Semantic communication (SemCom) is emerging as a key technology for future sixth-generation (6G) systems. Unlike traditional bit-level communication (BitCom), SemCom directly optimizes performance at the semantic level, leading to superior communication efficiency. Nevertheless, the task-oriented nature of SemCom renders it challenging to completely replace BitCom. Consequently, it is desired to consider a semantic-bit coexisting communication system, where a base station (BS) serves SemCom users (sem-users) and BitCom users (bit-users) simultaneously. Such a system faces severe and heterogeneous inter-user interference. In this context, this paper provides a new semantic-bit coexisting communication framework and proposes a spatial beamforming scheme to accommodate both types of users. Specifically, we consider maximizing the semantic rate for semantic users while ensuring the quality-of-service (QoS) requirements for bit-users. Due to the intractability of obtaining the exact closed-form expression of the semantic rate, a data driven method is first applied to attain an approximated expression via data fitting. With the resulting complex transcendental function, majorization minimization (MM) is adopted to convert the original formulated problem into a multiple-ratio problem, which allows fractional programming (FP) to be used to further transform the problem into an inhomogeneous quadratically constrained quadratic programs (QCQP) problem. Solving the problem leads to a semi-closed form solution with undetermined Lagrangian factors that can be updated by a fixed point algorithm. This method is referred to as the MM-FP algorithm. Additionally, inspired by the semi-closed form solution, we also propose a low-complexity version of the MM-FP algorithm, called the low-complexity MM-FP (LP-MM-FP), which alleviates the need for iterative optimization of beamforming vectors. Extensive simulation results demonstrate that the proposed MM-FP algorithm outperforms conventional beamforming algorithms such as zero-forcing (ZF), maximum ratio transmission (MRT), and weighted minimum mean-square error (WMMSE). Moreover, the proposed LP-MMFP algorithm achieves comparable performance with the WMMSE algorithm but with lower computational complexity.

The advent of Industry 4.0 has positioned the industrial Internet of Things (IIoT) as a cornerstone of future industry. In this article, the problem of spectral-efficient communication and computation resource allocation for distributed reconfigurable intelligent surfaces (RISs) assisted probabilistic semantic communication (PSC) in IIoT is investigated. In the considered model, multiple RISs are deployed to serve multiple users, while PSC adopts compute-then-transmit protocol to reduce the size of the transmission data. To support the high-rate transmission, the semantic compression ratio, transmit power allocation, and distributed RISs deployment must be jointly considered. This joint communication and computation problem is formulated as an optimization problem whose goal is to maximize the sum semantic-aware transmission rate of the system under the total transmit power, phase shift, RIS-user association, and semantic compression ratio constraints. To solve this problem, a many-to-many matching scheme is proposed to solve the RIS-user association subproblem, the semantic compression ratio subproblem is addressed following the greedy policy, while the phase shift of RIS can be optimized using the tensor-based beamforming. Numerical results verify the superiority of the proposed algorithm.

Channel estimation in hybrid massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is challenging as only low-dimensional channel measurement can be obtained at the receiver while the true channel is high-dimensional. In the compressed sensing (CS) based channel estimation algorithms, a well-designed measurement matrix will contribute to better estimation performance. Therefore, a temporal correlation-based self-adaptive measurement matrix design (TC-SAMMD) method is proposed, and the corresponding frame structure and channel estimation technique are developed in this paper. Specifically, pilots are transmitted twice during each block, and the process of channel estimation contains three steps: time-varying channel estimation by Kalman filtering, self-adaptive measurement matrix design, and two pilots-based channel estimation via the simultaneous orthogonal matching pursuit (SOMP) algorithm. Simulation results demonstrate that the proposed TC-SAMMD method outperforms conventional measurement matrix design approaches in terms of the channel estimation error, thanks to the “adaptiveness" to the channel’s characteristics in consecutive blocks. Besides, it is shown that the TC-SAMMD strategy with a full-ranged innovation noise can effectively mitigate the performance degradation caused by the angle shifts.

This paper delves into the potential of intelligent reflecting surfaces (IRSs) in enabling integrated sensing and communication (ISAC) in multi-user multi-path scenarios. We introduce a three-dimensional (3D) multi-user ISAC framework with distributed IRSs, which offers simultaneous signal demodulation, channel estimation, and localization. The transmission is divided into a user access stage and a downlink transmission stage. In the first stage, we propose an algorithm for simultaneous uplink signal demodulation and angles of arrival (AoA) estimation at the semi-passive IRS. Moreover, a joint active and passive beamforming scheme inspired by radar-communication, is proposed to enhance both communication and localization performance in the downlink stage, while eliminating the need for distinct localization reference signals. Numerical results demonstrate that the proposed ISAC framework achieves centimeter-level localization accuracy while maintaining comparable communication performance to communication-only systems, thus validating its effectiveness.

An integrated sensing and communication (ISAC) system assisted by distributed passive intelligent reflecting surfaces (IRSs) is proposed in this paper, which enables simultaneous signal demodulation and location sensing, without channel state information (CSI). A detailed workflow of the proposed IRS-based ISAC system is designed, including transmission protocol, joint location sensing and demodulation, as well as beamforming optimization. Specifically, each coherent block consists of two stages, and each stage is divided into two time blocks, where the signal demodulation, channel estimation, and location sensing are conducted by the proposed integrated localization and demodulation (I-LAD) algorithm, simultaneously. Simulation results show that the signal demodulation performance of the proposed I-LAD algorithm is close to that of the benchmark scheme assuming perfect CSI. At the same time, the channel estimation performance is comparable to that of the parallel factor decomposition (PAPRFAC) method or the compressive sensing-based channel estimation (CS-EST) method. In addition, the proposed location sensing scheme almost achieves the Cramér-Rao lower bound (CRLB) for distributed IRS-assisted localization-only systems, confirming the effectiveness of the proposed I-LAD algorithm.

Federated edge learning(FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model-updates with the server to complete the global model training. Due to limited bandwidth and uncertain wireless environment, FEEL may impose heavy burden to the current communication system. In addition, under the common FEEL framework, the server needs to wait for the slowest device to complete the update uploading before starting the aggregation process, leading to the straggler issue that causes prolonged communication time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1) performing data compression on the edge devices and 2) setting a deadline on the edge server to exclude the straggler devices. However, undesired gradient compression errors and transmission outage are introduced by the aforementioned operations respectively, affecting the convergence of FEEL as well. In view of these practical issues, we formulate a training time minimization problem, with the compression ratio and deadline to be optimized. To this end, an asymptotically unbiased aggregation scheme is first proposed to ensure zero optimality gap after convergence, and the impact of compression error and transmission outage on the overall training time are quantified through convergence analysis. Then, the formulated problem is solved in an alternating manner, based on which, the noveljoint compression and deadline optimization(JCDO) algorithm is derived. Numerical experiments for different use cases in FEEL including image classification and autonomous driving show that the proposed method is nearly 30X faster than the vanilla FedSGD algorithm, and outperforms the state-of-the-art schemes.

In terahertz (THz) massive multiple-input multiple-output (MIMO) systems, the combination of huge bandwidth and massive antennas results in severe beam split, thus making the conventional phase-shifter based hybrid precoding architecture ineffective. With the incorporation of true-time-delay (TTD) lines in the hardware implementation of the analog precoders, delay-phase precoding (DPP) emerges as a promising architecture to effectively overcome beam split. However, existing DPP approaches suffer from poor performance, high complexity, and weak robustness in practical THz channels. In this paper, we propose a novel DPP approach in wideband THz massive MIMO systems. First, the matrix decomposition optimization problem is converted into a compressive sensing (CS) form, which can be solved by the proposed extended spatially sparse precoding (SSP) algorithm. To compensate for beam split, frequency-dependent measurement matrices are designed, which can be approximately realized by feasible phase and delay codebooks. Furthermore, several efficient atom selection techniques are developed to further reduce the complexity of the extended SSP algorithm. In simulation, the proposed DPP approach achieves superior performance, complexity, and robustness by using it alone or in combination with existing DPP approaches under various settings.

In this paper, we investigate the issue of uplink integrated sensing and communication (ISAC) in 6G wireless networks where the sensing echo signal and the communication signal are received simultaneously at the base station (BS). To effectively mitigate the mutual interference between sensing and communication caused by the sharing of spectrum and hardware resources, we provide a joint sensing transmit waveform and communication receive beamforming design with the objective of maximizing the weighted sum of normalized sensing rate and normalized communication rate. It is formulated as a computationally complicated non-convex optimization problem, which is quite difficult to be solved by conventional optimization methods. To this end, we first make a series of equivalent transformation on the optimization problem to reduce the design complexity, and then develop a deep learning (DL)-based scheme to enhance the overall performance of ISAC. Both theoretical analysis and simulation results confirm the effectiveness and robustness of the proposed DL-based scheme for ISAC in 6G wireless networks.

This paper presents a study on the integrated sensing and communication (ISAC) for symbiotic radio systems in mobile scenarios, where the receiver shared by the primary and secondary systems is capable of locating the moving object of secondary systems via estimating the direction of arrival (DoA), and detecting the symbols for both systems. For the considered symbiotic radio systems, the strong interference from the primary systems and moving object pose significant challenges for location sensing. Moreover, the symbol detection for symbiotic radio systems typically relies on channel state information (CSI), which would reduce the spectrum efficiency substantially. To address these challenges, this paper proposes a low-complexity and high-accuracy two-dimension DoA estimator based on the ratio of dual points on the main lobe using a single snapshot. The theoretical performance in terms of effective sensing probability and mean square error for the proposed estimator are provided in closed-form. To eliminate the dependence of symbol detection on CSI, we propose to fully capture the characteristic of the ISAC systems and introduce two types of DoA-assisted detectors to detect both the primary and secondary symbols. Specifically, the reciprocal linear detectors that use heuristic linear combiners and iterative detection to achieve mutual benefit for both transmissions, as well as a Manchester detector that can separate the primary and secondary signals, are proposed. Finally, the numerical results are provided to validate the correctness of the theoretical analysis, and demonstrate the superiority of the proposed DoA estimator and DoA-assisted detectors.

We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. The commonly adopted compression schemes introduce information loss into local data while improving communication efficiency, and it remains an open problem whether such discrete-valued mechanisms provide any privacy protection. In this paper, we study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP). More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms, including the binomial noise and the binomial mechanisms that are proposed for privacy preservation, and the sign-based methods that are proposed for data compression, in closed-form expressions. We further investigate the amplification in privacy by sparsification and propose a ternary stochastic compressor. By leveraging compression for privacy amplification, we improve the existing methods by removing the dependency of accuracy (in terms of mean square error) on communication cost in the popular use case of distributed mean estimation, therefore breaking the three-way tradeoff between privacy, communication, and accuracy.

This article considers a novel intelligent reflecting surface (IRS)-assisted cell-free symbiotic radio (SR) system, where an IRS is employed to facilitate the transmission from all the base stations (BSs) to the primary receivers (PRs), i.e., the primary transmission, and simultaneously, to send messages to the Internet of Things (IoT) devices, i.e., the secondary transmission. We aim to minimize the bit error rate (BER) of the IRS symbols by jointly optimizing the transmit beamformers at all the BSs and the IRS phase shifts. To solve this nonconvex problem, we propose a plenty-based algorithm by using the multidimensional complex quadratic transform (MCQT) and the consensus alternative direction method of multipliers (ADMMs) methods, and obtain a high-performance solution. Besides, the convergence of the proposed algorithm is analyzed. Simulation results demonstrate the effectiveness of the proposed algorithm and show that the proposed algorithm outperforms two baselines under different setups.

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. As over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication, thus by substituting the wireless transmission in the traditional split ML framework with OAC, the communication load can be eased. In this paper, we propose to deploy split ML in a wireless multiple-input multiple-output (MIMO) communication network utilizing the intricate interplay between MIMO-based OAC and NN. The basic procedure of the OAC split ML system is first provided, and we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over a MIMO channel carrying out multi-stream analog communication. The precoding and combining matrices which are regarded as trainable parameters, and the MIMO channel matrix, which are regarded as unknown (implicit) parameters, jointly serve as a fully connected layer of the NN. Most interestingly, the channel estimation procedure can be eliminated by exploiting the MIMO channel reciprocity of the forward and backward propagation, thus greatly saving the system costs and/or further improving its overall efficiency. The generalization of the proposed scheme to the conventional NNs is also introduced, i.e., the widely used convolutional NNs. We demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations.

Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and energy consumption. However, channel estimation in the context of HAD is challenging due to only limited radio frequency (RF) chains at transceivers. Although various compressive sensing (CS) algorithms have been developed to solve this problem by exploiting inherent channel sparsity and sparsity structures, practical effects, such as power leakage and beam squint, can still make the real channel features deviate from the assumed models and result in performance degradation. Besides, the high complexity of CS algorithms caused by a large number of iterations hinders their applications in practice. To tackle these issues, we develop a deep learning (DL)-based channel estimation approach where the sparse Bayesian learning (SBL) algorithm is unfolded into a deep neural network (DNN). In each SBL layer, Gaussian variance parameters of the sparse angular domain channel are updated by a tailored DNN, which is able to capture complicated channel sparsity structures in various domains effectively and efficiently. The measurement matrix is jointly optimized for performance improvement. Then, the proposed approach is extended to the multi-block case where channel correlation in time is further exploited to adaptively predict the measurement matrix and facilitate the update of variance parameters. Simulation results show that the proposed approaches outperform existing approaches in terms of both performance and complexity.

Reconfigurable intelligent surface (RIS) has shown its great potential in facilitating device-based integrated sensing and communication (ISAC), where sensing and communication tasks are mostly conducted on different time-frequency resources. While the more challenging scenarios of simultaneous sensing and communication (SSC) have so far drawn little attention. In this paper, we propose a novel RIS-aided ISAC framework where the inherent location information in the received communication signals from a blind-zone user equipment is exploited to enable SSC. We first design a two-phase ISAC transmission protocol. In the first phase, communication and coarse-grained location sensing are performed concurrently by exploiting the very limited channel state information, while in the second phase, by using the coarse-grained sensing information obtained from the first phase, simple-yet-efficient sensing-based beamforming designs are proposed to realize both higher-rate communication and fine-grained location sensing. We demonstrate that our proposed framework can achieve almost the same performance as the communication-only frameworks, while providing up to millimeter-level positioning accuracy. In addition, we show how the communication and sensing performance can be simultaneously boosted through our proposed sensing-based beamforming designs. The results presented in this work provide valuable insights into the design and implementation of other ISAC systems considering SSC.

In this paper, we establish an integrated sensing and communication (ISAC) system based on a distributed semi-passive intelligent reflecting surface (IRS), which allows location sensing and data transmission to be conducted on the same time-frequency resources. The detailed working process of the proposed IRS-based ISAC system is designed, including the transmission protocol, location sensing and beamforming optimization. Specifically, each coherence block consists of the channel estimation period, the ISAC period with two time blocks, and the pure communication (PC) period. During the channel estimation period, the low-dimensional effective user-BS channel is estimated. During each time block of the ISAC period, data transmission and user positioning are carried out simultaneously. The estimated user location in the first time block will be used for beamforming design in the second time block. During the PC period, only data transmission is conducted, by invoking the user location estimated in the second time block of the ISAC period for beamforming design. Simulation results show that a millimeter-level positioning accuracy can be achieved by the proposed location sensing scheme. Besides, the proposed two beamforming schemes based on the estimated location achieve similar performance to the benchmark schemes assuming perfect channel state information, verifying the effectiveness of beamforming design using sensed location information.

In this paper, we exploit the potential benefits of intelligent reflecting surface (IRS) in achieving integrated sensing and communication (ISAC) at the same frequency and time resources. To this end, we establish a novel framework, in which a single-antenna user transmits to a multi-antenna base station, with the aid of a distributed semi-passive IRS. In the established framework, the transmission period is divided into two time blocks. At each time block, the distributed semi-passive IRS conducts the location sensing and data transmission simultaneously. Simple-yet-efficient location sensing and beamforming design schemes are respectively proposed. Particularly, the estimated user location in the first time block is used to facilitate the beamforming design of the IRS in the second time block. Through numerical results, we demonstrate that the proposed location sensing scheme can achieve a millimeter-level positioning accuracy, even when the number of semi-passive reflecting elements is small and the allocated sensing time is short. In addition, we show that, utilizing the imperfect location information obtained from our proposed location sensing scheme, the proposed beamforming design scheme can achieve almost the same performance as the optimal beamforming scheme assuming the perfect channel state information, thus verifying the effectiveness of our proposed framework and providing valuable insights into the design of other IRS-enabled ISAC systems.

In this paper, we investigate a wireless-powered covert communication (WP-CC) system, where a full-duplex (FD) receiver transmits artificial noise (AN) to simultaneously charge an energy-constrained transmitter and to confuse a warden’s detection on the transmitter’s communication activity. A probabilistic accumulate-then-transmit (ATT) protocol, where the transmitter sends its information with a prior probability p conditioned on the available energy being sufficient, is proposed to maximize the communication covertness subject to a requirement on the communication quality. In order to facilitate the optimal design of the prior probability p and the information transmit power in the considered WP-CC system, we also derive the warden’s minimum detection error probability and characterize the effective covert rate from the transmitter to the receiver to quantify the communication covertness and quality, respectively. Our examination shows that the proposed probabilistic ATT protocol can achieve higher communication covertness than the traditional ATT protocol with p = 1.

The core requirement of massive Machine-Type Communication (mMTC) is to support reliable and fast access for an enormous number of machine-type devices (MTDs). In many practical applications, the base station (BS) only concerns the list of received messages instead of the source information, introducing the emerging concept of unsourced random access (URA). Although some massive multiple-input multiple-output (MIMO) URA schemes have been proposed recently, the unique propagation properties of millimeter-wave (mmWave) massive MIMO systems are not fully exploited in conventional URA schemes. In grant-free random access, the BS cannot perform receive beamforming independently as the identities of active users are unknown to the BS. Therefore, only the intrinsic beam division property can be exploited to improve the decoding performance. In this paper, a URA scheme based on beam-space tree decoding is proposed for mmWave massive MIMO system. Specifically, two beam-space tree decoders are designed based on hard decision and soft decision, respectively, to utilize the beam division property. They both leverage the beam division property to assist in discriminating the sub-blocks transmitted from different users. Besides, the first decoder can reduce the searching space, enjoying a low complexity. The second decoder exploits the advantage of list decoding to recover the miss-detected packets. Simulation results verify the superiority of the proposed URA schemes compared to the conventional URA schemes in terms of error probability.

In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user’s position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional data (here the location coordinates), which often results in overfitting and large prediction error. To this end, we resort to a novel physics-inspired generative approach. Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths. We further incorporate the most recently developed sinusoidal representation network (SIREN) into the proposed network to implicitly represent the highly dynamic phases of all possible paths, which usually cannot be well predicted by the conventional neural networks with non-periodic activators. The resultant framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also extended to the MIMO channel case. Key performance measures including prediction accuracy, convergence speed, network scale and robustness to channel estimation error are comprehensively evaluated and compared with existing popular networks, which show that our proposed network is much more efficient in representing, learning and predicting wireless channels in a given communication environment.

Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure for an intelligent and simplified sixth-generation (6G) wireless network. Nevertheless, to further enhance the capacity and coverage, more radio remote heads (RRHs) as well as high-fidelity and low-latency fronthaul links are required, which may lead to high implementation cost. To address this issue, we propose to exploit the intelligent reflecting surface (IRS) as an alternative way to enhance the C-RAN, which is a low-cost and energy-efficient option. Specifically, we consider the uplink transmission where multi-antenna users communicate with the baseband unit (BBU) pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs. RRHs can conduct either point-to-point (P2P) compression or Wyner-Ziv coding to compress the received signals, which are then forwarded to the BBU pool through fronthaul links. We investigate the joint design and optimization of user transmit beamformers, IRS passive beamformers, and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding. By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation (SDR), we propose a successive convex approximation approach to solve non-convex problems, and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided. Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.

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

The roll-out of various emerging wireless services has triggered the need for the sixth-generation (6G) wireless networks to provide functions of target sensing, intelligent computing and information communication over the same radio spectrum. In this paper, we provide a unified framework integrating sensing, computing, and communication to optimize limited system resource for 6G wireless networks. In particular, two typical joint beamforming design algorithms are derived based on multi-objective optimization problems (MOOP) with the goals of the weighted overall performance maximization and the total transmit power minimization, respectively. Extensive simulation results validate the effectiveness of the proposed algorithms. Moreover, the impacts of key system parameters are revealed to provide useful insights for the design of integrated sensing, computing, and communication (ISCC).

The challenge in massive Machine Type Communication (mMTC) is to support reliable and instant access for an enormous number of machine-type devices (MTDs). In some particular applications of mMTC, the access point (AP) only has to know the messages received, but not where they source from, thus giving rise to the concept of unsourced random access (URA). In this paper, we propose a novel URA scheme exploiting the elegant properties of Reed-Muller (RM) sequences. Specifically, after dividing the message of an active user into several information chunks, RM sequences are used to carry those chunks, for exploiting the vast sequence space to improve the spectral efficiency, and their nested structure to enable reliable and efficient sequence detection. Next, we further explore a novel structural property of RM sequences for designing sparse patterns which carry part of the information and serve as the hints of coupling the information chunks of a single user. The factors affecting the performance of our slot-based RM detection are characterized. Besides, the complexity of the proposed message stitching method is analyzed and compared to the commonly used tree coding approach. Our simulation results verify the enhanced performance of the proposed URA scheme in error probability and computational complexity compared to the existing counterpart.

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of performance and complexity. In this paper, an attention mechanism, exploiting the channel distribution characteristics, is proposed to improve the estimation accuracy of highly separable channels with narrow angular spread by realizing the “divide-and-conquer” policy. Specifically, we introduce a novel attention-aided DL channel estimation framework for conventional massive MIMO systems and devise an embedding method to effectively integrate the attention mechanism into the fully connected neural network for the hybrid analog-digital (HAD) architecture. Simulation results show that in both scenarios, the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead. Furthermore, strong robustness under different system and channel parameters can be achieved by the proposed approach, which further strengthens its practical value. We also investigate the distributions of learned attention maps to reveal the role of attention, which endows the proposed approach with a certain degree of interpretability.

Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In the context of hybrid analog-digital (HAD) transceivers, channel estimation becomes even more complicated due to information loss caused by limited radio-frequency chains. The conventional compressive sensing (CS) algorithms usually suffer from unsatisfactory performance and high computational complexity. In this paper, we propose a novel deep learning (DL) based framework for uplink channel estimation in HAD massive MIMO systems. To better exploit the sparsity structure of channels in the angular domain, a novel angular space segmentation method is proposed, where the entire angular space is segmented into many small regions and a dedicated neural network is trained offline for each region. During online testing, the most suitable network is selected based on the information from the global positioning system. Inside each neural network, the region-specific measurement matrix and channel estimator are jointly optimized, which not only improves the signal measurement efficiency, but also enhances the channel estimation capability. Simulation results show that the proposed approach significantly outperforms the state-of-the-art CS algorithms in terms of estimation performance and computational complexity.

In this paper, we propose a novel semi-passive elements-aided channel estimation framework for intelligent reflecting surface (IRS), where a small portion of IRS reflecting elements are able to process the received signal for facilitating the channel estimation. Specifically, the BS-IRS channel is estimated by applying the estimation of signal parameters via rotational invariance technique (ESPRIT), while the user-IRS channels are estimated by combining the use of total least square (TLS) ESPRIT and multiple signal classification (MUSIC) methods. The required training time of the proposed channel estimation scheme is irrelevant to the number of IRS reflecting elements, thus substantially reducing the training overhead. Simulation results show the great advantages of our proposed scheme over both the conventional compressed sensing (CS)-based channel estimation and cascaded channel estimation schemes.

Intelligent reflecting surface (IRS) is a promising solution for enabling the control of wireless environments. Most of existing works consider that an IRS consists of a large number of reconfigurable passive elements and each of them can reflect the signal independently. However, it is difficult and costly in practice to manufacture this kind of large-scale IRS. In this paper, we propose a practical electromagnetic-compliant reflection coefficient model for element-grouping IRS, which configures eachsub-surface(including multiple homogeneous elements) but not each single element. Under this model, the reflection amplitude and the phase shift are coupled, which brings new challenges for optimizing the IRS configuration. We study a phase shift optimization problem to maximize the received power in an IRS-aided wireless system. As this problem is difficult to solve due to the complicated coupling in the practical model, we transform it into avector compositionproblem equivalently. By constructing a geometry-based phase analysis model, we analyze the relationship of the vectors in a two-dimensional space to maximize the length of the sum vector. By exploiting the geometrical properties, we propose a low-complexity phase control algorithm to find the optimal phase shifts of IRS. Simulation results reveal that substantial performance gains are achieved by the proposed algorithm compared to the conventional schemes.

Millimeter-wave (mmW)/Terahertz (THz) wideband communication employing a large-scale antenna array is a promising technique of the sixth-generation (6G) wireless network for realizing massive machine-type communications (mMTC). To reduce the access latency and the signaling overhead, we design a grant-free random access scheme based on joint active device detection and channel estimation (JADCE) for mmW/THz wideband massive access. In particular, by exploiting the simultaneously sparse and low-rank structure of mmW/THz channels with spreads in the delay-angular domain, we propose two multi-rank aware JADCE algorithms via applying the quotient geometry of product of complex rank-$L$matrices with the number of clusters$L$. It is proved that the proposed algorithms require a smaller number of measurements than the currently known bounds on measurements of conventional simultaneously sparse and low-rank recovery algorithms. Statistical analysis also shows that the proposed algorithms can linearly converge to the ground truth with low computational complexity. Finally, extensive simulation results confirm the superiority of the proposed algorithms in terms of the accuracy of both activity detection and channel estimation.

Intelligent reflecting surface (IRS) is a newly emerged technology that can increase the energy and spectral efficiency of wireless communication systems. This paper considers an IRS-aided multi-user multiple-input single-output (MISO) communication system, and presents a detailed analysis and optimization framework for the weighted sum-rate (WSR) of the downlink transmission over Rician fading channels. Unlike most of the prior works where the active beamformer at the base station (BS) and passive beamformer at the IRS are jointly designed based on the instantaneous channel state information (CSI), this paper proposes a low-complexity transmission protocol where the IRS passive beamforming and BS power allocation coefficient vector are optimized in the large timescale based on the statistical CSI, and the BS transmit beamforming is designed in the small timescale based on only the instantaneous CSI of the effective BS-user channels. Therefore, the channel training overhead in each channel coherence interval under our proposed protocol is independent of the number of IRS reflecting elements, which is in sharp contrast to most of the prior works. By considering maximum-ratio transmit beamforming at the BS, we derive a lower bound of the ergodic WSR in closed-form. Then, we propose an efficient algorithm to jointly optimize the IRS passive beamforming and BS power allocation coefficient vector for maximizing the ergodic WSR lower bound. Numerical results validate the tightness of our derived WSR bound and show that the proposed scheme outperforms various existing schemes in terms of complexity or capacity performance.

In this paper, we investigate the optimal design of a wireless-powered covert communication (WP-CC) system, where a full-duplex (FD) receiver transmits artificial noise (AN) to simultaneously charge an energy-constrained transmitter and to confuse a warden’s detection on the transmitter’s communication activity. In order to achieve a higher level of covertness, we propose a probabilistic accumulate-then-transmit (ATT) protocol, where the transmitter is able to adjust the prior probability conditioned on the available energy being sufficient, i.e.,$p$, rather than setting$p=1$as in the traditional ATT protocol to maximize the system throughput. Then, we derive the warden’s minimum detection error probability and characterize the effective covert rate from the transmitter to the receiver to quantify the communication covertness and quality, respectively. The derived analytical results facilitate the joint optimization of the probability$p$and the information transmit power to maximize the communication covertness subject to a quality-of-service (QoS) requirement on communication. We further present the optimal design of a cable-powered covert communication (CP-CC) system as a benchmark for comparison. Our simulation shows that the proposed probabilistic ATT protocol (with a varying$p$) can achieve the covertness upper bound determined by the CP-CC system, while the traditional ATT protocol (with$p=1$) cannot, which confirms the benefits brought by the proposed probabilistic ATT protocol in covert communications.

Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional algorithms including the zero-forcing (ZF) algorithm and the weighted minimum mean square error (WMMSE) algorithm failed to achieve a satisfactory trade-off between complexity and performance. In this paper, leveraging on the power of deep learning, we propose a low-complexity precoding design framework for MU-MIMO systems. The key idea is to transform the MIMO precoding problem into the multiple-input single-output precoding problem, where the optimal precoding structure can be obtained in closed-form. A customized deep neural network is designed to fit the mapping from the channels to the precoding matrix. In addition, the technique of input dimensionality reduction, network pruning, and recovery module compression are used to further improve the computational efficiency. Furthermore, the extension to the practical MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) system is studied. Simulation results show that the proposed low-complexity precoding scheme achieves similar performance as the WMMSE algorithm with very low computational complexity.

Federated edge learning (FEEL) is a popular distributed learning framework that allows privacy-preserving collaborative model training via periodic learning-updates communication between edge devices and server. Due to the constrained bandwidth, only a subset of devices can be selected to upload their updates at each training iteration. This has led to an active research area in FEEL studying the optimal device scheduling policy for communication time minimization. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially and indirectly by minimizing either the iteration rounds or per-round latency, while the total communication time is determined by both metrics. To close this research gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the remaining communication time through cross-disciplinary effort that combines the learning theory for convergence rate analysis and communication theory for per-round latency analysis. Building on the novel analytical result, an optimized probabilistic device scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. Extensive experiments based on real-world dataset and a use case on collaborative 3D objective detection in autonomous driving are provided to verify the superiority of the proposed policy over three benchmark policies based on the indirect solution approaches.

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning-based designs have exhibited great potential in terms of both performance and complexity. In this paper, an attention-aided deep neural network is proposed for channel estimation in hybrid analog-digital massive MIMO systems. Specifically, the integrated attention mechanism automatically realizes the “divide-and-conquer” policy to exploit the distribution characteristics of highly separable channels with narrow angular spread. Simulation results show that the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead, and the strong robustness further strengthens the practical value of the proposed approach. Moreover, the distributions of learned attention maps are also investigated to reveal the role of attention and endow the proposed approach with a certain degree of interpretability.

Mobile channel estimation is very challenging as usually it requires more pilots and channel observations to obtain the channel state information (CSI) and the resultant estimation accuracy may decrease with the number of antennas and sub-carriers. Through exploring the long-and-short-term intrinsic spatial and temporal correlation among a set of historic channel instances randomly obtained within a certain communication environment, channel prediction can help increase the CSI accuracy w.r.t. to that obtained from only the pilots, and thus save signaling overhead and computational cost. In this paper, we propose a novel generative Periodic-Activator-enabled Auto Encoder-LSTM network (GPAE-LSTMnet) for accurate channel prediction of mobile MIMO channels, which first compresses the high dimensional channel matrix with high-frequency features to a low dimensional space with relatively low-frequency feature space that has high data smoothness and is suitable for time-series sequence prediction. After that, a LSTM network is used to predict the channel in the low dimensional space, which ensures high accuracy and low computational cost. Experimental results show that our proposed learning structure outperforms existing methods especially when the dimension of CSI to be predicted is relatively high, the time interval of the CSI sequence is relatively long and the number of network parameters is highly limited.

Millimeter-wave (mmWave) imaging has attracted much attention due to its potential applications in next generation wireless networks. However, how to design robust and efficient signaling and reconstruction algorithm still remains very challenging. In this paper, with the aid of the newly emerged intelligent reflecting surfaces (IRS), we propose a novel millimeter-wave computational imaging method exploiting the enormous illuminating patterns provided by Reed-Muller (RM) sequences. In particular, we construct a deterministic sensing matrix using the RM sequences with which to stimulate the objects via an intelligent reflecting surface, so as to modulate the amplitude and phase of the incident wave indirectly and increase the electromagnetic degrees of freedom. Compared with Zadoff-Chu sequence, Hadamard sequence and random sequence modulated signal illumination method, our proposed approach converges faster and achieves nearly the same accuracy as the illuminations increase, and has less hardware overhead.

Recently, millimeter wave (mmWave) imaging has received widespread attention. However, due to its nonlinearity and ill-posedness, it is challenging to reconstruct the precise electromagnetic properties of unknown targets from the measured scattered fields. In this paper, a new concentrative intelligent reflecting surface (IRS) aided computational imaging scheme is proposed. In the scheme, by dividing the region of imaging (ROI) into pixels, the imaging process is transformed into a compressed sensing problem. This paper proposes a fast block sparse Bayesian learning (BSBL) algorithm, which exploits the block sparsity of the reflection vector of ROI, and reduces the computational complexity through the generalized approximate message passing (GAMP) algorithm. Finally, the simulation results validate the performance advantages of the proposed algorithm and the efficiency of IRS in the imaging process.

In this article, we investigate the issue of massive access in a beyond fifth-generation (B5G) multibeam low-Earth orbit (LEO) satellite Internet-of-Things (IoT) network in the presence of channel phase uncertainty due to channel-state information (CSI) conveyance from the devices to the satellite via the gateway. Rather than time-division multiple access (TDMA) or frequency-division multiple access (FDMA) with multicolor pattern, a new nonorthogonal multiple access (NOMA) scheme is adopted to support massive IoT distributed over a very wide range. Considering the limited energy on the LEO satellite, two robust beamforming algorithms against channel phase uncertainty are proposed for minimizing the total power consumption in the scenarios of noncritical IoT applications and critical IoT applications, respectively. Both theoretical analysis and simulation results validate the effectiveness and robustness of the proposed algorithms for supporting massive access in satellite IoT.

With the increasing development of Internet of Things (IoT), the upcoming sixth-generation (6G) wireless network is required to support grant-free random access of a massive number of sporadic traffic devices. In particular, at the beginning of each time slot, the base station (BS) performs joint activity detection and channel estimation (JADCE) based on the received pilot sequences sent from active devices. Due to the deployment of a large-scale antenna array and the existence of a massive number of IoT devices, conventional JADCE approaches usually have high computational complexity and need long pilot sequences. To solve these challenges, this paper proposes a novel deep learning framework for JADCE in 6G wireless networks, which contains a dimension reduction module, a deep learning network module, an active device detection module, and a channel estimation module. Then, prior-feature learning followed by an adaptive-tuning strategy is proposed, where an inner network composed of the Expectation-maximization (EM) and back-propagation is introduced to jointly tune the precision and learn the distribution parameters of the device state matrix. Finally, by designing the inner layer-by-layer and outer layer-by-layer training method, a feature-aided adaptive-tuning deep learning network is built. Both theoretical analysis and simulation results confirm that the proposed deep learning framework has low computational complexity and needs short pilot sequences in practical scenarios.

Reconfigurable intelligent surface (RIS) is a promising solution to build a programmable wireless environment with reconfigurable passive elements, which can achieve high spectral and energy efficiency. In this paper, we investigate the ergodic capacity of RIS-assisted multi-user multiple-input single-output (MISO) wireless systems in both uplink and downlink scenarios. Unlike most of prior works, where instantaneous channel state information (CSI) is assumed, we consider the realistic scenario with only statistical CSI. For both scenarios, we first present an analytical expressions for the ergodic sum capacity of the system. Based on which, the joint power control (or transmit beamforming) and phase shift design problem maximizing the ergodic sum capacity is formulated. Capitalizing on the alternating direction method of multipliers (ADMM), fractional programming (FP) and alternating optimization (AO) methods, efficient suboptimal solutions are obtained for the non-convex design problems. Simulation results are presented to validate the accuracy of the analytical ergodic sum capacity expressions and evaluate the impact of key system parameters such as CSI, Rician$K$factor, number of RIS elements, and RIS location on the ergodic capacity performance. The findings suggest that the proposed statistical CSI design achieves decent performance compared with the instantaneous CSI based design. Moreover, a signal hot spot can be created when placing the RIS close to the users.

This paper considers an angle-domain intelligent reflecting surface (IRS) system. We derive maximum likelihood (ML) estimators for the effective angles from the base station (BS) to the user and the effective angles of propagation from the IRS to the user. It is demonstrated that the accuracy of the estimated angles improves with the number of BS antennas. Also, deploying the IRS closer to the BS increases the accuracy of the estimated angle from the IRS to the user. Then, based on the estimated angles, we propose a joint optimization of BS beamforming and IRS beamforming, which achieves similar performance to two benchmark algorithms based on full CSI and the multiple signal classification (MUSIC) method respectively. Simulation results show that the optimized BS beam becomes more focused towards the IRS direction as the number of reflecting elements increases. Furthermore, we derive a closed-form approximation, upper bound and lower bound for the achievable rate. The analytical findings indicate that the achievable rate can be improved by increasing the number of BS antennas or reflecting elements. Specifically, the BS-user link and the BS-IRS-user link can obtain power gains of order N and NM2, respectively, where N is the antenna number and M is the number of reflecting elements.

In this article, we investigate the issue of integrated sensing, computation and communication (SCC) in beyond fifth-generation (B5G) cellular internet of things (IoT) networks. According to the characteristics of B5G cellular IoT, a comprehensive design framework integrating SCC is put forward for massive IoT. For sensing, highly accurate sensed information at IoT devices are sent to the base station (BS) by using non-orthogonal communication over wireless multiple access channels. Meanwhile, for computation, a novel technique, namely over-the-air computation (AirComp), is adopted to substantially reduce the latency of massive data aggregation via exploiting the superposition property of wireless multiple access channels. To coordinate the co-channel interference for enhancing the overall performance of B5G cellular IoT integrating SCC, two joint beamforming design algorithms are proposed from the perspectives of the computation error minimization and the weighted sum-rate maximization, respectively. Finally, extensive simulation results validate the effectiveness of the proposed algorithms for B5G cellular IoT over the baseline ones.

Massive machine-type communication (mMTC) is expected to provide reliable and low-latency connectivity for an enormous number of devices, which turn active sporadically or frequently. In this highly dynamic situation, it is crucial to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this article, by exploiting the large sequence space, the excellent correlation property and especially the elegant nested structure of Reed-Muller (RM) sequences, we propose a new RA scheme, which facilitates both instantaneous access for newly active users and incremental access for the existing users who suffer from detection failures. In particular, when a failure occurs, the user continues accessing the channel employing an expanded RM sequence, which is combined with the previously received ones at the access point (AP) to form a longer sequence so as to attain potentially better detection probability. Furthermore, a recursive detection algorithm is designed for jointly detecting the resultant RM sequences and the channel coefficients of both the newly active users and the existing ones. The performance of the proposed algorithm is analyzed in detail. Our simulation results validate the analysis and show the scheme's superior access probability, access latency and computational complexity.

This paper proposes an angle domain framework for the design of an intelligent reflecting surface (IRS) system. The maximum likelihood (ML) principle is applied to derive the estimators for the effective angles among the base station (BS), IRS and user. It is demonstrated that the accuracy of the estimated angles improves with the number of BS antennas. Also, deploying the IRS closer to the BS increases the accuracy of the estimated angle from the IRS to the user. Then, exploiting the estimated angles, we propose a joint design of BS beamforming and IRS beamforming. Simulation results show that our proposed algorithm, which only needs few angle information, achieves nearly the same performance as the algorithm requiring full channel state information (CSI). Moreover, the optimized BS beam becomes more focused towards the IRS direction as the number of reflecting elements increases.

This paper proposes a novel location information aided design framework for multiple intelligent reflecting surface (IRS) systems. Assuming practical and imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order NM2is achieved, where N is the number of BS antennas and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs.

This paper designs a cooperative activity detection framework for massive grant-free random access in the sixth-generation (6G) cell-free wireless networks based on the covariance of the received signals at the access points (APs). In particular, multiple APs cooperatively detect the device activity by only exchanging the low-dimensional intermediate local information with their neighbors. The cooperative activity detection problem is non-smooth and the unknown variables are coupled with each other for which conventional approaches are inapplicable. Therefore, this paper proposes a covariance-based algorithm by exploiting the sparsity-promoting and similarity-promoting terms of the device state vectors among neighboring APs. An approximate splitting approach is proposed based on the proximal gradient method for solving the formulated problem. Simulation results show that the proposed algorithm is efficient for large-scale activity detection problems while requires shorter pilot sequences compared with the state-of-art algorithms in achieving the same system performance.

Millimeter-wave/Terahertz (mmW/THz) communications have shown great potential for wideband massive access in next-generation cellular internet of things (IoT) networks. To decrease the length of pilot sequences and the computational complexity in wideband massive access, this paper proposes a novel joint activity detection and channel estimation (JADCE) algorithm. Specifically, after formulating JADCE as a problem of recovering a simultaneously sparse-group and low rank matrix according to the characteristics of mmW/THz channel, we prove that jointly imposing l0norm and low rank on such a matrix can achieve a robust recovery under sufficient conditions, and verify that the number of measurements derived for the mmW/THz wideband massive access system is significantly smaller than currently known measurements bound derived for the conventional simultaneously sparse and low-rank recovery. Furthermore, we propose a multi-rank aware method by exploiting the quotient geometry of product of complex rank-Lmaxmatrices with the maximum number of scattering clusters Lmax. Theoretical analysis and simulation results confirm the superiority of the proposed algorithm in terms of computational complexity, detection error rate, and channel estimation accuracy.

In the mMTC scenario, enormous devices turn active sporadically or frequently to seek for opportunities to transmit short packets. In this highly dynamic situation, it is critical to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this paper, we propose an incremental RA scheme exploiting the nested Reed-Muller (RM) sequences. Specifically, the users who suffer from access failures expand their RM sequences following the given expansion rule, which utilizes both the nested structure and the cross-correlation property of RM sequences. At the receiver, a recursive detection algorithm is proposed, which exploits the discrepancy in the sequence length to detect the retransmission users progressively. On the other hand, new active users continuously spring up in the system, thus causing the incremental number of users seeking for access. In this case, the proposed scheme can detect newly active users together with retransmission ones with great detection capability and low access latency. Our simulation results verify the superior performance of the proposed RA scheme.

In this paper, we design a multi-beam low earth orbit (LEO) satellite internet of things (IoT) for beyond fifth-generation (B5G) wireless networks. Rather than time division multiple access (TDMA), a new non-orthogonal multiple access (NOMA) scheme is adopted to support massive IoT over a very wide range. In order to reduce the power consumption of multi-beam satellite, a spot beam design algorithm is proposed with the goal of minimizing the total power consumption subject to quality-of-service (QoS) requirements. Furthermore, considering high computational complexity of spot beam design in the context of massive IoT, a simple multi-beam design algorithm is provided. Finally, simulation results confirm the effectiveness of the proposed algorithms over conventional ones.

The beyond fifth-generation (B5G) cellular internet of things (IoT) network is required to support low-power and wide-coverage wireless access for a massive number of devices. However, the severe co-channel interference caused by massive access decreases the reliability and coverage. To solve this problem, we design a large intelligent reflecting surface (IRS) aided massive access framework, including channel estimation and information transmission. Furthermore, we analyze the performance of the proposed framework, and reveal the role of the large IRS. It is found that the large IRS is beneficial to improve the performance of edge-devices, and thus enhance the coverage. Moreover, we find that the refection coefficient should be carefully chosen according to channel conditions and system parameters to improve the performance.

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable individual rate is presented, which is valid for arbitrary system configurations. Besides, assuming a large number of reflecting elements, a simple approximated expression for the multicast rate is derived. A closed-form solution is obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the number of reflecting elements is sufficiently large. The analytical findings indicate that, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

In this paper, we investigate the issue of integrated computation and communication in beyond fifth-generation (B5G) cellular internet of things (IoT) networks with massive connectivity. By exploiting the open nature of wireless channels, a comprehensive deign framework integrating computation and communication over the same spectrum is first put forward for massive IoT. To achieve efficient integration of computation and communication under practical but adverse conditions, a robust algorithm is proposed by jointly optimizing transmit power and receive beamforming, with the goal of minimizing the computation error of computation signals while guaranteeing the requirement of communication signals. Finally, extensive simulations validate the robustness and effectiveness of the proposed algorithm for B5G cellular IoT.

This article addresses the issue of symbol detection in ambient backscatter communication systems with the multiantenna reader. Focusing on the ON-OFF keying modulation, the optimal detector minimizing the bit error rate (BER) is devised based on the maximum a posteriori principle. We then analyze the exact closed-form BER expression for the optimal detector. Moreover, simple approximate BER expressions are derived in certain asymptotic regimes. Furthermore, a simple energy detector is analyzed as a benchmark scheme, and the asymptotic BER is devised in a closed form. The findings of this article suggest that implementing multiple antennas at the reader is an effective means to enhance the BER performance and extend the tag-reader communication range. Also, the optimal detector always outperforms the energy detector. In particular, the optimal detector can avoid the error floor phenomenon, which is inevitable for the energy detector in the high SNR regime.

In this article, we apply the large intelligent reflecting surface (IRS) technique in beyond fifth-generation (B5G) cellular Internet of Things (IoT) to satisfy the requirements of massive connectivity, low power, and wide coverage. First, we design a framework for the large IRS-aided B5G cellular IoT, including channel estimation, uplink data transmission, and downlink data transmission. Then, we analyze the performance of the proposed framework, and reveal the impacts of key parameters of the large IRS on the spectral efficiency. Next, we propose a low-complexity time-length allocation algorithm to minimize the total energy consumption of B5G cellular IoT. Finally, extensive simulation results validate the accuracy of the derived theoretical expressions and the effectiveness of the proposed algorithm.

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable multicast rate is presented, which is valid for arbitrary system configurations. In addition, for certain asymptotic scenario, simple approximated expressions for the multicase rate are derived. Closed-form solutions are obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the pilot power or the number of reflecting elements is sufficiently large. However, it is desirable to allocate more power to weaker users when there are a large number of RF chains. The analytical findings indicate that, with large pilot power, the multicast rate is determined by the weakest user. Also, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

This article proposes a novel location information aided multiple intelligent reflecting surface (IRS) systems. Assuming imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order N M2is achieved, where N is the antenna number and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M, while becomes proportional to M2as non-line-of-sight (NLOS) paths vanish. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs. Finally, an optimal power allocation scheme has been proposed to improve the system performance.

We consider a wireless powered two-way relaying system consisting of two energy constrained single antenna sources and one multi-antenna relay with constant power supply. The time division protocol is adopted, where the relay first acts as the energy source and employs energy beamforming to charge the two sources, and then switches its role as a relay to help forward the information to the sources. To maintain user fairness, we aim to maximize the minimum rate of two sources, by jointly optimizing the energy beamforming vector, time splitting factor and relay transformation matrix. To further reduce the complexity of the optimal algorithm, we propose an alternating optimization method, where closed-form expressions for the energy beamforming and time splitting factor are obtained. To gain more insights, we propose a simple suboptimal design and analyze the outage probability and the average rate when the relay applies simple energy beamforming and transformation matrix. The analysis shows that the system can achieve a diversity order of $\frac {N}{3}$ for a system with a relay of $N$ antennas. Numerical results show that the performance of the proposed low-complexity alternating optimization method approaches the optimal algorithm over the entire SNR range, but has a significantly low complexity, and the proposed suboptimal design can also achieves a decent performance especially in small $N$ regime.

In this paper, a fast adaptive Principal Component Analysis (PCA) based scheme is proposed to estimate and track the channel state information (CSI) of two dimensional (2D) massive multiple-input and multiple-output (M-MIMO) systems with uniform rectangular array (URA). First, the signals received online at the base station (BS) are used to estimate and track the principal components. Then, based on the estimated signal eigenvectors, a 2D unitary estimating signal parameters via rotational invariance technique (ESPRIT) algorithm is introduced to jointly estimate and track the channel coefficients which include the direction of arrival (DoA) of elevation and azimuth angles and the channel gain corresponding to each resolvable path of the channel. In order to improve the tracking speed, an optimal step size is derived which can accelerate the convergence speed of channel tracking significantly. Since the 2D unitary ESPRIT algorithm is applied, the proposed method can reduce computational complexity by converting the complex data matrix to the real one. Simulation results are provided to verify the estimation and tracking accuracy of the proposed scheme.

In beyond fifth-generation (B5G) cellular internet of things (IoT) networks, energy supply and data aggregation of a massive number of devices are two vitally challenging issues. To address these challenges, we propose a wireless powered MIMO over-the-air computation (AirComp) design framework. Firstly, wireless power transfer (WPT) is utilized to charge massive IoT devices simultaneously by exploiting the open nature of wireless broadcast channel. Then, AirComp is adopted to reduce latency of massive data aggregation via exploring the superposition property of wireless multiple-access channel. To realize efficient convergence of energy supply and data aggregation in practical IoT networks, a robust design algorithm is provided by jointly optimizing beamforming of both WPT and AirComp. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones.

This paper investigates the performance of angle-domain millimeter-wave (mmWave) multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) systems in the presence of angular estimation error. A closed-form expression for the achievable rate of the system is derived. Based on which, a simple asymptotic approximation is obtained. The findings of paper suggest that, with a large number of BS antennas, the user rate is mainly constrained by the antenna number to beam number ratio (ANTBNR) and the spatial direction distance. In particular, increasing the ANTBNR would cause a severe rate loss, and the achievable rate is an increasing function with respect to the spatial direction distance. Capitalizing on this key observation, a novel cluster grouping scheme is designed to reduce the inter-cluster interference, which shows significant performance gain over a random cluster grouping scheme. Finally, simulation results are provided to corroborate the analytical results.

With the increasing development of cellular internet of things (IoT), the upcoming fifth generation (5G) wireless network is required to support massive IoT with sporadic traffic. In order to realize massive access over limited radio spectrum, a three-phase transmission protocol which consists of device detection and channel estimation, uplink data transmission and downlink data transmission is designed for the cellular IoT. In particular, we analyze the performance of the proposed transmission protocol, and reveal the impact of system parameters on the sum rate. Finally, simulation results validate the effectiveness of the theoretical claims.

In this paper, when transmit antenna selection (TAS) and maximal ratio combining (MRC) schemes are deployed for secrecy enhancement, the impact of channel correlation on secrecy performance is investigated over Rayleigh fading channels. Specially, both the exact and asymptotic secrecy outage probability (SOP) for TAS/MRC are derived based on a novel correlation model, where the correlated legitimate channels and eavesdropper channels are modeled as a set of conditional independent channel gains. Moreover, the lower bound of SOP is obtained in high signal-to-noise ratio (SNR) region. Finally, simulation results verify our analysis, and show that the channel correlation between the legitimate receiver and the eavesdropper is beneficial to the SOP at high SNR.

Ambient backscatter communication is a newly emerged ultra-low-power technology for the internet of things network. In this paper, we study the symbol detection of multi-antenna ambient backscatter communication system. In particular, the maximum-eigenvalue detector is derived from general likelihood ratio test, and the approximative BER expressions are characterized. The analytical results show that the BER of the proposed detector decreases with sampling rate N, signal-to-noise ratio γ and number of receiving antenna M, however, settles in high γ or M regime. In addition, we find the proposed detector eliminates the knowledge of noise power, which is of uncertainty and difficult to estimate. Then the simulation results validate the correctness of the theoretical analysis, and show that the proposed detector outperforms the existing energy detector in terms of BER performance.

Principle component analysis (PCA) can be used to estimate the eigenvalues and eigenvectors of high dimensional data set with low complexity. By using this instinct feature, an adaptive PCA based channel parameter estimation and tracking method is proposed for two dimensional (2D) massive multiple-input and multiple-output (M-MIMO) systems with uniform rectangular arrays (URAs) of antennas in this paper. The online received data is used to estimate and track the channel parameters (including the direction of arrival angles and the path gain). Since the adaptive PCA method is used, the proposed scheme has low complexity and high accuracy for estimation and tracking which are verified via the numerical simulations.

In this paper, a low complexity channel parameter estimation method is proposed for two-dimensional (2D) uniform rectangular array (URA) massive multiple-input and multiple-output (MIMO) systems. Instead of assuming independent fading between different transmit-receive antenna pairs, a physical channel which models the realistic scattering environment via the angles and gains associated with different propagation paths is studied. A novel 2D Fourier transform (FT) based on elevation and azimuth steering factors is designed to derive the spatial spectrum distribution of received signals at base-station (BS). Accordingly, the received data matrices are used to estimate the channel parameters, which includes the direction of arrival (DOA) angles and the channel gains respective to each resolvable path. Since the channel coefficients are estimated from the DOA perspective and the proposed 2D FT can be realized by Fast-Fourier-Transform (FFT), the computational complexity is reduced significantly. Simulation results are provided to verify the accuracy and the complexity of the proposed scheme.

With the increasing development of the cellular Internet of Things (IoT), the upcoming fifth-generation wireless network is required to support massive access of sporadic traffic devices. In this context, we design a three-phase transmission protocol which consists of device detection and channel estimation, uplink data transmission, and downlink data transmission for the cellular IoT, so as to realize massive access over limited radio spectrum. We analyze the performance of the proposed transmission protocol and derive closed-form expressions for the uplink and downlink achievable rates in terms of channel conditions and system parameters. Moreover, to improve the overall performance, we propose a length allocation algorithm by coordinating the three-phase transmission protocol in the unified sense. Extensive simulation results show that substantial performance gain can be obtained by the proposed algorithm.

This paper investigates the achievable performance of cell-free massive multiple-input multiple-output (MIMO) systems with low resolution analog-to-digital converters (ADCs) at both the access points (APs) and users. A closed-form expression for the achievable rate is derived, which enables the study of the effects of AP number, antenna number per AP, user number, and ADC resolution on the achievable rate. In addition, a simple asymptotic approximation for the individual user rate is presented, which shows that the user rate is mainly constrained by the ADC resolution at the user. Moreover, we propose an ADC resolution bits allocation scheme aiming at maximizing the sum rate subject to the total ADC resolution bits constraint, which substantially outperforms the equal ADC resolution bits allocation scheme. Furthermore, a max-min power control scheme is proposed, which not only ensures user fairness, but also improves the achievable rate. Finally, simulation results are provided to corroborate the analytical results.

In this paper, considering both the antenna correlation and the channel correlation between the legitimate receiver and the eavesdropper, we comprehensively investigate the secrecy performance for transmit antenna selection and maximal ratio combining (TAS/MRC) scheme in a multi-input multi-output multi-antenna eavesdropper (MIMOME) system. Different from the existing works, the correlated legitimate channels and correlated eavesdropper channels experience Nakagmai-${m}$fading with distinct fading parameters, and are modeled as a combination of conditionally independent channel gains and independent channel gains. By this novel correlation representation, the combined effect of antenna correlation and channel correlation between the legitimate receiver and the eavesdropper on secrecy performance is quantified via the secrecy outage probability (SOP) under a more general fading circumstance. In addition, two special cases of correlated channels are addressed in detail. Finally, simulations are performed to verify our analysis. The analysis and simulation results show that at high signal-to-noise ratio (SNR), antenna correlation at the legitimate receiver can improve the SOP performance for the correlated MIMOME wiretap channels, and this finding is contrary to the conventional wisdom that antenna correlation degrades the SOP performance.

In existing communication systems, the channel state information of each user equipment (UE) should be repeatedly estimated when it moves to a new position or when another UE takes its place. The underlying ambient information, including the specific layout of potential reflectors, which provides more detailed information about all UEs' channel structures, has not been fully explored and exploited. In this paper, we rethink the mm-wave channel estimation problem in a new and indirect way, i.e., instead of estimating the resultant composite channel response at each time, and for any specific location, we first conduct the ambient perception exploiting the fascinating radar capability of a mm-wave antenna array and then accomplish the location-based sparse channel reconstruction. In this way, the sparse channel for a quasi-static UE arriving at a specific location can be rapidly synthesized based on the perceived ambient information, thus greatly reducing the signaling overhead and online computational complexity. Based on the reconstructed mm-wave channel, single-beam mm-wave communication is designed and evaluated which shows an excellent performance. Such an approach, in fact, integrates the radar with communication, which may possibly open a new paradigm for future communication system design.

In this paper, we investigate the issue of sustainable communications for beyond fifth-generation (B5G) cellular internet of things (IoT) networks under adverse but practical conditions. A massive number of simple IoT devices without batteries harvest requisite energy from a part of the received signal. A design framework including channel state information (CSI) acquisition, signal construction, information decoding and energy harvesting, is first provided for sustainable communications of massive IoT. Then, based on the proposed design framework, we reveal the impacts of practically adverse factors, e.g., channel uncertainty, successive interference cancellation (SIC) and non-linear energy harvesting, on the performance of B5G cellular IoT. Furthermore, in order to effectively alleviate the impacts of these adverse factors, an outage-constrained robust algorithm is designed to maximize the overall performance of sustainable B5G cellular IoT. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones.

This paper presents a detailed secrecy performance analysis of correlated multi-antenna wiretap channels with three popular diversity combining schemes, namely maximal ratio combining, selection combining, and equal gain combining at the legitimate receiver. For single-input multiple-output wiretap channels, both exact and asymptotic expressions are derived for the secrecy outage probability (SOP) of these systems. The findings suggest that, compared with the scenario where all the channels are independent, correlation of the main channels alone increases the SOP by a factor of 1/ det(U), where det(U) is the determinant of correlation matrix U. In contrast, when the average SNR at the legitimate receiver is much larger than that at the eavesdropper, correlation between the main channels and the eavesdropper channels has a positive effect on SOP. Moreover, in the case of multiple-input and multiple-output wiretap channels, two transmit antenna selection schemes with or without the eavesdropper's channel state information are evaluated by the SOP and the average secrecy capacity for the considered correlated channel model, respectively. Finally, numerical simulations are conducted to corroborate the analytical results.

The ambient backscatter communication is a newly rising paradigm for the Internet-of-Things networks, which enables the connection of low-cost devices. This paper proposes a novel MFSK modulation for the Tag of ambient backscatter communications systems, and the corresponding detectors are designed depending on the capability of the Reader. In the case the Reader is not capable of removing the direct interference from the ambient source, a maximum likelihood detector is proposed. In another case, leveraging on the frequency shift feature of the MFSK modulation, the Reader can remove the direct interference. Then, a simple energy detector is proposed, and the closed-form expressions for the symbol error rate (SER) and outage probability of the system are derived. The findings of this paper suggest that the proposed MFSK modulation outperforms the popular ON-OFF keying modulation, and the impact of modulation order on the SER performance depends heavily on the operating bit signal to noise ratio. Moreover, it is shown that it is desirable to place the Tag close to the Reader in terms of minimizing the outage probability.

This paper deals with symbol detection in ambient backscatter communication systems with multi-antenna reader. Unlike most of the existing works which assume deterministic ambient radio frequency (RF) signals, we consider another important scenario with complex Gaussian RF signals. Focusing on the on-off keying modulation, the optimal detector minimizing the bit error rate (BER) is devised based on the maximum a posteriori principle, and an exact closed-form expression for the BER is derived. In addition, a simple energy detector is proposed to serve as a performance benchmark. Simulation results show that, implementing multiple antennas at the reader is an effective means to enhance the BER performance. Also, the proposed optimal detector always outperforms the energy detector. Furthermore, unlike the energy detector, whose BER settles in the high signal to noise ratio regime, no error floor exists for the proposed optimal detector.

We study the effects of low resolution analog-to-digital converters (ADCs) on cell-free massive multiple-input multiple-output (MIMO) systems. We first derive a closed-form expression for the achievable rate, which enables efficient evaluation of the impact of key parameters on system performance. Then, we give a simple approximate closed-form expression for the individual user rate, which indicates that the ADC resolution at the user is the main constraining factor for the user rate. Furthermore, we study the allocation of ADC resolution bits among different access points (APs) with fixed total resolution bits. It has been shown that our proposed ADC resolution bits allocation scheme is substantially superior to the equal ADC resolution bits allocation scheme. Finally, we provide simulation results to verify our analytical results.

As a promising candidate for future 5G and beyond, millimeter-wave (mmWave) has received considerable attention. However, mmWave channel estimation remains to be a challenging problem. To tackle this issue, this paper proposes a mmWave joint radar and communication system for indoor scenarios. By cooperating with mmWave radar, the propagation environment is perceived. Then, high-accuracy channel estimates are obtained, which helps in designing more accurate precoders and combiners. Compared with the conventional channel estimation methods, the proposed scheme significantly reduces the spectrum overhead and computational complexity, especially when massive antennas and multiple users are involved. Our results may shed some lights on the system design for future mmWave communications.

Secrecy performance is investigated for relay networks with multiple antennas Nt at the transmitter. As K relays are available, we propose an incremental threshold relaying scheme with low complexity, where both transmit antenna selection (TAS) and relay selection are deployed without eavesdropper channel state information (CSI). Both the exact and approximate secrecy outage probability (SOP) of proposed scheme are derived with outdated CSI under the cases of maximal ratio combining (MRC) scheme at the legitimate receiver. In the high signalto-noise ratio (SNR) regime, due to outdated CSI, the secrecy diversity order of MRC decreases to Nt+1 for relay selection and K + 1 for antenna selection. Finally, simulation results validate our analysis, and show that the proposed scheme alleviates the effect of the transmitter and relay links on SOP.

This paper considers the security issue of the fifth-generation wireless networks with massive connections, where multiple eavesdroppers aim to intercept the confidential messages through active eavesdropping. To realize secure massive access, non-orthogonal channel estimation and non-orthogonal multiple access techniques are combined to enhance the signal quality at legitimate users, while the inter-user interference is harnessed to deliberately confuse the eavesdroppers even without exploiting artificial noise. We first analyze the secrecy performance of the considered secure massive access system and derive a closed-form expression for the ergodic secrecy rate. In particular, we reveal the impact of some key system parameters on the ergodic secrecy rate via asymptotic analysis with respect to a large number of antennas and a high transmit power at the base station. Then, to fully exploit the inter-user interference for security enhancement, we propose to optimize the transmit powers in the stages of channel estimation and multiple access. Finally, extensive simulation results validate the effectiveness of the proposed secure massive access scheme.

We consider a single-cell network with a hybrid full-/half-duplex base station. For the practical scenario with N channels, K uplink users, and M downlink users (max{ K , M } ≤ N ≤ K + M ), we tackle the issue of user admission and power control to simultaneously maximize the user admission number and minimize the total transmit power when guaranteeing the quality-of-service requirement of individual users. We formulate a 0–1 integer programming problem for the joint-user admission and power allocation problem. Because finding the optimal solution of this problem is NP-hard in general, a low-complexity algorithm is proposed by introducing the novel concept of adding dummy users. Simulation results show that the proposed algorithm achieves performance similar to that of branch and bound algorithm and significantly outperforms the random pairing algorithm.

We consider a multipair half-duplex relay communication network, where the relay is deployed with one-analog-to-digital converters and one-bit digital-to-analog converters. To suppress the interpair interference and quantization artifacts, we propose nonlinear precoding schemes to forward the quantized signals at the relay. We first present a technique based on gradient projection, and then show how to refine the solution using ordered quantization and perturbation methods. For the single-user case with BPSK symbols, we obtain a closed-form solution for the optimal transmit vector. Numerical results verify that the proposed precoding design significantly outperforms quantized linear precoding strategies.

To achieve spectral-efficient massive access in future wireless networks, this paper proposes a comprehensive fully non-orthogonal communication framework. First, we design a fully non-orthogonal communication scheme which consists of non-orthogonal channel estimation and non-orthogonal multiple access. Then, we analyze the performance of the proposed fully non-orthogonal communication, and derive a tight lower bound on the spectral efficiency in terms of key system parameters and channel conditions. Meanwhile, several novel insights are provided on spectral efficiency via asymptotic analysis in three important cases, i.e., a large number of base station (BS) antennas, a high BS transmit power, and perfect channel state information (CSI) at the BS. Finally, we optimize the performance of the proposed fully non-orthogonal communication and present two simple but efficient optimization algorithms for maximizing the weighted sum of spectral efficiency. Extensive simulation results validate the effectiveness of the proposed schemes.

In this paper, we consider a point-to-point multi-input multi-output wireless-powered communication system, where the source S is powered by a dedicated power beacon (PB) with multiple antennas. Employing the time splitting protocol, the energy constrained source S first harvests energy through the radio-frequency signals sent by the PB and then uses this energy to transmit information to the destination D. Unlike several prior works, we assume that the energy transfer link is subjected to Rician fading, which is a real fading environment, due to relatively short range power transfer distance and the existence of a strong line of sight path. We present a comprehensive analysis of the achievable ergodic capacity in two scenarios, depending on the availability of channel state information (CSI) at PB, namely, the absence of CSI and partial CSI. For the former case, equal power allocation is used, while for the later one, energy beamforming is used to enhance the energy transfer efficiency. For both the cases, closed-form expressions for the upper and lower bounds of the ergodic capacity are derived. Furthermore, the optimal time split is discussed, and the capacity in the low and high signal-to-noise ratio regimes is studied through simple closed-form expressions. Numerical results and simulations are provided to validate the theoretical analysis. The results show that the Rician factor K has a significant impact on the ergodic capacity performance, and this impact strongly depends on the availability of the CSI at the PB.

This paper considers a two-way half-duplex decode-and-forward relaying system, where multiple pairs of single-antenna users exchange information via a multiple-antenna relay. Assuming that the channel knowledge is nonideal and the relay employs maximum ratio processing, we derive a largescale approximation of the sum spectral efficiency (SE) that is tight when the number of relay antennas M becomes very large. Furthermore, we study how the transmit power scales with M to maintain a desired SE. In particular, three special powerscaling cases are discussed and the corresponding asymptotic SE is deduced with clear insights. Our elegant power-scaling laws reveal a tradeoff between the transmit powers of the user/relay and pilot symbol. Finally, we formulate a power allocation problem in terms of maximizing the sum SE and obtain a local optimum by solving a sequence of geometric programming problems.

Ambient backscatter communication is a newly emerged paradigm, which utilizes the ambient radio frequency signal as the carrier to reduce the system battery requirement, and is regarded as a promising solution for enabling large-scale deployment of future Internet of Things networks. The key issue of ambient backscatter communication systems is how to perform reliable detection. In this paper, we propose novel encoding methods at the information tag and devise the corresponding symbol detection methods at the reader. In particular, Manchester coding and differential Manchester coding are adopted at the information tag, and the corresponding semi-coherent Manchester (SeCoMC) and non-coherent Manchester (NoCoMC) detectors are developed. In addition, analytical bit-error-rate (BER) expressions are characterized for both detectors assuming either complex Gaussian or unknown deterministic ambient signal. Simulation results show that the BER performance of unknown deterministic ambient signal is better, and the SeCoMC detector outperforms the NoCoMC detector. Finally, compared with the prior detectors for ambient backscatter communications, the proposed detectors have the advantages of achieving superior BER performance with lower communication delay.

In this paper, we consider a power beacon (PB) assisted two-way relaying network, where two single antenna energy constrained users first harvest energy from a multi-antenna PB and then communicate with each other with the assistance of a relay. The key aim of the paper is to design the energy beamforming vector and time split parameter for optimizing the system performance. In particular, two different design objectives are investigated, namely, max-min rate design and sum rate maximization design. Due to the non-convex nature of the optimization problems, the global optimal solutions are difficult to obtain. Instead, we propose an alternating optimization design framework where near optimal performance can be achieved through iterative optimization. In addition, to further reduce the computation complexity, closed-form suboptimal designs are provided. Simulation results are presented to validate the effectiveness of the proposed alternating optimization design and suboptimal design. The outcomes of the paper suggest that adopting the proposed energy beamforming vector at the PB can substantially boost the system performance. Also, the topology of the network has a significant impact on the achievable performance.

We study the impact of using one-bit analog-to-digital and digital-to-analog converters in a multipair amplify-and-forward MIMO relaying system. The relay estimates the channel state information using training data, and then uses the channel estimate to perform maximum ratio combining and maximum ratio transmission. An exact achievable rate is derived for the system under general assumptions on the quantization noise, and then a closed-form asymptotic approximation is derived, which enables efficient evaluation of the impact of key parameters on system performance. Contrary to the conventional unquantized systems, the performance is seen to depend on the specific pilot sequences that are employed. In addition, the sum rate gap between the double quantized relay system and an ideal unquantized system is shown to be a factor of 4/π2in the low source power regime.

This paper investigates a power beacon (PB) assisted two-way relaying network, in which two single antenna energy constraint users first harvest energy from a multi-antenna PB and then communicate with each other with the assistance of a relay. Considering user fairness, we propose the max-min design that maximizes the minimum rate of the two users and investigate the corresponding optimal energy beamformer and time split. Due to the non-convex nature of the optimization problems, an alternating optimization design is proposed in which the optimal beamformer and time split can be solved via a low-complexity algorithm and convex optimization, respectively. Our results show that the proposed alternating optimization design yields near optimal performance, and implementing multiple antennas at the PB can significantly improve the system performance.

This paper considers a multipair amplify-and-forward massive MIMO relaying system with low-resolution ADCs at both the relay and destinations. The channel state information (CSI) at the relay is obtained via pilot training, which is then utilized to perform simple maximum-ratio combining/maximum-ratio transmission processing by the relay. Also, it is assumed that the destinations use statistical CSI to decode the transmitted signals. A closed-form approximation of the achievable sum rate is presented, which enables the efficient evaluation of the impact of key system parameters on the system performance. It is found that increasing the number of relay antennas is an effective method to compensate for the rate loss caused by coarse quantization. Moreover, it is desirable to deploy the low-resolution ADCs at the relay and high-resolution ADCs at the destination.

Massive Multiple-Input Multiple-Output (MIMO), small cell, and full-duplex are promising techniques for future 5G communication systems, where interference has become the most challenging issue to be coped with. In this paper, we provide an interference coordination framework for a two-tier heterogeneous network (HetNet) that consists of a massive-MIMO enabled macro-cell base station (MBS) and a number of full-duplex small-cell base stations (SBSs). To suppress the interferences and maximize the throughput, the full-duplex mode of each SBS, i.e., in-band or out-of-band, which has different impact on the interference pattern, should be carefully selected. To address this problem, a distributed graph color algorithm (DGCA) based on price is proposed. Numerical results demonstrate that DGCA significantly improves the system throughput.

The massive multiple-input multiple-output (MIMO) technique is recognized as the corner stone of the next generation mobile communication systems. However, its uplink-training downlink-transmission mechanism contains a severe security breach. If the uplink training sequence is artificially contaminated by some active attackers, the downlink beamforming can significantly deviate from the legitimate user and could be focused on the eavesdropper's location. This active pilot contamination attack (PCA) not only blocks the legitimate communication, but yields user information leakage. In this work, a random symbol based PCA detection method is proposed. The "secure region" is derived theoretically according to the statistical distribution of the detection statistics, which enables a more accurate PCA detection. The energy examination mechanism is also used to force the attacker sending the random symbols and exposing itself in the random symbol based attack detection. Finally, the theoretical analysis and simulation results demonstrate the superior performance of the proposed method.

This paper aims to provide a comprehensive solution for the design, analysis, and optimization of a multiple-antenna non-orthogonal multiple access (NOMA) system for multiuser downlink communication with both time duplex division and frequency duplex division modes. First, we design a new framework for multiple-antenna NOMA, including user clustering, channel state information (CSI) acquisition, superposition coding, transmit beamforming, and successive interference cancellation. Then, we analyze the performance of the considered system, and derive exact closed-form expressions for average transmission rates in terms of transmit power, CSI accuracy, transmission mode, and channel conditions. For further enhancing the system performance, we optimize three key parameters, i.e., transmit power, feedback bits, and transmission mode. Especially, we propose a low-complexity joint optimization scheme, so as to fully exploit the potential of multiple-antenna techniques in NOMA. Moreover, through asymptotic analysis, we reveal the impact of system parameters on average transmission rates, and hence present some guidelines on the design of multiple-antenna NOMA. Finally, simulation results validate our theoretical analysis, and show that a substantial performance gain can be obtained over traditional orthogonal multiple access technology under practical conditions.

This letter investigates the performance of a legitimate surveillance system, where a legitimate monitor aims to eavesdrop on a dubious decode-and-forward relaying communication link. In order to maximize the effective eavesdropping rate, two strategies are proposed, where the legitimate monitor adaptively acts as an eavesdropper, a jammer, or a helper. In addition, the corresponding optimal jamming beamformer and jamming power are presented. Numerical results demonstrate that the proposed strategies attain better performance compared with intuitive benchmark schemes. Moreover, it is revealed that the position of the legitimate monitor plays an important role on the eavesdropping performance for the two strategies.

With the fast development of vehicular technologies, the number of mobile terminals as well as their moving speeds has increased conspicuously, making it meaningful to re-examine the impact of mobility on communication performance of the existing cellular systems. In this paper, the uplink sum rate of a multiple-input multiple-output orthogonal frequency-division multiple access (MIMO-OFDMA) cellular system with nodes moving randomly at different speeds is studied. In such a multi-user environment, mobility not only causes inter sub-carrier interferences (ICI) among different users, but also brings the so-called channel aging which together with the above interferences incur outdated and inaccurate channel estimation and thus entail additional pilot overhead. Here, we first derive the relationship between the users' mobility and the ICI power, characterize the channel aging, and derive the overall multi-user interference caused by the outdated and inaccurate channel state information as a function of mobility. Then, by capitalizing on the above results, the sum rate of the MIMO-OFDMA uplink is theoretically analyzed. Moreover, an adaptive transmission scheme in which the users adjust the pilot percentage according to mobility statistics is proposed to maximize the sum rate. Finally, numerical results are provided to validate our analyses.

This paper studies the achievable secrecy performance of multiple-input multiple-output wiretap channels in the presence of imperfect channel state information (CSI) with practical low-complexity transmission schemes. In particular, we propose a general order transmit antenna selection and power-efficient output-threshold maximal ratio combining scheme. Two separate cases depending on the availability of the eavesdropper's CSI at the transmitter are considered. New closed-form expressions of the secrecy outage probability and the average secrecy rate are obtained. In addition, the secrecy diversity order and array gains, high signal-to-noise ratio slope, and power offset are characterized through asymptotic analysis, which enables the characterization of the effect of imprecise transmit antenna selection, output-threshold, and imperfect CSI on the secrecy performance. Numerical results are presented to validate the main outcomes of this paper.

This paper considers a multipair amplify-and-forward massive MIMO relaying system with low-resolution analog-to-digital converters (ADCs) at both the relay and destinations. The channel state information (CSI) at the relay is obtained via pilot training, which is then utilized to perform simple maximum-ratio combining/maximum-ratio transmission processing by the relay. Also, it is assumed that the destinations use statistical CSI to decode the transmitted signals. Exact and approximated closed-form expressions for the achievable sum rate are presented, which enable the efficient evaluation of the impact of key system parameters on the system performance. In addition, optimal relay power allocation scheme is studied, and power scaling law is characterized. It is found that, with only low-resolution ADCs at the relay, increasing the number of relay antennas is an effective method to compensate for the rate loss caused by coarse quantization. However, it becomes ineffective to handle the detrimental effect of low-resolution ADCs at the destination. Moreover, it is shown that deploying massive relay antenna arrays can still bring significant power savings, i.e., the transmit power of each source can be cut down proportional to 1/M to maintain a constant rate, where M is the number of relay antennas.

This paper investigates the impact of the channel state information (CSI) and antenna correlation at the multi-antenna relay on the performance of wireless powered dual-hop amplify-and-forward relaying systems. Depending on the available CSI at the relay, two different scenarios are considered, namely, instantaneous CSI and statistical CSI where the relay has access only to the antenna correlation matrix. Adopting the power-splitting architecture, we present a detailed performance study for both cases. Closed-form analytical expressions are derived for the outage probability and ergodic capacity. In addition, simple high signal-to-noise ratio (SNR) outage approximations are obtained. Our results show that, antenna correlation itself does not affect the achievable diversity order, the availability of CSI at the relay determines the achievable diversity order. Full diversity order can be achieved with instantaneous CSI, while only a diversity order of one can be achieved with statistical CSI. In addition, the transmit antenna correlation and receive antenna correlation exhibit different impacts on the ergodic capacity. Moreover, the impact of antenna correlation on the ergodic capacity also depends heavily on the available CSI and operating SNR.

In this paper, we consider a three-node cooperative wireless powered communication system consisting of a multi-antenna hybrid access point (H-AP) and a single-antenna relay and a single-antenna user. The energy constrained relay and user first harvest energy in the downlink and then the relay assists the user using the harvested power for information transmission in the uplink. The optimal energy beamforming vector and the time split between harvest and cooperation are investigated. To reduce the computational complexity, suboptimal designs are also studied, where closed-form expressions are derived for the energy beamforming vector and the time split. For comparison purposes, we also present a detailed performance analysis in terms of the achievable outage probability and the average throughput of an intuitive energy beamforming scheme, where the H-AP directs all the energy towards the user. The findings of the paper suggest that implementing multiple antennas at the H-AP can significantly improve the system performance, and the closed-form suboptimal energy beamforming vector and time split yields near optimal performance. Also, for the intuitive beamforming scheme, a diversity order of N+1/2 can be achieved, where N is the number of antennas at the H-AP.

To improve national security, government agencies have long been committed to enforcing powerful surveillance measures on suspicious individuals or communications. In this paper, we consider a wireless legitimate surveillance system, where a full-duplex multi-antenna legitimate monitor aims to eavesdrop on a dubious communication link between a suspicious pair via proactive jamming. Assuming that the legitimate monitor can successfully overhear the suspicious information only when its achievable data rate is no smaller than that of the suspicious receiver, the key objective is to maximize the eavesdropping non-outage probability by joint design of the jamming power, receive and transmit beamformers at the legitimate monitor. Depending on the number of receive/transmit antennas implemented, i.e., single-input single-output, single-input multiple-output, multiple-input single-output, and multiple-input multiple-output (MIMO), four different scenarios are investigated. For each scenario, the optimal jamming power is derived in a closed form and efficient algorithms are obtained for the optimal transmit/receive beamforming vectors. Moreover, low-complexity suboptimal beamforming schemes are proposed for the MIMO case. Our analytical findings demonstrate that by exploiting multiple antennas at the legitimate monitor, the eavesdropping non-outage probability can be significantly improved compared with the single-antenna case. In addition, the proposed suboptimal transmit zero-forcing scheme yields similar performance as the optimal scheme.

This paper investigates the impact of antenna correlation on wireless powered dual-hop multi-antenna relaying systems with instantaneous channel state information (CSI) or statistical CSI at the relay. Considering the power-splitting architecture, we study the outage probability as well as the achievable diversity order of the system for amplify-and-forward protocol. Our results show that, antenna correlation itself does not affect the achievable diversity order, the availability of CSI at the relay determines the achievable diversity order. Full diversity order can be achieved with instantaneous CSI, while only unit diversity order can be achieved with statistical CSI. In addition, with instantaneous CSI, antenna correlation is detrimental in the moderate and high SNR regime while it results in a better performance when the operating SNR is low. On the contrary, antenna correlation is always beneficial with only statistical CSI.

We consider a multi-pair two-way amplify-and-forward relaying system with a massive antenna array at the relay and estimated channel state information, assuming maximum-ratio combining/transmission processing. Closed-form approximations of the sum spectral efficiency are developed and simple analytical power scaling laws are presented, which reveal a fundamental trade-off between the transmit powers of each user/the relay and of each pilot symbol. Finally, the optimal power allocation problem is studied.

In future Internet of Things (IoT) networks, the explosive growth of mobile devices compel us to reconsider the effectiveness of the current frequency-division multiple access (FDMA) schemes. Devices' differentiated mobility features and diversified scattering environments make it more complicated to characterize the multi-user interference. In this paper, we thoroughly analyze the impacts of devices' mobility on the inter-sub-carrier interference (ICI) in an IoT system based on the 3GPP narrow-band orthogonal frequency-division multiple access (NB-OFDMA) protocol, and obtain the relationship between the system sum-rate and devices' mobility. Our results may shed some lights on the system design under the mobile scenarios.

In this paper, we investigate the secrecy performance of dual-hop amplify-and-forward (AF) multi-antenna relaying systems over Rayleigh fading channels by taking into account the direct link between the source and destination. To improve the secrecy performance, two linear processing schemes at relay and maximal ratio combining (MRC) at destination are proposed, namely, Zero-forcing/MRC (ZF/MRC) and Maximal ratio transmission/MRC (MRT/MRC). For these schemes, we present new tight analytical expressions of the secrecy outage probability. In addition, we examine the performance in high signal-to-noise ratio (SNR) regimes, and present simple secrecy outage approximations for all schemes. The results reveal that: 1) The MRT/MRC scheme achieves a full diversity order of M+1, while the ZF/MRC scheme achieves a diversity order ofM, where M is the number of antennas at relay. 2) The ZF/MRC scheme outperforms the MRT/MRC scheme in the low SNR regime, while becomes inferior to the MRT/MRC scheme in the high SNR regime.

In this paper, we analyze the secrecy performance of multi-antenna cognitive wiretap network, where the secondary transmitter (Alice) communicates with the secondary receiver (Bob) in the presence of an eavesdropper (Eve). Specifically, we investigate the cases of maximal-ratio combining (MRC) with half-duplex (HD) and selection combining/Zero forcing beamforming (SC/ZFB) scheme with full-duplex (FD) operation, respectively. Assuming the Rayleigh fading, closed-form expressions for the secrecy outage probability of cognitive wiretap channels with MRC and SC/ZFB are derived. Furthermore, we provide simple asymptotic approximations for the secrecy outage probability and find that both schemes achieve full diversity. In addition, simulation results reveal that MRC outperforms SC/ZBF in the low interference threshold regime, while the opposite holds in the high interference threshold regime.

This paper focuses on low complexity successive cancellation list (SCL) decoding of polar codes. In particular, using the fact that splitting may be unnecessary when the reliability of decoding the unfrozen bit is sufficiently high, a novel splitting rule is proposed. Based on this rule, it is conjectured that, if the correct path survives at some stage, it tends to survive till termination without splitting with high probability. On the other hand, the incorrect paths are more likely to split at the following stages. Motivated by these observations, a simple counter that counts the successive number of stages without splitting is introduced for each decoding path to facilitate the identification of correct and incorrect paths. Specifically, any path with counter value larger than a predefined threshold ω is deemed to be the correct path, which will survive at the decoding stage, while other paths with counter value smaller than the threshold will be pruned, thereby reducing the decoding complexity. Furthermore, it is proved that there exists a unique unfrozen bit uN-K1+1, after which the successive cancellation decoder achieves the same error performance as the maximum likelihood decoder if all the prior unfrozen bits are correctly decoded, which enables further complexity reduction. Simulation results demonstrate that the proposed low complexity SCL decoder attains performance similar to that of the conventional SCL decoder, while achieving substantial complexity reduction.

A novel low-complexity multiuser adaptive modulation (MAM) scheme is proposed for uplink massive multiple input and multiple output systems with zero-forcing detection, which requires only slow-varying large-scale shadowing information for the users. Closed-form expressions are derived for the average spectral efficiency (ASE) and bit error outage (BEO). In addition, for MAM with joint power control, optimal power adaptation strategy and switching thresholds are obtained. Compared with the conventional fast adaptive modulation, which requires fast-varying small-scale fading information, the proposed MAM scheme achieves similar ASE performance with slight increase in BEO.

This paper considers a wirelessly powered wiretap channel, where an energy constrained multi-antenna information source, powered by a dedicated power beacon, communicates with a legitimate user in the presence of a passive eavesdropper. Based on a simple time-switching protocol, where power transfer and information transmission are separated in time, we investigate two popular multi-antenna transmission schemes at the information source, namely, maximum ratio transmission and transmit antenna selection. Closed-form expressions are derived for the achievable secrecy outage probability and average secrecy rate for both schemes. In addition, simple approximations are obtained at the high signal-to-noise ratio (SNR) regime. Our results demonstrate that by exploiting the full knowledge of channel state information (CSI), we can achieve a better secrecy performance, e.g., with full CSI of the main channel, the system can achieve substantial secrecy diversity gain. On the other hand, without the CSI of the main channel, no diversity gain can be attained. Moreover, we show that the additional level of randomness induced by wireless power transfer does not affect the secrecy performance in the high SNR regime. Finally, our theoretical claims are validated by the numerical results.

We consider a full-duplex (FD) decode-and-forward system in which the time-switching protocol is employed by the multiantenna relay to receive energy from the source and transmit information to the destination. The instantaneous throughput is maximized by optimizing receive and transmit beamformers at the relay and the time-split parameter. We study both optimum and suboptimum schemes. The reformulated problem in the optimum scheme achieves closed-form solutions in terms of transmit beamformer for some scenarios. In other scenarios, the optimization problem is formulated as a semidefinite relaxation problem and a rank-one optimum solution is always guaranteed. In the suboptimum schemes, the beamformers are obtained using maximum ratio combining, zero-forcing, and maximum ratio transmission. When beamformers have closed-form solutions, the achievable instantaneous and delay-constrained throughput are analytically characterized. Our results reveal that beamforming increases both the energy harvesting and loop interference suppression capabilities at the FD relay. Moreover, simulation results demonstrate that the choice of the linear processing scheme as well as the time-split plays a critical role in determining the FD gains.

We investigate the secrecy performance of dual-hop amplify-and-forward multi-antenna relaying systems over Rayleigh fading channels, considering the direct link between the source and the destination. In order to exploit the available direct link and the multiple antennas for secrecy improvement, different linear processing schemes at the relay and different diversity combining techniques at the destination are proposed, namely: 1) zero-forcing/maximal ratio combining (ZF/MRC); 2) ZF/selection combining (ZF/SC); 3) maximal ratio transmission/MRC (MRT/MRC); and 4) MRT/SC. For all these schemes, we present new closed-form approximations for the secrecy outage probability. Moreover, we investigate a benchmark scheme, i.e., cooperative jamming/ZF (CJ/ZF), where the secrecy outage probability is obtained in exact closed-form. In addition, we present asymptotic secrecy outage expressions for all the proposed schemes in the high signal-to-noise ratio (SNR) regime, in order to characterize key design parameters, such as secrecy diversity order and secrecy array gain. The outcomes of this paper can be summarized as follows: 1) MRT/MRC and MRT/SC achieve a full diversity order of M + 1, ZF/MRC and ZF/SC achieve a diversity order of M, while CJ/ZF only achieves unit diversity order, where M is the number of antennas at the relay; 2) ZF/MRC (ZF/SC) outperforms the corresponding MRT/MRC(MRT/SC) in the low SNR regime, while becomes inferior to the corresponding MRT/MRC (MRT/SC) in the high SNR; and 3) all the proposed schemes tend to outperform the CJ/ZF with moderate number of antennas, and linear processing schemes with MRC attain better performance than those with SC.

This paper proposes a novel power beacon-assisted wiretap channel, where an energy constrained source, powered by a dedicated power beacon, aims to establish secure communications with a legitimate user in the presence of an eavesdropper. The power beacon and the source are assumed to have multiple antennas, while the legitimate user and the eavesdropper are equipped with a single antenna each. Considering a time-switching protocol, the power beacon first charges the source through wireless power transfer, and then acts as a friendly jammer to assist the source for secure communication. Depending on the available channel state information (CSI) at the power beacon, we propose several different jamming schemes for secrecy performance enhancement. Closed-form expressions are derived for the achievable secrecy outage probability of the proposed schemes. In addition, simple and accurate approximations are obtained at the high signal-to-noise ratio regime. The outcomes suggest that jamming is an effective approach to improve the secrecy performance: with full CSI available at the power beacon, employing simple zero-forcing scheme attains significant secrecy diversity gain and without the CSI of the eavesdropper link, jamming provides only some array gain. Moreover, we show that the proposed limited feedback-based random beamforming is an effective approach, which may outperform the antenna selection scheme. Furthermore, numerical results reveal that there exists a unique time switching ratio that maximizes the effective secrecy throughput.

In high mobility wireless communication scenarios, the most challenging issue is to cope with the extremely fast fading channel. Compared with its static counterpart, channel estimation in high mobility scenarios consumes excessive energy and spectrum to achieve similar performance. To address this issue, we exploit delay correlation with sequential antenna-hopping (AH) to convert the rapid fading channels to a virtual slow-fading channel. As a result, robust communication can be achieved with much less channel estimation overhead even under high mobility. Finally, two important performance metrics, namely, channel estimation mean square error (MSE) and transmission symbol error rate (SER), are re-examined for this virtual slow-fading channel. Numerical results verify the good performance of the proposed scheme and elucidate its effectiveness in high mobility communication scenarios.

We investigate the achievable sum rate and energy efficiency of zero-forcing precoded downlink massive multiple-input multiple-output systems in Ricean fading channels. A simple and accurate approximation of the average sum rate is presented, which is valid for a system with arbitrary rank channel means. Based on this expression, the optimal power allocation strategy maximizing the average sum rate is derived. Moreover, considering a general power consumption model, the energy efficiency of the system with rank-1 channel means is characterized. Specifically, the impact of key system parameters, such as the number of users N, the number of BS antennas M, Ricean factor K and the signal-to-noise ratio (SNR) ρ are studied, and closed-form expressions for the optimal ρ and M maximizing the energy efficiency are derived. Our findings show that the optimal power allocation scheme follows the water filling principle, and it can substantially enhance the average sum rate in the presence of strong line-of-sight effect in the low SNR regime. In addition, we demonstrate that the Ricean factor K has significant impact on the optimal values of M, N and ρ.

We consider a dual-hop full-duplex (FD) relaying system, where the energy constrained relay node is powered by radio frequency signals from the source using the time-switching architecture. Both the amplify-and-forward and decode-and-forward relaying protocols are studied. Specifically, we provide an analytical characterization of the achievable throughput of three different communication modes, namely, instantaneous transmission, delay-constrained transmission, and delay tolerant transmission. In addition, the optimal time split is studied for different transmission modes. Our results reveal that, when the time split is optimized, FD relaying could substantially boost the system throughput compared to the conventional half-duplex relaying architecture for all three transmission modes. In addition, it is shown that the instantaneous transmission mode has the highest throughput. However, compared to the delay tolerant transmission mode, the throughput gap is negligible. Unlike the instantaneous time split optimization which requires instantaneous channel state information, the optimal time split in the delay tolerant transmission mode depends only on the statistics of the channel, hence, is attractive for practical implementation.

This paper investigates the impact of the multiple antenna and co-channel interference (CCI) on the outage performance of a dual-hop amplify-and-forward energy harvesting relaying network. The energy constrained relay is powered by radio frequency signals and employs the power splitting receiver architecture. To exploit the benefit of multiple antennas, two different linear processing schemes are investigated, namely, Maximum ratio combining/maximal ratio transmission (MRC/ MRT) and Minimum mean-square error/MRT (MMSE/MRT). For both schemes, a new closed-form outage lower bound and a simple high signal-to-noise ratio outage approximation are derived, respectively. Also, the achievable diversity order is quantified. In addition, we study the optimal power splitting ratio which minimizes the outage probability. Our results show that, by increasing the energy harvesting capability, the implementation of multiple antennas significantly improves the systems performance. Moreover, CCI could be potentially exploited to boost the performance, while how much performance gain can be obtained depends on the choice of the linear processing scheme.

This paper investigates the achievable sum-rate of uplink massive multiple-input multiple-output (MIMO) systems considering a practical channel impairment, namely, aged channel state information (CSI). Taking into account both maximum ratio combining (MRC) and zero-forcing (ZF) receivers at the base station, we present tight closed-form lower bounds on the sum-rate for both receivers, which provide efficient means to evaluate the sum-rate of the system. More importantly, we characterize the impact of channel aging on the power scaling law. Specifically, we show that the transmit power of each user can be scaled down by 1/√(M), which indicates that aged CSI does not affect the power scaling law; instead, it causes only a reduction on the sum rate by reducing the effective signal-to-interference-and-noise ratio (SINR).

This paper studies the capacity scaling law of the multi-pair relay network with K source-destination pairs and M relays, where each node is equipped with a single antenna and works in half duplex mode. With the conventional two-slot relaying, the capacity was found to scale as K/2 log (M)+O(1) for fixed K and M → ∞. This paper shows that the capacity scaling law can be further improved to K log (M)+O(1) with successive relaying, as if the relays were full duplex. This scaling law can be achieved by a distributed coherent amplify-and-forward scheme, which only requires local channel state information (CSI) at each relay and statistical CSI at the sources and destinations.

The distribution of information bits and frozen bits on the decoder graph can be exploited to simplify the successive‐cancellation (SC) decoding of polar codes. In this study, the authors establish a general simplified SC decoding framework for polar codes with arbitrary block‐length, which is independent of any specific or explicit generator matrices and considerably reduces decoding complexity while retaining the same error performance. Then, based on the fact that the complexity reduction depends on the distribution of information bits, a so‐called m ‐radius reselection scheme is proposed to construct multiple feasible information sets which are different from the original one defined by Arıkan. In this way, flexible tradeoffs between performance and complexity could be achieved.

In this study, the authors investigate the Gallager's error exponents of dual‐hop amplify‐and‐forward systems over generalised η ‐ μ and κ ‐ μ fading channels, two versatile channel models which encompass a number of popular fading channels such as Rayleigh, Rician, Nakagami‐ m , Hoyt and one‐sided Gaussian fading channels. The authors present new analytical expressions for the probability density function of the end‐to‐end signal‐to‐noise‐ratio (SNR) of the system. These analytical expressions are then applied to analyse the system performance through the study of Gallager's exponents, which are classical tight bounds of error exponents and present the tradeoff between practical information rate and the reliability of communication. Two types of Gallager's exponents, namely, random coding error exponent and expurgated error exponent, are studied. Based on the newly derived analytical expressions, the authors provide an efficient method to compute the required codeword length to achieve a predefined upper bound of error probability. In addition, the analytical expressions are derived for the cutoff rate and ergodic capacity of the system. Moreover, simplified expressions are presented at the high SNR regime. All the analytical results are verified via Monte–Carlo simulations.

This letter maximizes the achievable throughput of a relay-assisted wirelessly powered communications system, where an energy constrained source, assisted by an energy constrained relay and both powered by a dedicated power beacon (PB), communicates with a destination. Considering the time splitting approach, the source and relay first harvest energy from the PB, which is equipped with multiple antennas, and then transmits the information to destination. Simple closed-form expressions are derived for the optimal PB energy beamforming vector and time split for energy harvesting and information transmission. Numerical results and simulations demonstrate the superior performance compared with some intuitive benchmark beamforming scheme. Also, it is found that placing the relay at the middle of the source-destination path is no longer optimal.

In this paper, we present a comprehensive investigation on the secrecy performance of opportunistic relay selection systems employing the decode-and-forward protocol over Rayleigh fading channels. Considering a practical setting where direct link between the source node (Alice) and the destination node (Bob) is available, we study the secrecy performance of three different diversity combining schemes, namely, maximum ratio combining (MRC), distributed selection combining (DSC), and distributed switch-and-stay combining (DSSC). Throughout the analysis, we consider two different scenarios based on the availability of the eavesdropper's channel state information (CSI), i.e., Scenario A, where the eavesdropper's CSI is not available at Alice and the relay, and Scenario B, where Alice and the relay have knowledge about the eavesdropper's CSI. For Scenario A, we derive exact closed-form expressions for secrecy outage probability and simple asymptotic approximations for the secrecy outage probability, which enable the characterization of the achievable secrecy diversity order and coding gains. For Scenario B, we derive closed-form expressions for the achievable secrecy rates. For both scenarios, we investigate the impact of feedback delay (outdated CSI) on the secrecy performance wherein exact and asymptotic secrecy outage probability and closed-form expressions of the secrecy achievable rates are obtained. Our analytical findings suggest that both the MRC and DSC schemes achieve the maximum diversity order of K+1 where K is the number of relays. In addition, the feedback delay has a significant impact on the achievable secrecy performance by reducing the achievable diversity order to two.

This paper investigates the achievable sum-rate of massive multiple-input multiple-output (MIMO) systems in the presence of channel aging. For the uplink, by assuming that the base station (BS) deploys maximum ratio combining (MRC) or zero-forcing (ZF) receivers, we present tight closed-form lower bounds on the achievable sum-rate for both receivers with aged channel state information (CSI). In addition, the benefit of implementing channel prediction methods on the sum-rate is examined, and closed-form sum-rate lower bounds are derived. Moreover, the impact of channel aging and channel prediction on the power scaling law is characterized. Extension to the downlink scenario and multicell scenario is also considered. It is found that, for a system with/without channel prediction, the transmit power of each user can be scaled down at most by 1/√M (where M is the number of BS antennas), which indicates that aged CSI does not degrade the power scaling law, and channel prediction does not enhance the power scaling law; instead, these phenomena affect the achievable sum-rate by degrading or enhancing the effective signal to interference and noise ratio, respectively.

Provisioning reliable wireless services for railway passengers is becoming an increasingly critical problem to be addressed with the fast development of high speed trains (HST). In this paper, exploiting the linear mobility inherent to the HST communication scenario, we discover a new type of spatial-temporal correlation between the base station and moving antenna array on the roof top of the train. Capitalizing on the new spatial-temporal correlation structure and properties, an improved differential space-time modulation (DSTM) scheme is proposed. Analytical expressions are obtained for the pairwise error probability of the system. It is demonstrated that the proposed approach achieves superior error performance compared with the conventional DSTM scheme. In addition, an adaptive method, which dynamically adjusts the transmission block length is proposed to further enhance the system performance. Numerical results are provided to verify the performance of the proposed schemes.

This paper investigates the average throughput of a wireless-powered communications system, where an energy constrained source, powered by a dedicated power beacon (PB), communicates with a destination. It is assumed that the PB is capable of performing channel estimation, digital beamforming, and spectrum sensing as a communication device. Considering a time-splitting approach, the source first harvests energy from the PB equipped with multiple antennas, and then transmits information to the destination. Assuming Nakagami-m fading channels, analytical expressions for the average throughput are derived for two different transmission modes, namely, delay tolerant and delay intolerant. In addition, closed-form solutions for the optimal time split, which maximize the average throughput are obtained in some special cases, i.e., high-transmit power regime and large number of antennas. Finally, the impact of cochannel interference is studied. Numerical and simulation results have shown that increasing the number of transmit antennas at the PB is an effective tool to improve the average throughput and the interference can be potentially exploited to enhance the average throughput, since it can be utilized as an extra source of energy. Also, the impact of fading severity level of the energy transfer link on the average throughput is not significant, especially if the number of PB antennas is large. Finally, it is observed that the source position has a great impact on the average throughput.

In this paper, an energy harvesting dual-hop relaying system without/with the presence of co-channel interference (CCI) is investigated. Specifically, the energy constrained multi-antenna relay node is powered by either the information signal of the source or via the signal receiving from both the source and interferer. In particular, we first study the outage probability and ergodic capacity of an interference free system, and then extend the analysis to an interfering environment. To exploit the benefit of multiple antennas, three different linear processing schemes are investigated, namely, 1) Maximum ratio combining/maximum ratio transmission (MRC/MRT), 2) Zero-forcing/MRT (ZF/MRT) and 3) Minimum mean-square error/MRT (MMSE/MRT). For all schemes, both the systems outage probability and ergodic capacity are studied, and the achievable diversity order is also presented. In addition, the optimal power splitting ratio minimizing the outage probability is characterized. Our results show that the implementation of multiple antennas increases the energy harvesting capability, hence, significantly improves the systems performance.

This paper investigates the impact of different linear processing techniques on the outage performance of dual-hop amplify-and-forward (AF) relaying systems with co-channel interference (CCI) at the multiple antenna relay. Specifically, three heuristic linear precoding schemes are proposed to combat the detrimental effect of CCI, namely, 1) Maximum ratio combining/maximal ratio transmission (MRC/MRT), 2) Zero-forcing/MRT (ZF/MRT), 3) Minimum mean-square error/MRT (MMSE/MRT). New exact outage expressions as well as simple high signal-to-noise ratio (SNR) outage approximations are derived for all three schemes. Our results demonstrate that while the MRC/MRT and the MMSE/MRT schemes achieve a full diversity order of N, the ZF/MRT scheme can only achieve a diversity order of N-M, where N is the number of relay antennas and M is the number of interferers. In addition, it is observed that the MMSE/MRT scheme always achieves the best outage performance. The ZF/MRT scheme outperforms the MRC/MRT scheme in the low SNR regime, and exhibits an inferior performance compared to the MRC/MRT scheme in the high SNR regime.

This paper presents a comprehensive performance analysis of multiuser multiple antenna amplify-and-forward relaying networks employing opportunistic scheduling with feedback delay and co-channel interference over Rayleigh fading channels. Specifically, we derive exact as well as approximate closed-form expressions for the outage probability and average symbol error rate (SER) of the system. In addition, simple asymptotic expressions at the high signal-to-noise ratio (SNR) regime are obtained, which facilitate the characterization of the achievable diversity order and coding gain of the system. Moreover, two novel ergodic capacity bounds valid for general systems with arbitrary number of antennas and users are proposed. Finally, the optimum power allocation scheme in terms of minimizing the average SER is studied, and simple analytical solutions are obtained. Simulation results are provided to corroborate the derived analytical expressions, and it is demonstrated that the ergodic capacity bounds remain sufficiently tight across the entire range of SNRs and the proposed power allocation scheme offers significant improvements on the SER performance. The findings of the paper suggest that the full diversity order can only be achieved when there is ideal feedback, i.e., no feedback delay, and the diversity order always reduces to one in the presence of feedback delay. Also, the impact of key parameters such as the number of antennas and users on the system performance is intimately dependent on the level of feedback delay.

In this paper, we propose transmit antenna selection with maximal ratio combining (TAS/MRC) in dual-hop decode-and-forward spectrum-sharing relaying networks with the primary user's interference and outdated channel state information (CSI). In this network, a single antenna that maximizes the received SNR is selected at the secondary transmitter, and the MRC is adopted at the secondary receiver. To efficiently evaluate the impact of key parameters on the system performance, we derive the exact analytical expression for the outage probability of the secondary network in a Rayleigh fading channel. Moreover, we present simple asymptotic expressions for the outage probability in a high SNR regime, which reveal practical insights on the achievable diversity order and coding gain. The findings suggest that whether the outdated CSI concerning the secondary transmission links has significant impact on the outage probability of the system depends on the interference power constraint at primary receivers. Specifically, under the proportional interference power constraint, the achievable diversity order is affected by imperfect CSI regarding the secondary transmission links, and the diversity-multiplexing tradeoff is independent of the primary network. However, under the fixed interference power constraint, the error floor is displayed, and the achievable diversity order reduces to zero regardless of the CSI concerning the secondary transmission links.

We consider a dual-hop full-duplex relaying system, where the energy constrained relay node is powered by radio frequency signals from the source using the time-switching architecture, both the amplify-and-forward and decode-and-forward relaying protocols are studied. Specifically, we provide an analytical characterization of the achievable throughput of three different communication modes, namely, instantaneous transmission, delay-constrained transmission, and delay tolerant transmission. In addition, the optimal time split is studied for different transmission modes. Our results reveal that, when the time split is optimized, the full-duplex relaying could substantially boost the system throughput compared to the conventional half-duplex relaying architecture for all three transmission modes. In addition, it is shown that the instantaneous transmission mode attains the highest throughput. However, compared to the delay-constrained transmission mode, the throughput gap is rather small. Unlike the instantaneous time split optimization which requires instantaneous channel state information, the optimal time split in the delay-constrained transmission mode depends only on the statistics of the channel, hence, is suitable for practical implementations.

In this paper, we analyze the ergodic capacity of a dual-hop amplify-and-forward relaying system, where the relay is equipped with multiple antennas and subject to co-channel interference and the additive white Gaussian noise. Specifically, we consider three heuristic precoding schemes, where the relay first applies the: 1) maximal-ratio combining (MRC); 2) zero-forcing (ZF); and 3) minimum mean-squared error (MMSE) principle to combine the signal from the source, and then steers the transformed signal toward the destination with the maximum ratio transmission (MRT) technique. For the MRC/MRT and MMSE/MRT schemes, we present new tight analytical upper and lower bounds for the ergodic capacity, while for the ZF/MRT scheme, we derive a new exact analytical ergodic capacity expression. Moreover, we make a comparison among all three schemes, and our results reveal that, in terms of the ergodic capacity performance, the MMSE/MRT scheme always has the best performance and the ZF/MRT scheme is slightly inferior, while the MRC/MRT scheme is always the worst one. Finally, the asymptotic behavior of ergodic capacity for the three proposed schemes are characterized in large N scenario, where N is the number of relay antennas. Our results reveal that, in the large N regime, both the ZF/MRT and MMSE/MRT schemes have perfect interference cancellation capability, which is not possible with the MRC/MRT scheme.

This paper presents an analytical investigation on the performance of Rayleigh-product MIMO channels with linear minimum mean-square-error (MMSE) or zero-forcing (ZF) receivers. For MMSE receivers, exact closed-form expressions for the ergodic sum-rate of the system are derived. In addition, simplified expressions are obtained for the key parameters dictating the sum-rate performance of the system in the high signal-to-noise ratio (SNR) regime (i.e., high SNR slope and power offset) and low SNR regime (i.e., minimum energy per information bit required to convey any positive rate and the wideband slope). While for ZF receivers, tight closed-form upper and lower bounds for the ergodic sum-rate of the system are derived. It is analytically proven that the ZF and MMSE receivers achieve the same sum rate performance in the high SNR regime. Moreover, for both MMSE and ZF receivers, the achievable diversity-multiplexing tradeoff (DMT) of Rayleigh-product MIMO channels is characterized. The findings suggest that a larger number of scatterers will improve the the performance of Rayleigh-product MIMO channels with linear receivers, and the ZF receivers achieve the same performance as the MMSE receivers in Rayleigh-product MIMO channels in the high SNR regime. Moreover, it is demonstrated that as long as the number of the scatterers is greater than the number of receive antennas, linear receivers achieve the optimal DMT.

This paper considers a dual-hop amplify-and-forward (AF) relaying system where the relay is equipped with multiple antennas, while the source and the destination are equipped with a single antenna. Assuming that the relay is subjected to co-channel interference (CCI) and additive white Gaussian noise (AWGN) while the destination is corrupted by AWGN only, we propose three heuristic relay precoding schemes to combat the CCI, namely, 1) Maximum ratio combining/maximal ratio transmission (MRC/MRT), 2) Zero-forcing/MRT (ZF/MRT), 3) Minimum mean-square error/MRT (MMSE/MRT). We derive new exact outage expressions as well as simple high signal-to-noise ratio (SNR) outage approximations for all three schemes. Our findings suggest that both the MRC/MRT and the MMSE/MRT schemes achieve a full diversity of N, while the ZF/MRT scheme achieves a diversity order of N-M, where N is the number of relay antennas and M is the number of interferers. In addition, we show that the MMSE/MRT scheme always achieves the best outage performance, and the ZF/MRT scheme outperforms the MRC/MRT scheme in the low SNR regime, while becomes inferior to the MRC/MRT scheme in the high SNR regime. Finally, in the large N regime, we show that both the ZF/MRT and MMSE/MRT schemes are capable of completely eliminating the CCI, while perfect interference cancelation is not possible with the MRC/MRT scheme.

In this paper, we investigate the ergodic sum rate of K fading MIMO channels with linear MMSE receiver, which is an analytically friendly fading model to describe both the small scale fading and shadowing. The exact closed-form expression of the ergodic sum rate was derived, which is applicable for arbitrary number of transmit and receive antennas and any signal to noise ratio (SNR) range. Moreover, simple expressions were derived for the ergodic sum rate in the high and low SNR regimes. The analytical results enable us to gain insights on how the key system parameters affect the ergodic sum rate of the system. Numerical results are presented and verified via Monte Carlo simulations.

In this paper, we analyze the performance of a dual-hop system with partial relay selection and feedback delay over Nakagami-m fading channels in the presence of multiple identical interferers at the destination. Based on the new closed-form expressions for the cumulative distribution function of the effective signal-to-interference plus noise ratio, we present closed-form expressions for the outage probability and average symbol error rate (SER) for both fixed and CSI-assisted gain relaying systems. To gain further insights, the asymptotic outage probability and average symbol error rates at the high signal to noise ratio regimes are examined. Our results reveal that increasing the number of relays does not provide any extra diversity gain due to the presence of the feedback delay. Specifically, we show that feedback delay as well as CCI only affect the coding gain of the system. The accuracy of the analytical results are supported Monte-Carlo simulations.

The finite-SNR Diversity-Multiplexing Tradeoff (DMT) problem is studied in the context of spectrum aggregation, where there exist multiple noncontiguous (discrete) spectrum segments each of which has its own power limit. Firstly, we give an optimal power allocation strategy to achieve the ergodic capacity under Rayleigh fading. Secondly, two different coding schemes, i.e., coding without across sub-channels and coding across, are presented to achieve the lower and upper bound of diversity gain of this system under the same multiplexing gain. Then the finite-SNR DMT under i.i.d Rayleigh fading is derived, and its accurate expression for the coding-without-across scheme and the upper bound for the coding-across scheme are achieved respectively. The asymptotic performances of both schemes are also obtained. Under the equal sub-channel bandwidth setting, for infinite SNR, the coding-across scheme achieves M (the number of sub-channels) times diversity gain against the without-coding-across scheme. The loss of diversity gain caused by non-identical bandwidth and power limit is also analyzed.

The performance of single-cell distributed multiple-input multiple-output (D-MIMO) systems is not only affected by small-scale Rayleigh fading but also from large-scale fading and path-loss. In this paper, we elaborate on the sum rate of D-MIMO systems employing linear zero-forcing receivers, accounting for both large and small-scale fading effects, as well as spatial correlation at the transmit side. In particular, we consider the classical lognormal model and propose closed-form upper and lower bounds on the achievable sum rate. Using these bounds as a starting point, we pursue a "large-system" analysis and provide asymptotic expressions when the number of antennas at the base station (BS) grow large, and when the number of antennas at both ends grow large with a fixed and finite ratio. A detailed characterization in the asymptotically high and low signal to noise ratio regimes is also provided. An interesting observation from our results is that in order to maximize the sum rate, the RPs should be placed at unequal distances to the BS when they experience the same level of shadowing. The resulting closed-form expressions are compared with the corresponding results on MIMO optimal receivers.

This paper presents an analytical investigation on the outage performance of dual-hop multiple antenna amplify-and-forward relaying systems in the presence of interference. For both the fixed-gain and variable-gain relaying schemes, exact analytical expressions for the outage probability of the systems are derived. Moreover, simple outage probability approximations at the high signal-to-noise-ratio regime are provided, and the diversity order achieved by the systems are characterized. Our results suggest that variable-gain relaying systems always outperform the corresponding fixed-gain relaying systems. In addition, the fixed-gain relaying schemes only achieve diversity order of one, while the achievable diversity order of the variable-gain relaying scheme depends on the location of the multiple antennas.

In this paper, we pursue a performance evaluation of distributed multiple-input multiple-output (MIMO) systems in composite Rayleigh/Inverse-Gaussian fading channels. Capitalizing on some generic bounding techniques, we first derive new closed-form bounds on the ergodic capacity of optimal receivers. In order to gain useful insights into the impact of fading parameters on optimal receivers' performance, a detailed characterization in the asymptotically high and low signal-to-noise ratio regimes is also provided. In addition, we explore the “large-system” regime and provide asymptotic expressions when the number of antennas grows large. A similar performance analysis is performed for the achievable sum rate of distributed MIMO systems employing linear minimum mean-square error receivers.

This paper presents a detailed sum rate characterization of distributed multiple-input multiple-output systems operating over composite fading channels and employing linear zero-forcing receivers. We consider the Rayleigh/Lognormal fading model and also take into account the effects of path-loss and spatial correlation at the transmit side. New closed-form upper and lower bounds on the achievable sum rate are proposed that apply for arbitrary numbers of antennas. Moreover, we investigate the concept of large-scale multiple-antenna systems when the number of receive antennas grow large. In this asymptotic regime, it is shown that the effects of Rayleigh fading are averaged out and the channel is dominated by the much more slowly varying shadowing. An interesting observation from our results is that in order to maximize capacity, the radio ports should be placed at unequal distances to the base station when they experience the same level of shadowing.

Along with the channel capacity, the error exponent is one of the most important information theoretic measures of reliability, as it sets ultimate bounds on the performance of communication systems employing codes of finite complexity. In this paper, we derive the closed-form expressions for the Gallager's random coding and expurgated error exponents for Rayleigh fading product multiple-input multiple-output (MIMO) channels under the assumption that there is no channel-state information (CSI) at the transmitter and perfect CSI at the receiver. From the derived analytical expressions, we get insight into an elementary tradeoff between the communication reliability and information rate for the Rayleigh fading product MIMO channels. Moreover, we can easily compute the necessary codeword length without the extensive Monte-carlo simulation to achieve predefined error probability at a given rate below the channel capacity. In addition, we derive the exact closed-form expressions for the ergodic capacity and cutoff rate based on easily computable Meijer G-function. The closed-form expressions for the error exponents, ergodic capacity and cutoff rate have also been derived for Rayleigh fading keyhole MIMO channels as the example of special case.

In this paper, we study the error exponent of generalized-K fading multiple-input multiple-output (MIMO) channels over Orthogonal Space-Time Block Codes (OSTBC). Error exponent sets ultimate bounds on the performance of communication systems employing codes of finite complexity and has been seen as one of the most significant information theoretic measures of reliability. In this paper, closed-form expressions of Gallager's random coding and expurgated error exponents for generalized-K fading MIMO channels over OSTBC are derived, assuming that there is no channel-state information (CSI) at the transmitter and perfect CSI at the receiver. Based on which, we gain valuable insight into the fundamental tradeoff between the communication reliability and information rate of the channel. The necessary codeword length to achieve predefined error probability at a given rate below the capacity of channel is identified. Moreover, we derive closed-form expressions for the cutoff rate, critical rate and expurgation rate.

This paper presents an analytical investigation on the outage performance of dual-hop multiple antenna fixed-gain amplify-and-forward relay systems in the presence of co-channel interference. Exact closed-form analytical expressions for the outage probability of the systems are derived. Moreover, simple outage probability approximations at the high signal to noise ratio regime are provided, and the diversity order achieved by the systems are characterized. Our results suggests that the fixed-gain AF relaying schemes only achieves diversity order of one. Moreover, it is beneficial to put the multiple antennas at the source or destination node rather than at the relay node.

In this study, the authors derive the effective capacity limits for multiple antenna channels which quantify the maximum achievable rate with consideration of link-layer delay-bound violation probability. Both correlated multiple-input single-output and multiple-input multiple-output keyhole channels are studied. Based on the closed-form exact expressions for the effective capacity of both channels, the authors look into the asymptotic high and low signal-to-noise ratio regimes, and derive simple expressions to gain more insights. The impact of spatial correlation on effective capacity is also characterised with the aid of a majorisation theory result. It is revealed that antenna correlation reduces the effective capacity of the channels and a stringent quality-of-service requirement causes a severe reduction in the effective capacity but can be alleviated by increasing the number of antennas.

This paper investigates the performance of optimal single stream beamforming schemes in multiple-input multiple-output (MIMO) dual-hop amplify-and-forward (AF) systems. Assuming channel state information is not available at the source and relay, the optimal transmit and receive beamforming vectors are computed at the destination, and the transmit beamforming vector is sent to the transmitter via a dedicated feedback link. Then, a set of new closed-form expressions for the statistical properties of the maximum eigenvalue of the resultant channel is derived, i.e., the cumulative density function (cdf), probability density function (pdf) and general moments, as well as the first order asymptotic expansion and asymptotic large dimension approximations. These analytical expressions are then applied to study three important performance metrics of the system, i.e., outage probability, average symbol error rate and ergodic capacity. In addition, more detailed treatments are provided for some important special cases, e.g., when the number of antennas at one of the nodes is one or large, simple and insightful expressions for the key parameters such as diversity order and array gain of the system are derived. With the analytical results, the joint impact of source, relay and destination antenna numbers on the system performance is addressed, and the performance of optimal beamforming schemes and orthogonal space-time block-coding (OSTBC) schemes are compared. Results reveal that the number of antennas at the relay has a great impact on how the numbers of antennas at the source and destination contribute to the system performance, and optimal beamforming not only achieves the same maximum diversity order as OSTBC, but also provides significant power gains over OSTBC.

This letter presents an analytical performance investigation on the cognitive broadcast channels (BC) with user selection. Exact closed-form expressions for the outage probability and multiuser interference diversity gain of the cognitive BC are derived. In addition, closed-form upper and lower bounds for the ergodic capacity of the system are presented. These analytical results not only provide fast and efficient means to evaluate the performance of the system, they also enable us to gain valuable insights on the impact of key parameters such as peak transmit power, interference temperature constraint, primary transmit power and channel fading parameters on the system performance.

We investigate the ergodic sum rate of multiple-input multiple-output Nakagami-m fading channels with linear receivers. In particular, both mean square error and zero-forcing receivers are considered. For dual transmit antenna configurations, we present new, closed-form upper bounds on the ergodic sum rate of both receivers. Moreover, we derive exact expressions for the two key parameters dictating the sum rate behavior in the low signal to noise ratio regime, namely the minimum energy per information bit to reliably convey any positive rate and the wideband slope. By doing so, we are able to explicitly demonstrate the sub-optimality of linear receivers compared to optimal receivers, and draw useful insights into the impact of model parameters (e.g., number of antennas, fading parameters).

In this paper, we analyze the performance of a dual- hop system with partial relay selection and feedback delay over Rayleigh fading channels in the presence of multiple identical interferers at the destination. Based on the new closed-form expressions for the cumulative distribution function of the effective signal-to-interference plus noise ratio, we present closed-form expressions for the outage probability and average symbol error rates for both fixed and CSI-assisted gain relaying systems. To gain further insights, the asymptotic outage probability and average symbol error rates at the high signal to noise ratio regimes are examined. Monte-Carlo simulation results confirm the accuracy of the our analytical results.

In this paper, we introduce two novel upper bounds on the achievable sum rate of multiple-input multiple-output (MIMO) systems with Zero-Forcing (ZF) receivers. The presented bounds are given in tractable closed-form and apply for different fading models, like uncorrelated/doubly correlated Rayleigh fading and Ricean fading. In addition, the first bound establishes an interesting relationship between the sum rate and the first negative moment of the unordered eigenvalue of the instantaneous correlation matrix. Based on our analytical expressions, we are able to explore the impact of the model parameters, such as number of antennas, spatial correlation and Ricean-K factor, on the sum rate of MIMO ZF receivers.

In this paper, we study the eigenvalue-based spectrum sensing techniques for multiple-antenna cognitive radio networks. First, we study the extreme eigenvalue distributions of a complex Wishart matrix and then, in contrast to the asymptotic analysis reported in the literature, we derive the exact distribution of the test statistics of (i) maximum eigenvalue detector (MED) (ii) maximum-minimum eigenvalue (MME) detector and (iii) energy with minimum eigenvalue (EME) detector for finite number of samples (n) and finite number of antennas (m). These distributions are represented by complex hypergeometric functions of matrix argument, which can be expressed in terms of complex zonal polynomials. We also describe the method to compute these complex hypergeometric functions. Based on these exact distribution of the test statistics we find the exact decision thresholds as a function of the desired probability of false-alarms for MED, MME and EME. Simulation results show superior performance compared to the decision thresholds obtained from asymptotic (i.e, n,m → ∞) distributions.

This paper presents an analytical investigation on the performance of optimal beamforming scheme in multiple-input multiple-output dual-hop amplify-and-forward systems. We first derive a set of new closed-form expressions for the statistical properties of the resultant channel matrix, including the cumulative distribution function and probability density function of the maximum eigenvalue, as well as their first order asymptotic expansions. These analytical expressions are then applied to derive some important performance metrics of the system, i.e., outage probability, diversity order, array gain and ergodic capacity. Numerical simulations are carried out to validate the correctness of the analytical results.

In this paper, we investigate the performance of a dual-hop amplify-and-forward relay system in Nakagami-\emph{m} fading channels in the presence of multiple interferers. Based on the new closed-form expression for the cumulative distribution function of a new type of random variable involving a number of independent gamma random variables, we present closed-form expressions for the outage probability, general moments for the end-to-end signal to interference and noise ratio and ergodic capacity of the system. In addition, we look into the high signal to noise ratio regime and characterize the diversity order and coding gain achieved by the system. Our result shows that the diversity of the system is limited by the hop experiencing more severer fading. Moreover, the interference does not reduce the diversity order of the system, instead, it degrades the outage performance by affecting the coding gain of the system.

This paper presents an analytical performance investigation of the capacity limits of correlated multiple-input single-output (MISO) channels in the presence of quality-of-service (QoS) requirements. Exact closed-form expression for the effective capacity of correlated MISO channels is derived. In addition, simple expressions are obtained at the asymptotic high and low signal-to-noise ratio (SNR) regimes, which provide insights into the impact of various system parameters on the effective capacity of the system. Also, a complete characterization of the impact of spatial correlation on the effective capacity is provided with the aid of a majorization theory result. The findings suggest that antenna correlation reduce the effective capacity of the channels. Moreover, a stringent QoS requirement causes a significant reduction in the effective capacity but this can be effectively alleviated by increasing the number of antennas.

In this paper, we analyze the asymptotic outage probability performance of both decode-and-forward (DF) and amplify-and-forward (AF) relaying systems using partial relay selection and the best relay selection schemes for Nakagami-m fading channels. We derive their respective outage probability expressions in the asymptotic high signal-to-noise ratio (SNR) regime, from which the diversity order and coding gain are analyzed. In addition, we investigate the impact of power allocation between the source and relay terminals and derive the diversity-multiplexing tradeoff (DMT) for these relay selection systems. The theoretical findings suggest that partial relay selection can improve the diversity of the system and can achieve the same DMT as the best relay selection scheme under certain conditions.

This paper investigates the achievable sum rate of Rayleigh product MIMO channels with linear MMSE receiver. Exact closed-form expressions are derived, which provide an efficient means to evaluate the achievable sum rate of the system with arbitrary number of antennas and scatters. In addition, simple expressions were obtained for the key parameters dictating the sum rate performance of the system at the high and low signal to noise ratio regime, which enables us to gain insights on how system parameters such as the number of scatters affect the achievable sum rate of the system. Our analytical results confirm the intuition that increasing the number of scatters improves the achievable sum rate of the system.

In this letter, we investigate the performance of dual-hop channel state information-assisted amplify-and-forward relaying systems over Nakagami-mfading channels in the presence of multiple interferers at the relay. Assuming integer fading parameterm, we derive closed-form expressions for the exact outage probability and accurate approximation for symbol error rate of the system. Furthermore, we look into the asymptotical high signal to noise ratio regime, and characterize the diversity order achieved by the system. All the analytical results are validated via Monte Carlo simulations.

This paper studies the Demmel condition number of Wishart matrices, a quantity which has numerous applications to wireless communications, such as adaptive switching between beamforming and diversity coding, link adaptation, and spectrum sensing. For complex Wishart matrices, we give an exact analytical expression for the probability density function (p.d.f.) of the Demmel condition number, and also derive simplified expressions for the high tail regime. These results indicate that the condition of complex Wishart matrices is dominantly decided by the difference between the matrix dimension and degree of freedom (DoF), i.e., the probability of drawing a highly ill conditioned matrix decreases considerably when the difference between the matrix dimension and DoF increases. We further investigate real Wishart matrices, and derive new expressions for the p.d.f. of the smallest eigenvalue, when the difference between the matrix dimension and DoF is odd. Based on these results, we succeed to obtain an exact p.d.f. expression for the Demmel condition number, and simplified expressions for the high tail regime.

We use non-asymptotic random matrix theory to obtain new expressions for certain statistical properties of spatially uncorrelated multi-keyhole multiple-input multiple-output (MIMO) channels. These include closed-form expressions for the distribution and moments of an unordered eigenvalue, as well as solutions for the expected log-determinant and expected characteristic polynomial. Our results are general for arbitrary numbers of transmit and receive antennas, and arbitrary number of keyholes with possibly differing powers. Based on these, we derive exact closed-form expressions and simplified upper and lower bounds for the ergodic mutual information, assuming that channel state information (CSI) is only known at the receiver. Results show that the capacity of multi-keyhole channels is typically worse than that for rich-scattering MIMO Rayleigh channels.

In this paper, we study the statistical distribution of the Demmel condition number of complex Wishart matrices, a quantity which has found numerous applications to wireless communications, such as adaptive switching, link adaptation, and spectrum sensing. We give an exact analytical expression for the probability density function (p.d.f.) of the Demmel condition number, and also derive simplified expressions for the high tail regime. These results indicate that the condition of complex Wishart matrices is dominantly decided by the difference between the matrix dimension and degree of freedom (DoF).

In this letter, we analyze the outage performance of a dual-hop relay fading channel in an interference-limited environment. We first derive closed-form expressions for the outage probability of both the amplify-and-forward (AF) and the decode-and-forward (DF) relay channels, based on which, the diversity analysis is conducted. In addition, we show that in terms of the outage probability performance, the worst scenario appears to be the case with equal received-power interferers, for a given total received interference power.

This paper studies the ergodic capacity of multiple-input multiple-output (MIMO) systems with a single co-channel interferer in the low signal-to-noise-ratio (SNR) regime. Two MIMO models namely Rician and Rayleigh-product channels are investigated. Exact analytical expressions for the minimum energy per information bit, Eb/N0min, and wideband slope, S0, are derived for both channels. Our results show that the minimum energy per information bit is the same for both channels while their wideband slopes differ significantly. Further, the impact of the numbers of transmit and receive antennas, the Rician K factor, the channel mean matrix and the interference-to-noise-ratio (INR) on the capacity, is addressed. Results indicate that interference degrades the capacity by increasing the required minimum energy per information bit and reducing the wideband slope. Simulation results validate our analytical results.

This paper presents an analytical characterization of the ergodic capacity of amplify-and-forward (AF) MIMO dual-hop relay channels, assuming that the channel state information is available at the destination terminal only. In contrast to prior results, our expressions apply for arbitrary numbers of antennas and arbitrary relay configurations. We derive an expression for the exact ergodic capacity, simplified closed-form expressions for the high SNR regime, and tight closed-form upper and lower bounds. These results are made possible by employing recent tools from finite-dimensional random matrix theory, which are used to derive new closed-form expressions for various statistical properties of the equivalent AF MIMO dual-hop relay channel, such as the distribution of an unordered eigenvalue and certain random determinant properties. Based on the analytical capacity expressions, we investigate the impact of the system and channel characteristics, such as the antenna configuration and the relay power gain. We also demonstrate a number of interesting relationships between the dual-hop AF MIMO relay channel and conventional point-to-point MIMO channels in various asymptotic regimes.

This paper presents an analytical performance analysis for a multi-keyhole multiple-input multiple-output (MIMO) fading channel. Our main contribution is a set of new statistical results for the maximum eigenvalue of the random multi-keyhole channel matrix, which includes closed-form expressions for the cumulative distribution function and probability density function, as well as asymptotic expansions. Based on the results, we analyze the outage performance of the optimal joint beamforming system in multi-keyhole MIMO channels. Our results illustrate that the outage performance of the multi-keyhole channels is generally worse than that of rich-scattering MIMO Rayleigh channels.

In this paper, we address the performance of a dual- hop relay fading channel in an interference-limited environment, in which the destination node is corrupted by co-channel interference while the relay node is perturbed by only an additive white Gaussian noise. We first investigate the scenario with equal-power interferers, under which the analytical expressions for the outage probability of both the amplify-and-forward (AF) and the decode-and-forward (DF) relay channels are derived in closed form. We then extend the analysis for the general case with unequal-power interferers. Using majorization theory, we show that in terms of outage probability, the worst scenario appears if the interferers are of equal power, for a given total interference power.

This paper presents an analytical performance investigation of an interference-limited double scattering multiple-input multiple-output (MIMO) channel employing optimum combining. Our main contribution is the derivation of the closed-form expressions for the cumulative distribution function (c.d.f.) and probability density function (p.d.f.) of the maximum eigenvalue of the resultant channel matrix after optimal combining when the transmit, receive and scattering correlation matrices are identities (hence, the channel is referred to as a Rayleigh-product channel). These results allow us to obtain the outage probability of the optimal combining system in a Rayleigh-product channel with co-channel interferences. Furthermore, we investigate, in depth, an important special case of double scattering channel, the keyhole channel, where based on various closed-form expressions for exact and asymptotic measures derived, we examine three key performance metrics, namely, the ergodic capacity, the outage performance and the symbol-error-rate (SER). The analytical results derived are validated by Monte-Carlo simulations.

This paper studies the analytical performance of a Rayleigh-product multiple-input multiple-output (RP-MIMO) channel in the presence of spatial correlation and cochannel interferences (CCI) at the receiver side. With optimal combining, we first derive a closed-form expression for the cumulative distribution function (C.D.F.) of the maximum eigenvalue of the resultant channel matrix. This result permits us to analyze the outage probability of the RP-MIMO channel. In addition, the ergodic capacity of a keyhole MIMO channel with cochannel interferences is also derived from these new statistical results.

This paper analyzes the ergodic capacity of dual-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay channels, assuming that the channel state information is available only at the destination terminal. We first present a new expression for the unordered eigenvalue density of a certain product of independent complex matrices, which is used to derive an exact expression for ergodic capacity. We then employ results from multivariate statistics to derive new closed-form upper and lower bounds of the ergodic capacity. In contrast to prior work, our results apply for arbitrary numbers of antennas and arbitrary relay configurations. Numerical results are provided to validate the theoretical analysis.

This paper investigates the ergodic capacity limit of multiple-input multiple-output (MIMO) Nakagami-fading channels for arbitrary finite number of antennas. Through the use of majorization theory, we derive ergodic capacity bounds for this general class of channels. In the high signal-to-noise-ratio (SNR) regime, we show that a simple expression for the capacity upper bound is available, which reveals the impact of channel fading parameter on the system ergodic capacity. Besides, the asymptotic behavior of the ergodic capacity where the number of antenna(s) at one or both side(s) goes to infinity, is studied. By considering the low SNR regime, on the other hand, we give an approximation and a lower bound for the ergodic capacity. Monte Carlo simulation results are provided to verify the tightness of the proposed bounds.