxed Difference Set (RDS) of Maximum-Length CH (RDSML-CH) Sequences—are constructed. For the first time, the maximum-length sequences (also called as m-sequences) are used to create 2-D CH matrices with a reduced number of “jump” columns. The two new constructions carry the desirable properties of maximum rendezvous diversity (MRD) and even channel use (ECU) and, more importantly, have the shortest periods in their categories of constructions. Another contribution is on the embedment of optical orthogonal codes (OOCs) to eliminate “stay” columns in the RDSML-CH matrices, thus resulting in the first and only family of the asynchronous CH sequences that can asymptotically achieve the theoretical period lower bound. To support the RDSML-CH construction, a new shortened RDS algorithm for creating the OOCs with smaller weight-to-length ratios than the existing ones is proposed. Numerical and simulation analyses show that the two new constructions have a good balance of short period, MRD, ECU, short time-to-rendezvous (TTR) mean, small TTR variance, and short maximum-TTR, thus more suitable for practical CR wireless networks than the existing constructions. [ABSTRACT FROM AUTHOR]

The differential chaos shift keying with code index modulation (CIM-DCSK) using Walsh codes can increase the data rate, but error rate performance degrades over practical multipath channel with high delay spread. To tackle this drawback, this paper proposes a parallel CIM-DCSK (PT-CIM-DCSK), where the first time slot is used to transmit the chaotic reference sequence, and the second slot is used to transmit the multiple sequences carrying data bits. In particular, multiple $q$ chaotic sequences are added for data transmission in parallel, and each sequence is modulated by CIM-DCSK with $m_{c}$ bits. By exploiting the quasi-orthogonal characteristics of permuted chaotic signals, the interference between $q$ sequences can be remarkably suppressed. In this way, given the data rate, PT-CIM-DCSK can achieve a larger length of Walsh segment than the CIM-DCSK, since its parallel transmission compensates the rate. In addition, the error performance of the proposed scheme over the multipath Rayleigh fading channel is theoretically analyzed. The theoretical and consistent simulation results demonstrate that the PT-CIM-DCSK can obtain significant performance gains over the conventional CIM-DCSK, and the gain can be up to 5 dB in multipath fading channel with high delay. This is because the larger Walsh segment of PT-CIM-DCSK provides the enhanced robustness against multipath effect. The performance superiority of the proposed scheme is further verified in practical ultra-wideband (UWB) wireless industrial scenarios, which suggests promising for low-cost and low-complexity wireless communication applications. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is a promising new technology that is able to create a favorable wireless signal propagation environment by collaboratively reconfiguring the passive reflecting elements yet with low hardware and energy cost. In IRS-aided wireless communication systems, channel modeling is a fundamental task for communication algorithm design and performance optimization, which however is also very challenging since in-depth domain knowledge and technical expertise in radio signal propagations are required, especially for modeling the high-dimensional cascaded base station (BS)-IRS and IRS-user channels (also referred to as the reflected channels). In this paper, we propose a model-driven generative adversarial network (GAN)-based channel modeling framework to autonomously learn the reflected channel distribution, without complex theoretical analysis or data processing. The designed GAN (also named as IRS-GAN) is trained to reach the Nash equilibrium of a minimax game between a generative model and a discriminative model. For the single-user case, we propose to incorporate the special structure of the reflected channels into the design of the generative model. While for the multiuser case, we extend the IRS-GAN and present a multiuser IRS-GAN (abbreviated as IRS-GAN-M), where the distributions of the reflected channels associated with different users are learned simultaneously with reduced number of network parameters (as compared to the naive scheme that assigns a dedicated IRS-GAN for each user). Moreover, theoretical analysis is presented to prove that the minimax game in the IRS-GAN-M framework has a global optimum if the generative and discriminative models are given with enough capacity. Simulation results are presented to validate the effectiveness of the proposed IRS-GAN framework. [ABSTRACT FROM AUTHOR]

Wireless communications are vulnerable to cyber attackers, which now have the flexibility to choose their type of attack. In this paper, combined with intelligent reflect surface (IRS), we jointly optimize base station beamforming and IRS reflected beamforming to counter smart attackers, thereby improving system security. Considering that attackers can flexibly choose their attack methods, such as jamming or eavesdropping, we make the base station intelligent by using reinforcement learning, which can predict the attack methods of attackers and choose whether to add artificial noise into the transmitted signals. At the same time, the interaction between the base station and the smart attackers are established as a non-cooperative game, the Nash equilibrium of the game is derived. Based on this, the base station anti-smart attackers strategy based on Deep Q-learning (DQN) is proposed, which can restrain the attack of the attacker to improve the security of the system. It can be verified from the simulation results that the proposed anti-smart attackers strategy can effectively enhance the secrecy rate of the wireless communication system, resist the attacker’s attack, and intelligently transmit artificial noise to improve system security. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (M-MIMO) aided positioning technologies have been recently recognized as promising solutions to fulfil the high accuracy requirement of indoor localization systems. In this paper, exploiting a distributed M-MIMO framework, we propose to employ a deep belief network (DBN) to analyze the received signal strengths (RSS) generated by a diffraction model, where the impact from interfering persons on the targeted user equipment (UE) is considered. Next, the preliminary DBN estimates are forwarded to a long-short term memory network (LSTMN), where the trajectory information of the targeted UE can be extracted based on much less historical trajectory information than existing solutions. Then, the three-dimension (3D) coordinates of the UE’s positions can be estimated with a back propagation neural network (BPNN) which combines the outputs of DBN and LSTMN. Finally, extensive simulation results are provided to demonstrate the effectiveness of the proposed BPNN scheme. [ABSTRACT FROM AUTHOR]

To realize high energy-efficient, high spectrum-efficient, and high-throughput data transmission, a multi-carrier differential chaos shift keying communication system with hybrid index modulation, referred to as HIM-MC-DCSK system, is proposed in this paper. In the HIM-MC-DCSK system, we intelligently integrate the carrier-number-index technique and carrier-index technique into the MC-DCSK modulation in order to significantly boost the transmission efficiency in wireless communication systems. Especially, we use a pair of orthogonal signals to represent the active and inactive subcarriers in the HIM-MC-DCSK system so as to exploit all the subcarriers to transmit information bits. In addition, the theoretical bit-error-rate (BER) expressions of the HIM-MC-DCSK system are derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. Also, the data rate, spectrum efficiency, energy efficiency, and complexity of the proposed system are carefully analyzed. Monte-Carlo simulations not only verify the accuracy of the theoretical analysis but also illustrate the superiority of the proposed system. [ABSTRACT FROM AUTHOR]

Due to their ability to create favorable line-of-sight (LoS) propagation environments, intelligent reflecting surfaces (IRSs) are regarded as promising enablers for future millimeter-wave (mm-wave) wireless communication. In this paper, we investigate channel estimation for IRS-assisted mm-wave multiple-input multiple-output (MIMO) wireless systems. By leveraging the sparsity of mm-wave channels in the angular domain, we formulate the channel estimation problem as an $\ell _{1}$ -norm regularized optimization problem with fixed-rank constraints. To tackle the non-convexity of the formulated problem, an efficient algorithm is proposed by capitalizing on alternating minimization and manifold optimization (MO), which yields a locally optimal solution. To further reduce the computational complexity of the estimation algorithm, we propose a compressive sensing- (CS-) based channel estimation approach. In particular, a three-stage estimation protocol is put forward where the subproblem in each stage can be solved via low-complexity CS methods. Furthermore, based on the acquired channel state information (CSI) of the cascaded channel, we design a passive beamforming algorithm for maximization of the spectral efficiency. Simulation results reveal that the proposed MO-based estimation (MO-EST) and beamforming algorithms significantly outperform two benchmark schemes while the CS-based estimation (CS-EST) algorithm strikes a balance between performance and complexity. [ABSTRACT FROM AUTHOR]

Caching at the wireless edge nodes is a promising way to boost the spatial and spectral efficiency, for the sake of alleviating networks from content-related traffic. Coded caching originally introduced by Maddah-Ali and Niesen significantly speeds up communication efficiency by transmitting multicast messages simultaneously useful to multiple users. Most prior works on coded caching are based on the assumption that each user may request all content in the library. However, in many applications the users are interested only in a limited set of content that depends on their location. For example, assisted self-driving vehicles may access super High-Definition maps of the area through which they are travelling. Motivated by these considerations, this paper formulates the coded caching problem for location-based content with edge cache nodes. The considered problem includes a content server with access to ${\mathsf N}$ location-based files (e.g., High-Definition maps), ${\mathsf K}$ edge cache nodes located at different regions, and ${\mathsf K}$ users (i.e., vehicles) each of which is in the serving region of one cache node and can retrieve the cached content of this cache node with negligible cost. Depending on the location, each user only requests a file from a location-dependent subset of the library. The objective is to minimize the worst-case load (i.e., the worst-case number of broadcasted bits from the content server among all possible demands). For this novel coded caching problem, we propose a highly non-trivial converse bound under uncoded cache placement (i.e., each cache node directly copies some library bits in its cache), which shows that a simple achievable scheme is optimal under uncoded cache placement. In addition, this achievable scheme is also proved to be generally order optimal within a factor of 3. Finally, we extend the coded caching problem for location-based content to the multiaccess coded caching topology originally proposed by Hachem et al., where each user is connected to ${\mathsf L}$ nearest cache nodes. When ${\mathsf L}\geq 2$ , we characterize the exact optimality on the worst-case load. [ABSTRACT FROM AUTHOR]

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters’ estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters. [ABSTRACT FROM AUTHOR]

In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for power-constrained Internet-of-Things (IoT) smart devices, where IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI). We formulate a hybrid access point (HAP) transmit energy minimization problem by jointly optimizing time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient under the imperfect CSI and non-linear energy harvesting model. On account of the high coupling of optimization variables, the formulated problem is a non-convex optimization problem that is difficult to solve directly. To address the above-mentioned challenging problem, alternating optimization (AO) technique is applied to decouple the optimization variables to solve the problem. Specifically, through AO, time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient are divided into three sub-problems to be solved alternately. The difference-of-convex (DC) programming is used to solve the non-convex rank-one constraint in solving IRS energy reflection coefficient and information reflection coefficient. Numerical simulations verify the superiority of the proposed optimization algorithm in decreasing HAP transmit energy compared with other benchmark schemes. [ABSTRACT FROM AUTHOR]

This paper investigates an intelligent reflecting surface (IRS) aided secure wireless powered communication network (WPCN), where a transmitter first harvests energy from a power station (PS), and then uses the collected energy to transmit information to multiple internet of things (IoT) devices in the form of multicast in the presence of multiple eavesdroppers. An IRS is deployed to enhance the efficiency of wireless energy transfer (WET) and secure wireless information transfer (WIT). Considering that the PS and transmitter belong to different service providers, we model this energy interaction through a Stackelberg game and propose an IRS-aided energy trading and secure communication (IRS-ETSC) scheme, in which the transmitter needs to pay an energy price as an incentive for the PS energy service. Specifically, the transmitter as the leader can control the energy price, WET time, two-stage phase shifts, and beamforming vector, while the PS as the follower can adjust the transmit power. To solve the non-convex leader game problem, we propose a two-step approach to decompose the original problem into two subproblems. The first subproblem can be solved independently by an efficient alternating optimization (AO) based algorithm, in which the closed-form optimal beamforming vector and energy phase shifts are alternately optimized. Then, the second subproblem is relaxed by semidefinite relaxation (SDR) and solved by an iterative algorithm based on block coordinate descent (BCD), where the optimal energy price, optimal WET time, and suboptimal information phase shifts can be obtained by golden section method and successive convex approximation (SCA) respectively. Both subproblems can converge to a stationary point. Numerical results show that compared with the traditional non-IRS scheme, the proposed IRS-ETSC scheme achieves utility improvement for both the PS and transmitter. [ABSTRACT FROM AUTHOR]

This paper investigates power allocation for minimizing the average bit error probability (BEP) in hybrid communication systems, more specifically hybrid power line/wireless systems (HPWSs). In this regard, a brief discussion of HPWSs is presented in order to point out the complementary characteristics of power line and wireless media. By considering orthogonal frequency-division multiplexing and maximal-ratio combining, optimization problems associated with the minimization of the average BEP in hybrid communication systems are formulated under two distinct transmission power constraints, called sum power and sum power-channel constraints. For both constraints, it is demonstrated that at most one medium should be used for data transmission over subchannels with the same index in order to minimize the average BEP in hybrid communication systems. Based on this finding, power allocation algorithms are derived for this purpose in HPWSs. Numerical analyses are then used to validate the proposed algorithms by comparing them with interior-point-based power allocation algorithms. In addition, performance comparisons show the advantage of the proposals over alternative algorithms from the literature, including previous algorithms proposed for maximizing the achievable data rate. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) consisting of a large number of programmable near-passive units has been a hot topic in wireless communications due to its capability in providing smart radio environments to enhance the communication performance. However, the existing research are mainly based on simplistic channel models, which will, in principle, lead to inaccurate analysis of the system performance. In this paper, we propose a general three-dimensional (3D) wideband non-stationary end-to-end channel model for RIS assisted multiple-input multiple-output (MIMO) communications, which takes into account the physical properties of RIS, such as unit numbers, unit sizes, array orientations and array configurations. By modeling the RIS by a virtual cluster, we describe the end-to-end channel by a superposition of virtual line-of-sight (V-LoS), single-bounced non-LoS (SB-NLoS), and double-bounced NLoS (DB-NLoS) components. We also derive an equivalent cascaded channel model and show the equivalence between end-to-end and cascaded modeling of RIS channels. Then, a sub-optimal solution with low complexity is used to derive the RIS reflection phases. The impact of physical properties of RIS, such as unit numbers, unit sizes, array orientations, array configurations and array relative locations, on channel statistical characteristics has been investigated and analyzed, the results demonstrate that the proposed model is helpful for characterizing the RIS-assisted communication channels. [ABSTRACT FROM AUTHOR]

The use of massive multiple-input multiple-output (MIMO) technology has enabled increased efficiency and capacity of wireless communication systems. When the downlink channel to user terminals (UTs) is known at the base station (BS), the BS can precode transmission to simplify detection at the UTs. In frequency division duplexed (FDD) systems, obtaining CSI at the transmitter to fully exploit the advantages of massive MIMO is complicated, since the number of channel coefficients to be trained and fed back from the UT is very large. This is exacerbated in the case where the channel is time varying, where frequent retraining and feedback is required. However, if the channel coefficients are spatially correlated, the amount of feedback required can be reduced significantly. In this paper, we consider the case where the channel coefficients from the BS to the UTs are spatially correlated, and show that efficient allocation of training power based on eigenvalues of the channel correlation matrix significantly boosts achievable rates. Further, we show that using Kalman filters to track the evolving channel coefficients reduces the need to retrain channels, and the reduced training requirement translates to higher data rates. Simulations confirm that optimal training and tracking channel modes enhances rates significantly. [ABSTRACT FROM AUTHOR]

Many applications of wireless sensor networks (WSNs) require deploying sensors in hostile environments, where an adversary may eavesdrop communications. To secure communications in WSNs, the $q$ -composite key predistribution scheme has been proposed in the literature. In this paper, we investigate secure $k$ -connectivity in WSNs operating under the $q$ -composite scheme, in consideration of the unreliability of wireless links. Secure $k$ -connectivity ensures that any two sensors can find a path in between for secure communication, even when $k-1$ sensors fail. We present conditions on how to set the network parameters such that the network has secure $k$ -connectivity asymptotically almost surely. The result is given in the form of a sharp zero–one law. [ABSTRACT FROM AUTHOR]

Caching is a promising technique for 5G networks to reduce the backhual traffic and increase the overall network efficiency. In this paper, we study the caching placement policy with consideration of cooperative transmission for a two-hop relay-enabled device-to-device (D2D) network. In the caching placement phase, the probabilistic caching placement policy is considered, and in the content transmission phase, the hybrid automatic repeat request (HARQ) scheme with soft information combining [i.e., energy accumulation (EA) and mutual-information accumulation (MIA)] is utilized to improve the content retrieval experience. Cache-aided successful transmission probability (CSTP) is adopted as the main performance metric in this paper. By using tools from stochastic geometry, analytical expressions for the CSTP under different transmission schemes are derived. In moderate or high SIR regime, the optimal caching placement policy is identified based on the analytical expression and the CSTP performance is maximized accordingly. Evaluation results suggest that the MIA-based caching strategy performs better as opposed to existing strategies in most cases, but this outperformance vanishes when the transmission environment becomes severe or when users’ requests become concentrated. [ABSTRACT FROM AUTHOR]

The future connectivity landscape, and notably, the 5G wireless systems will feature Ultra-Reliable Low Latency Communication (URLLC). The coupling of high reliability and low latency requirements in URLLC use cases makes the wireless access design very challenging, in terms of both the protocol design and of the associated transmission techniques. This paper aims to provide a broad perspective on the fundamental tradeoffs in URLLC, as well as the principles used in building access protocols. Two specific technologies are considered in the context of URLLC: massive MIMO and multi-connectivity, also termed interface diversity. This paper also touches on the importance of the proper statistical methodology for designing and assessing extremely high-reliability levels. [ABSTRACT FROM AUTHOR]

To expedite research progress on terahertz (THz) communications, we analyze the outage performance of THz communication systems by a compound channel model in this paper. Different from existing models, the compound channel model incorporates the effects of spreading loss, molecular absorption loss, shadowing, and multi-path fading via a composite distribution. By using this model, we maintain an equilibrium of the outage performance analysis between mathematical tractability and the fidelity of realistic THz channels. Specifically, by utilizing the compound channel model, outage performance analysis can get rid of sophisticated case-specific channel modeling relying on field measurement and the ray-tracing assessment. To facilitate the application of the proposed channel model, we also design a maximum likelihood estimation (MLE) based channel parameter estimation approach for the compound channel model. The analytical results of outage performance by using the compound channel model are given in closed form and verified by numerical results. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) are envisioned to be a disruptive wireless communication technique that is capable of reconfiguring the wireless propagation environment. In this paper, we study a free-space RIS-assisted multiple-input single-output (MISO) communication system in far-field operation. To maximize the received power from the physical and electromagnetic nature point of view, a comprehensive optimization, including beamforming of the transmitter, phase shifts of the RIS, orientation and position of the RIS is formulated and addressed. After exploiting the property of line-of-sight (LoS) links, we derive closed-form solutions of beamforming and phase shifts. For the non-trivial RIS position optimization problem in arbitrary three-dimensional space, a dimensional-reducing theory is proved. The simulation results show that the proposed closed-form beamforming and phase shifts approach the upper bound of the received power. The robustness of our proposed solutions in terms of the perturbation is also verified. Moreover, the RIS significantly enhances the performance of the mmWave/THz communication system. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a secure mobile edge computing (MEC) network in the presence of multiple eavesdroppers, where multiple users can offload parts of their tasks to the computational access point (CAP). The multiple eavesdroppers may overhear the confidential task offloading, which leads to information leakage. In order to address this issue, we present the minimization problem of the secrecy outage probability (SOP), by jointly taking into account the constraints from the latency and energy consumption. With the aim to improve the system secrecy performance, we then introduce three user selection criteria to choose the best user among multiple ones. Specifically, criterion I maximizes the locally computational capacity, while criterion II and III maximize the secrecy capacity and data rate of main links, respectively. For these criteria, we further analyze the system secrecy performance by deriving analytical and asymptotic expressions for the SOP, from which we can conclude important insights for the system design. Finally, simulation and analytical results are provided to verify the proposed analysis. The results show that the three criteria can efficiently safeguard the MEC networks, compared to the traditional local computing and fully offloading, especially with a large value of user number. [ABSTRACT FROM AUTHOR]

In this paper, the concept of opportunistic bits (OBs) is developed in short-packet communications and investigated from a finite blocklength perspective. In the OB-based transmission, the data unit of a packet is divided into two parts: OBs and conventional bits (CBs). The OBs are not physically transmitted but used to indicate the index of the time slot (TS) when the packet containing CBs is transmitted. The loading of a bulk of OB-based packets into multiple TSs can be modelled as a Repeated Balls-into-Bins process with a multi-queue storage. If the bulk is not large enough, certain combination(s) of OBs will not appear, which leaves certain TS(s) empty and hence reduces the TS load efficiency. To evaluate the OB-based transmission performance, we formulate its maximal payload rate and TS load efficiency. With the aid of these two formulations, the energy gain, the goodput, and the latency of OB-based short-packet communications are derived and obtained in analytical forms. For achieving further insights, illustrative numerical results on the resource utilisation efficiency and the performance not only substantiate the advantages of the OB-based transmission over the conventional but also provide useful tools and specifications for its design in massive short-packet communications. [ABSTRACT FROM AUTHOR]

Covert wireless communications aim to hide the existence of transmission behavior from watchful adversaries to enhance security. In this paper, we propose a spectrum mask based covert communication strategy in Internet of Things (IoT) networks, where the overt channels are leveraged to enhance the covertness. Specifically, the legitimate IoT transmitter superimposes its own message on the overt channel to avoid being detected by the warden. We assume that proper Gaussian signaling (PGS) is adopted at the overt channel, and improper Gaussian signaling (IGS) is adopted at the legitimate transmitter to improve the covert transmission performance. To maximize the covert rate of the legitimate IoT system, a joint transmit power and IGS factor optimization problem is formulated under the constraints of covertness requirement. The metric of minimum error detection probability, that represents the worst-case for the legitimate transmitter, is utilized to measure the covertness. By exploiting the piece-wise monotonic properties of the objective function and the constraints, we derive the optimal transmit power and IGS factor pairs in both the IGS and PGS schemes. Finally, extensive numerical results are presented to demonstrate that the IGS scheme can improve the covert rate compared to the PGS scheme under a given covertness constraint. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless communication systems, enhances the spectral and energy efficiency by intelligently adjusting the propagation conditions between base stations (BSs) and mobile equipments (MEs). An RIS consists of many low-cost passive reflecting elements that are optimized to improve the quality of the received signal. In this paper, we study the problem of power control at the BS and RIS optimization for application to physical-layer broadcasting. Our goal is to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BS and the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposed optimization methods, we derive lower bounds to quantify the average transmit power at the BS as a function of the number of MEs, the number of RIS elements, and the number of antennas at the BS. The simulation results demonstrate that the average transmit power at the BS is close to the lower bound in an RIS-aided system, and is significantly lower than the average transmit power in conventional schemes without an RIS. [ABSTRACT FROM AUTHOR]

This paper considers the multiaccess coded caching systems formulated by Hachem et al., including a central server containing $N$ files connected to $K$ cache-less users through an error-free shared link, and $K$ cache-nodes, each equipped with a cache memory size of $M$ files. Each user has access to $L$ neighbouring cache-nodes with a cyclic wrap-around topology. The coded caching scheme proposed by Hachem et al. suffers from the case that $L$ does not divide $K$ , where the needed number of transmissions (a.k.a. load) is at most four times the load expression for the case where $L$ divides $K$. Our main contribution is to propose a novel transformation approach to smartly extend the schemes satisfying some conditions for the well known shared-link caching systems to the multiaccess caching systems. Then we can get many coded caching schemes with different subpacketizations for multiaccess coded caching system. These resulting schemes have the maximum local caching gain (i.e., the cached contents stored at any $L$ neighbouring cache-nodes are different such that the number of retrieval packets by each user from the connected cache-nodes is maximal) and the same coded caching gain as the original schemes. Applying the transformation approach to the well-known shared-link coded caching scheme proposed by Maddah-Ali and Niesen, we obtain a new multiaccess coded caching scheme that achieves the same load as the scheme of Hachem et al. but for any system parameters. Under the constraint of the cache placement used in this new multiaccess coded caching scheme, our delivery strategy is approximately optimal when $K$ is sufficiently large. Finally, we also show that the transmission load of the proposed scheme can be further reduced by compressing the multicast message. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has recently received extensive research interest due to its capability to intelligently change the wireless propagation environment. For RIS-aided wireless communications in frequency division duplex (FDD) mode, channel feedback at the user equipment (UE) is essential for the base station (BS) to acquire the downlink channel state information (CSI). In this paper, a dimension reduced channel feedback scheme is proposed to reduce the channel feedback overhead by exploiting the single-structured sparsity of BS-RIS-UE cascaded channel. Since different UEs share the same sparse BS-RIS channel but have their respective RIS-UE channels, there are only limited non-zero column vectors in the BS-RIS-UE cascaded channel matrix, and different UEs share the same indexes of the non-zero columns. Thus, the downlink CSI can be decomposed into user-independent channel information (i.e., the indexes of non-zero columns) and user-specific channel information (i.e., the non-zero column vectors), where the former for all UEs can be fed back by only one UE, while the latter can be fed back with a fairly low overhead by different UEs, respectively. Simulation results show that, compared with the conventional method, the proposed scheme can reduce channel feedback overhead by more than 80% for RIS-aided wireless communications. [ABSTRACT FROM AUTHOR]

Channel estimation is challenging for the reconfigurable intelligent surface (RIS)-aided wireless communications. Since the number of coefficients of the cascaded channel among the base station (BS), the RIS, and the user equipment (UE), is the product of the number of BS antennas, the number of RIS elements, and the number of UEs, the pilot overhead can be prohibitively high. In this paper, we propose a two-timescale channel estimation framework to exploit the property that the BS-RIS channel is high-dimensional but quasi-static, while the RIS-UE channel is mobile but low-dimensional. Specifically, to estimate the quasi-static BS-RIS channel, we propose a dual-link pilot transmission scheme, where the BS transmits downlink pilots and receives uplink pilots reflected by the RIS. Then, we propose a coordinate descent-based algorithm to recover the BS-RIS channel. Since the quasi-static BS-RIS channel is estimated less frequently than the mobile channel is, the average pilot overhead can be reduced from a long-term perspective. Although the mobile RIS-UE channel has to be frequently estimated in a small timescale, the associated pilot overhead is low thanks to its low dimension. Simulation results show that the proposed two-timescale channel estimation framework can achieve accurate channel estimation with low pilot overhead. [ABSTRACT FROM AUTHOR]

Huge overhead of beam training imposes a significant challenge in millimeter-wave (mmWave) wireless communications. To address this issue, in this paper, we propose a wide beam based training approach to calibrate the narrow beam direction according to the channel power leakage. To handle the complex nonlinear properties of the channel power leakage, deep learning is utilized to predict the optimal narrow beam directly. Specifically, three deep learning assisted calibrated beam training schemes are proposed. The first scheme adopts convolution neural network to implement the prediction based on the instantaneous received signals of wide beam training. We also perform the additional narrow beam training based on the predicted probabilities for further beam direction calibrations. However, the first scheme only depends on one wide beam training, which lacks the robustness to noise. To tackle this problem, the second scheme adopts long-short term memory (LSTM) network for tracking the movement of users and calibrating the beam direction according to the received signals of prior beam training, in order to enhance the robustness to noise. To further reduce the overhead of wide beam training, our third scheme, an adaptive beam training strategy, selects partial wide beams to be trained based on the prior received signals. Two criteria, namely, optimal neighboring criterion and maximum probability criterion, are designed for the selection. Furthermore, to handle mobile scenarios, auxiliary LSTM is introduced to calibrate the directions of the selected wide beams more precisely. Simulation results demonstrate that our proposed schemes achieve significantly higher beamforming gain with smaller beam training overhead compared with the conventional and existing deep-learning based counterparts. [ABSTRACT FROM AUTHOR]

Low-latency and energy-efficient wireless communication holds the potential of enabling the industrial internet of things (IIoT), automatic driving, and telesurgery. Cross-layer scheduling, which is aware of both the channel and queue states, has attracted considerable attention recently because it is capable of reducing the average delay substantially while meeting a given average power constraint. As a result, there has been considerable work, in which joint channel and buffer aware scheduling is formulated as a constrained Markov decision process (CMDP). In general, the optimal solution to a CMDP problem is characterized by the stationary probability of actions, yielding probabilistic cross-layer scheduling with possibly high complexity in practice. In this paper, we are interested in the low-latency and energy-efficient stationary cross-layer scheduling policy for wireless channels with continuous channel states, e.g. Rayleigh fading. It is interestingly shown that a deterministic cross-layer scheduling policy can achieve the optimal tradeoff between the average delay and power. In other words, the signaling complexity of cross-layer scheduling can be significantly reduced without causing sub-optimality. Simulation results also demonstrate that our work provides a low-complexity solution for low-latency and energy-efficient wireless communications. [ABSTRACT FROM AUTHOR]

The Terahertz band is envisioned to meet the demanding 100 Gbps data rates for 6G wireless communications. Aiming at combating the distance limitation problem with low hardware-cost, ultra-massive MIMO with hybrid beamforming is promising. However, relationships among wavelength, array size and antenna spacing give rise to the inaccuracy of planar-wave channel model (PWM), while an enlarged channel matrix dimension leads to excessive parameters of applying spherical-wave channel model (SWM). Moreover, due to the adoption of hybrid beamforming, channel estimation (CE) needs to recover high-dimensional channels from severely compressed channel observation. In this paper, a hybrid spherical- and planar-wave channel model (HSPM) is investigated and proved to be accurate and efficient by adopting PWM within subarray and SWM among subarrays. Furthermore, a two-phase HSPM CE mechanism is developed. A deep convolutional-neural-network (DCNN) is designed in the first phase for parameter estimation of reference subarrays, while geometric relationships of the remaining channel parameters between reference subarrays are leveraged to complete CE in the second phase. Extensive numerical results demonstrate the HSPM is accurate at various communication distances, array sizes and carrier frequencies. The DCNN converges fast and achieves high accuracy with 5.2 dB improved normalized-mean-square-error compared to literature methods, and owns substantially low complexity. [ABSTRACT FROM AUTHOR]

This paper investigates the fundamental covert rate performance in a wireless relaying system consisting of a source-destination pair, a full-duplex (FD) relay and a warden, where the relay can work at either the FD mode or the half-duplex (HD) mode. We first provide theoretical modeling for the instantaneous/average covert rate when the system works solely under the FD mode or HD mode, and then explore the corresponding optimal transmit power control of relay for the covert rate maximization. For an improvement of covert rate, we further propose a joint FD/HD mode that flexibly switches between the FD and HD modes depending on channel state of the relay self-interference channel. Under the joint FD/HD mode, we also examine the related problems of theoretical modeling for covert rate and optimal transmit power control of relay for covert rate maximization. Finally, extensive numerical results are provided to illustrate the covert rate performances of the relaying system under the FD, HD and joint FD/HD modes. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the benefit of using intelligent reflecting surface (IRS) in multi-user multiple-input single-output (MU-MISO) systems, in the presence of eavesdroppers. We maximize the weighted sum secrecy rate by jointly designing the secure beamforming (BF), the artificial noise (AN), as well as the phase shift of the IRS. An alternating optimization (AO) method is proposed to deal with the formulated non convex problem. In particular, the secure beamforming and AN jamming matrix are optimally designed via the successive convex approximation (SCA) approach for given phase shift, which can be derived by considering the alternating direction method of multiplier (ADMM) and element-wise block coordinate decent (EBCD) methods. Finally, simulation results are presented to show the benefit of the IRS in terms of improving the secrecy performance, when compared to other methods. [ABSTRACT FROM AUTHOR]

Low-latency communication is expected to play a key role in the Industrial Internet of Things (IIoT). Although there has been considerable effort on reducing latency, few attention has been focused on the low-latency transmission of distortion-tolerant data, e.g. IIoT’s analog samples, over fading channels. In this paper, we present a cross-layer approach to jointly adapt the transmission power, rate, and compression ratio based on the instantaneous buffer and channel states. In particular, to minimize the average delay under both average power and distortion constraints, we formulate a Constrained Markov Decision Process (CMDP) with multi-dimensional state and action spaces. A novel solution is then presented by judiciously decomposing the multi-dimensional CMDP into a deterministic hierarchical optimization consisting of a linear programming and a convex optimization problem. Furthermore, we show the optimality of a threshold-based scheduling policy that highly reduces the complexity and present an optimal delay-power-distortion tradeoff that characterizes a fundamental performance tradeoff between the physical, network, and application layers. The joint lossy compression and power allocation scheme realizes a deep cross-layer optimization of source coding, queuing control, and wireless transmission, which outperforms the traditional cross-layer design consisting of only two layers, not to mention layered protocols. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of a mixed radio-frequency-underwater wireless optical communication (RF-UWOC) system where an unmanned aerial vehicle (UAV), as a low-altitude mobile aerial base station, transmits information to an autonomous underwater vehicle (AUV) through a fixed-gain amplify-and-forward (AF) or decode-and-forward (DF) relay. The analysis accounts for the main factors that affect the system performance, such as the UAV height, air bubbles, temperature gradient, water salinity variations, detection techniques, and pointing errors. Employing the fixed-gain AF and DF relays, expressions for some key performance metrics are derived, e.g., outage probability (OP), average bit error rate (ABER), and average channel capacity. Moreover, in order to get further insights, asymptotic analyses for the OP and ABER are also carried out. Additionally, for the two relaying systems, we derive analytical expressions for the optimal UAV altitude that minimizes the OP. Simulation results show that the UAV altitude influences the system performance and there is an optimal altitude which ensures a minimum OP. Furthermore, it is demonstrated that the diversity order of the fixed-gain AF relaying depends on the RF link and pointing errors, while the diversity order of the DF relaying depends on the detection techniques and pointing errors. [ABSTRACT FROM AUTHOR]

The use of channel state information (CSI) at the transmitter significantly enhances the performance of wireless communication systems. However, the requirement of CSI feedback places an undue burden on the reverse link, especially in links that employ multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), where CSI takes the form of a precoding matrix (precoder) for each subcarrier. Typical deployments use quantization and feedback of CSI at certain subcarriers, with interpolation to fill in missing CSI at the transmitter. Past work has used the orthogonal structure of precoders with Flag manifolds for quantization and interpolation of CSI, although interpolation is complicated due to the absence of analytic expressions for geodesics on Flag manifolds. Other approaches have involved the parameterization of the precoder into scalar parameters that are amenable to quantization and interpolation. In this paper, we present efficient methods to quantize and interpolate on Flag manifolds, using both optimal algorithms as well as simplified suboptimal algorithms. Further, we unify these with the parameterization based approaches and show that these translate directly to low-complexity quantization and interpolation on Flag manifolds. Simulations reveal that the proposed precoder quantization and interpolation effectively enhance achievable rates with limited complexity. [ABSTRACT FROM AUTHOR]

Conventional authentication techniques based on cryptography and computational hardness are facing growing challenges for deployment in resource-constrained Internet-of-Things (IoT) devices. The dramatically increased security overhead and latency from the inherent computational processing make these conventional static security techniques undesirable for emerging machine communications. In this paper, we propose a novel lightweight continuous authentication scheme for identifying multiple resource-constrained IoT devices via their pre-arranged pseudo-random access time sequences. A transmitter will be authenticated as legitimate if and only if its access time sequential order is matched with a pre-agreed unique pseudo-random binary sequence (PRBS) between itself and the base station. The seed for generating the PRBS between each transceiver pair is acquired by exploiting the channel reciprocity, which is time-varying and difficult for a third party to predict. Hence, the proposed scheme provides seamless protection for legitimate communications by refreshing the seeds adaptively without incurring long latency, complex computation, and high communication overhead. Our results show that the proposed scheme achieves high entropy and low bit mismatch rate. Finally, we demonstrate the superiority of our scheme over the existing schemes in quantization performance, authentication performance, and computation cost. [ABSTRACT FROM AUTHOR]

Large intelligent surfaces (LISs) constitute a new and promising wireless communication paradigm that relies on the integration of a massive number of antenna elements over the entire surfaces of man-made structures. The LIS concept provides many advantages, such as the capability to provide reliable and space-intensive communications by effectively establishing line-of-sight (LOS) channels. In this paper, the system spectral efficiency (SSE) of an active LIS system is asymptotically analyzed under a practical LIS environment with a well-defined uplink frame structure. In order to verify the impact on the SSE of pilot contamination, the SSE of a multi-LIS system is asymptotically studied and a theoretical bound on its performance is derived. Given this performance bound, an optimal pilot training length for multi-LIS systems subjected to pilot contamination is characterized and, subsequently, the number of devices that need to be serviced by the LIS in order to maximize the performance is derived. Simulation results show that the derived analyses are in close agreement with the exact mutual information in presence of a large number of antennas, and the achievable SSE is limited by the effect of pilot contamination and intra/inter-LIS interference through the LOS path, even if the LIS is equipped with an infinite number of antennas. Additionally, the SSE obtained with the proposed pilot training length and number of scheduled devices is shown to reach the one obtained via a brute-force search for the optimal solution. [ABSTRACT FROM AUTHOR]

Although grant-based mechanisms have been a predominant approach for wireless access for years, the additional latency required for initial handshake message exchange and the extra control overhead for packet transmissions have stimulated the emergence of grant-free (GF) transmission. GF access provides a promising mechanism for carrying low and moderate traffic with small data and fits especially well for massive machine type communications (mMTC) applications. Despite a surge of interest in GF access, how to handle heterogeneous mMTC traffic based on GF mechanisms has not been investigated in depth. In this paper, we propose a priority enabled GF access scheme which performs dynamic slot allocation in each 5G new radio subframe to devices with different priority levels on a subframe-by-subframe basis. While high priority traffic has access privilege for slot occupancy, the remaining slots in the same subframe will be allocated to low priority traffic. To evaluate the performance of the proposed scheme, we develop a two-dimensional Markov chain model which integrates these two types of traffic via a pseudo-aggregated process. Furthermore, the model is validated through simulations and the performance of the scheme is evaluated both analytically and by simulations and compared with two other GF access schemes. [ABSTRACT FROM AUTHOR]

Doppler effect is a fundamental phenomenon that appears in wave propagation, where a moving observer experiences dilation or contraction of the wavelength of a wave. It also appears in radio frequency (RF) wireless communication when there exists a relative movement between the transmitter and the receiver, and is widely considered as a major impairment for reliable wireless communication. The current paper proposes Doppler assisted wireless communication (DAWC) that exploits Doppler effect and uses kinetic energy for co-channel interference (CCI) mitigation. The proposed system also exploits the propagation environment and the network topology and consists of an access point (AP) with a rotating drum antenna. The rotating drum receive antenna is designed in such a way that it shifts the interfering signals away from the desired signal band. This paper includes a detailed system model, and the results show that under favorable fading conditions, CCI can be significantly reduced. Therefore, it is anticipated that more sophisticated wireless systems and networks can be designed by extending the basic ideas proposed herein. [ABSTRACT FROM AUTHOR]

This paper provides an analytical performance characterization of both the uplink (UL) and downlink (DL) user-centric network multiple-input multiple-output (MIMO) systems, where a cooperating base station (BS) cluster is formed for each user individually and the clusters for different users may overlap. In this model, cooperating BSs (each equipped with multiple antennas) jointly perform zero-forcing beamforming to the set of single-antenna users associated with them. As compared with a baseline network MIMO system with disjoint BS clusters, the effect of user-centric clustering is that it improves signal strength in both the UL and DL, while reducing cluster-edge interference in the DL. This paper quantifies these effects by assuming that BSs and users form Poisson point processes and by further approximating both the signal and interference powers using Gamma distributions of appropriate parameters. We show that BS cooperation provides significant gain as compared to single-cell processing for both the UL and DL, but the advantage of user-centric clustering over the baseline disjoint clustering system is significant for the DL cluster-edge users only. Although the analytic results are derived with the assumption of perfect channel state information and infinite backhaul between the cooperating BSs, they nevertheless provide architectural insight into the design of the future cooperative cellular networks. [ABSTRACT FROM AUTHOR]

In this paper, we consider an uplink heterogeneous cloud radio access network (H-CRAN), where a macro base station (BS) coexists with many remote radio heads (RRHs). For cost savings, only the BS is connected to the baseband unit (BBU) pool via fiber links. The RRHs, however, are associated with the BBU pool through wireless fronthaul links, which share the spectrum resource with radio access networks. Due to the limited capacity of fronthaul, the compress-and-forward scheme is employed, such as point-to-point compression or Wyner–Ziv coding. Different decoding strategies are also considered. This paper aims to maximize the uplink ergodic sum-rate (SR) by jointly optimizing quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links, which is a mixed time-scale issue. To reduce computational complexity and communication overhead, we introduce an approximation problem of the joint optimization problem based on large-dimensional random matrix theory, which is a slow time-scale issue, because it only depends on statistical channel information. Finally, an algorithm based on Dinkelbach’s algorithm is proposed to find the optimal solution to the approximate problem. In summary, this paper provides an economic solution to the challenge of constrained fronthaul capacity and also provides a framework with less computational complexity to study how bandwidth allocation and fronthaul compression can affect the SR maximization problem. [ABSTRACT FROM AUTHOR]

Multiple-input Multiple-output (MIMO) technologies play an important role in modern and future wireless communication systems as they can improve the capacity without increasing the bandwidth. MIMO detection is one of the key technologies for MIMO system designs. MIMO detection schemes based on Message Passing (MP) algorithms have attracted extensive attention in recent years. The MIMO detection scheme based on Expectation Propagation (EP), which is also a kind of MP algorithms, has been proved to achieve the Bayes-optimal performance under the conditions of large system limit and compression rate threshold. However, there is improvement space for the detection performance of the EP algorithm when the conditions are not satisfied, which are the common cases in the practical applications. In this paper, when the conditions of the large system limit and compression rate threshold are not satisfied, we analyze the influences of two factors, the initial parameters selection and the moment matching strategy, on the performance of EP based detection scheme. As a result, we propose an improved EP detection scheme based on optimizing the two factors. Simulation results show that the proposed scheme outperforms the original EP detection scheme in different MIMO scenarios. [ABSTRACT FROM AUTHOR]

Communication system design for wireless networked control systems (WNCSs) requires strict timing, reliability and lifetime guarantees despite limited battery resources and the non-idealities introduced by wireless networking such as delays. In this paper, we introduce radio frequency (RF) energy harvesting paradigm into WNCS framework for the first time in the literature. We study the optimal power control, energy harvesting and scheduling problem with the objective of providing maximum level of adaptivity under periodicity, delay and reliability requirements. We show that the power allocation problem is separable from the scheduling problem at optimality and provide the exact expression for optimal power control. The scheduling problem is then formulated as a mixed integer linear programming (MILP) problem and proven to be NP-Hard. For the scheduling, we propose polynomial-time heuristic algorithms motivated by the analogy between scheduling sensor nodes with energy harvesting requirements over time units and jobs with sequence dependent setup times on identical machines. We prove the theoretical worst-case bound for the performance of these heuristics. We show via extensive simulations that the proposed algorithms perform close-to-optimal and significantly better than Earliest Deadline First (EDF) algorithm in terms of adaptivity, delay, reliability and average runtime. [ABSTRACT FROM AUTHOR]