Ultra-Reliable and Low-Latency Communication (URLLC) requires stable transmission under strict latency and high-reliability constraints. Consequently, passive retransmission, which merely waits for bit errors to occur, cannot meet these stringent control and security requirements. This paper proposes an active feedback mechanism, namely the Transmission Prediction Feedback Network (TPFNet). It leverages trajectory-aware modeling and post-decoding statistics to perform risk assessment and guide strategy switching, thereby proactively managing subsequent retransmissions. The TPFNet concept is based on utilizing the historical progression of symbols on the constellation diagram as an initial reference, integrating memory-augmented trajectory-aware modeling. This approach ensures that decision-making is not confined to the instantaneous state received at a single point in time. Initially, it uses the in-phase and quadrature path trajectories of the received sequence to jointly assess instantaneous deviations and temporally cumulative morphological changes. Secondly, it employs residual regression for the calibration of symbol positions, complemented by a concentric constraint loss to mitigate shrinkage bias toward the constellation origin. Finally, it provides a confidence metric for the transceiver link, which facilitates adaptive modulation and coding scheme switching. Simulation results demonstrate that the proposed method significantly enhances transmission fidelity under diverse channel conditions. It markedly improves the block error rate and undetected error rate while enhancing the robustness of symbol decisions at the boundaries.

Channel models with a good balance of pervasiveness, accuracy, and efficiency are important for the design and optimization of the sixth generation (6G) wireless communication systems. In this paper, a pervasive beam domain channel model (BDCM) capable of modeling all frequency bands and scenarios in 6G is proposed. Unlike traditional geometry-based stochastic models (GBSMs) that describe channels between antenna pairs in the space domain, the pervasive BDCM reformulates the channel in terms of beam pairs to describe special channel characteristics in the beam domain, such as sparsity and Doppler insensibility. The proposed BDCM incorporates essential spatial wideband and spherical wavefront effects for ultra-massive multiple-input multiple-output (MIMO) by considering the nonlinear phase variations across antenna arrays. The pervasive transform matrices for different antenna configurations are derived to enable flexible conversions between the pervasive GBSM and pervasive BDCM. In addition, key statistical properties of the BDCM are derived and analyzed. The proposed pervasive BDCM in different frequency bands and scenarios are validated by measurement data and compared with the GBSM results. The complexity analysis reveals that the proposed pervasive BDCM significantly reduces the computational complexity compared with the pervasive GBSM under different scatterer densities.

We investigate a non-orthogonal pilot design for massive multiple-input multiple-output (MIMO) channel estimation with orthogonal frequency division multiplexing (OFDM) modulation. Leveraging two-dimensional (2D) beam based channel model, we formulate a signal model for channel estimation with a comb-type pilot structure. Then, we propose generalized phase shift pilots (GPSPs) and reveal that both GPSPs and their discrete Fourier transform (DFT) have constant modulus, and the DFT of GPSPs also has optimal autocorrelation property and beneficial crosscorrelation property. Subsequently, we prove that the inter-user interference with GPSPs is negligible when the weight sequence length of GPSPs is large enough or when channels can be differentiated in the angle domain, validating the feasibility of GPSPs, especially when the statistical channel state information (CSI) is unavailable. Further, by leveraging the correlation properties of GPSPs, an efficient implementation for GAMP based channel estimation is provided. Simulation results indicate that GPSPs enable low-complexity channel estimation while ensuring satisfactory performance.

The soft-output successive cancellation list (SOSCL) decoder provides a methodology for estimating the aposteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder of polar codes. However, the sequential decoding nature of SCL introduces high decoding latency to SOSCL. In this paper, we incorporate node-based fast decoding into the SO-SCL framework. After addressing the challenge of soft output extraction in special node decoding, we proposed the soft-output fast SCL (SO-FSCL) decoding algorithm, along with its log-domain implementation and hardware-friendly version. The proposed SO-FSCL decoder can be regarded as an addon extension to FSCL decoder, enabling us to autonomously choose whether to output only hard decisions like FSCL or to provide additional soft outputs. Latency and complexity analyses demonstrate that SO-FSCL can significantly reduce, for example, decoding time steps by 81.8% (with unlimited resources), the number of additions by 41.3%, and the number of comparisons by 46.4%. Meanwhile, simulation results indicate that SO-FSCL delivers almost the same soft-output performance as SO-SCL, outperforming other soft-output polar decoders, especially in scenarios involving iterative decoding.

In this paper, we investigate the signal detection for user-centric network (UCN) massive multi-input multi-output (mMIMO) system. We consider that the users are divided into multiple user groups (UGs). For each UG, leveraging the interference sparsity, we reveal that the performance of the minimum-mean-square-error (MMSE) detector can be guaranteed in the network mMIMO system by using the matched filtering (MF) outputs of the intra-group and interfering users. Then, with the base station (BS) connection sparsity, we reveal that the detection performance of each UG is primarily determined by a limited number of associated BSs. To facilitate practical application, we propose a straightforward user grouping method and outline the process for determining interfering users and associated BSs for each UG in UCN mMIMO systems. Then, we propose a user-centric detection method that decouples the detection process for each UG into two stages. In the first stage, local MF is performed at each associated BSs using local information. In the second stage, group-wise interference cancellation (IC) is carried out to obtain detection results at the primary serving BS (PSBS) of each UG, with information exchanged from auxiliary serving BSs (ASBSs). Simulation results confirm the effectiveness and computational efficiency of our proposed user-centric detection for the UCN mMIMO system.

In this paper, we investigate the downlink (DL) precoder design for massive multiple-input multiple-output (MIMO) multiple low earth orbit (LEO) satellite communication (SATCOM). We first establish the DL beam based channel model for massive MIMO multiple LEO SATCOM systems by using sampled array response vectors. We propose a closed-form statistical channel state information (sCSI) based space domain DL precoder design for multi-satellite systems by maximizing the average signal-to-leakage-plus-noise ratio (ASLNR). Then, with the proposed beam based channel model, we transform the design of space domain DL precoder into that of lower-dimensional beam domain vector and the resulting space domain precoder is beam structured. Moreover, by leveraging the properties of the beam matrix, we propose a low-complexity design and implementation for the beam structured DL precoder. Simulation results demonstrate that the proposed beam structured DL precoder can achieve near performance to the space domain approach with significantly reduced computational complexity.

Massive multiple-input multiple-output - orthogonal frequency division multiplexing (MIMO-OFDM) systems face the challenge of high channel acquisition overhead while providing significant spectral efficiency (SE). Adjustable phase shift pilots (APSPs) are an effective technique to acquire channels with low overhead by exploiting channel sparsity. In this paper, we extend it to multiple groups and propose multi-group adjustable phase shift pilots (MAPSPs) to improve SE further. We first introduce a massive MIMO-OFDM system model and transform the conventional channel model in the space-frequency domain to the angle-delay domain, obtaining a sparse channel matrix. Then, we propose a method of generating MAPSPs through multiple basic sequences and investigate channel estimation processes. By analyzing the components of pilot interference, we elucidate the underlying mechanism by which interference affects MMSE estimation. Building upon this foundation, we demonstrate the benefit of phase scheduling in MAPSP channel estimation and establish the optimal design condition tailored for scheduling. Furthermore, we propose an implementation scheme based on Zadoff-Chu sequences that includes received signal pre-processing and pilot scheduling methods to mitigate pilot interference. Simulation results indicate that the MAPSP method achieves a lower mean square error (MSE) of estimation than APSP and significantly enhances SE in mobility scenarios.

Exploiting on-device data and computing power for machine learning at the network edge is challenged by constrained device resources, privacy requirements, and local data heterogeneity. To address the above gap, this work proposes a dual-functional blockchain framework named BagChain for bagging-based decentralized ML. BagChain integrates blockchain with distributed ML by replacing the computationally costly hash computing in proof-of-work with ML model training and validation, and does not rely on any trusted central servers. Individual miners in BagChain train base models by using their local computing resources and private data and further aggregate these base models, which could be very weak, into strong ensemble models. More specifically, we design a three-layer blockchain structure and associated generation and validation mechanisms to enable distributed ML among uncoordinated miners without revealing raw data. To reduce computational waste due to blockchain forking, we further propose the cross fork sharing mechanism for practical networks with lengthy delay and limited bandwidth. Extensive experiments illustrate the superiority and efficacy of BagChain when handling various ML tasks on both independently and identically distributed (IID) and non-IID datasets. BagChain remains robust and effective even when facing resource constrained mobile devices, heterogeneous private user data, and limited network connectivity. The source code of BagChain is released at: https://github.com/czxdev/BagChain.

With advancements in wireless communication and localization technologies, cellular networks are evolving towards integrated sensing and communication (ISAC) capabilities. To address the challenges of sensing-assisted communication, we introduce the digital twin of channel (DToC). Specifically, locations of user terminals (UTs) and their statistical channel state information (sCSI) are treated as physical objects and virtual counterparts in the concept of digital twin (DT), respectively. In this work, we establish a probabilistic model that characterizes sCSI as a location-conditioned distribution. To enable precise sCSI generation, we enhance the latent diffusion model (LDM) and propose an improved latent diffusion model (ILDM) with deterministic sampling. We further propose an accelerated LDM method to speed up the generation process by skipping certain sampling steps. Simulation results demonstrate that the proposed ILDM achieves high accuracy in generating sCSI, while the accelerated LDM delivers significant speedups with minor performance degradation. Our results also validate that the DToC framework can effectively generate sCSI without pilot overhead.

Accurate channel state information (CSI) acquisition for massive multiple-input multiple-output (MIMO) systems is essential for future mobile communication networks. Channel fingerprint (CF), also referred to as channel knowledge map, is a key enabler for intelligent environment-aware communication and can facilitate CSI acquisition. However, due to the cost limitations of practical sensing nodes and test vehicles, the resulting CF is typically coarse-grained, making it insufficient for wireless transceiver design. In this work, we introduce the concept of CF twins and design aconditionalgenerative diffusion model (CGDM) with strong implicit prior learning capabilities as the computational core of the CF twin to establish the connection between coarse- and fine-grained CFs. Specifically, we employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CFconditionedon the coarse-grained CF, enabling the CGDM to learn the complicated distribution of the target data. During the denoising neural network optimization, the coarse-grained CF is introduced asside informationto accurately guide the conditioned generation of the CGDM. To make the proposed CGDM lightweight, we further leverage the additivity of output distortion and introduce a one-shot pruning approach along with a multi-objective knowledge distillation technique. Experimental results show that the proposed approach exhibits significant improvement in reconstruction performance compared to the baselines. Additionally, zero-shot testing on reconstruction tasks with different magnification factors further demonstrates the scalability and generalization ability of the proposed approach.

In this paper, we investigate receiver design for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. We first establish a modified beam based channel model (BBCM) by performing uniform sampling for directional cosine with deterministic sampling interval, where the beam matrix is constructed using a phase-shifted discrete Fourier transform (DFT) matrix. Based on the modified BBCM, we propose a beam structured turbo receiver (BSTR) involving low-dimensional beam domain signal detection for grouped user terminals (UTs), which is proved to be asymptotically optimal in terms of minimizing mean-squared error (MSE). Moreover, we extend it to windowed BSTR by introducing a windowing approach for interference suppression and complexity reduction, and propose a well-designed energy-focusing window. We also present an efficient implementation of the windowed BSTR by exploiting the structure properties of the beam matrix and the beam domain channel sparsity. Simulation results validate the superior performance of the proposed receivers but with remarkably low complexity.

In this paper, we propose a low complexity turbo receiver for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) systems. We first introduce the beam based channel model (BBCM) with uniform sampling for directional cosine. By leveraging the BBCM, we reveal the interference sparsity of HF skywave massive MIMO systems, which is defined as the asymptotic sparsity of the channel Gram matrix. Exploiting the interference sparsity, we provide a condition of extracting sufficient observation for signal detection. Motivated by this condition, we construct the interference user terminal (UT) set (IUS) and extract the observation vector from the received signal after matched filtering (MF) for each UT. Then, a low-dimensional interference sparsity-aware detector (ISD) is separately designed for each UT by minimizing the mean-squared error (MSE), and the interference sparsity-aware turbo receiver (ISTR) is subsequently formulated using ISDs. Under a relaxed version of the condition for sufficient observation selection, we prove the optimality of the ISTR. Further, we develop an efficient implementation of the ISTR, involving approximate computation of the ISD, the signal reconstructed by ISD and the channel Gram matrix. Moreover, an efficient construction of IUS using the statistical channel state information (CSI) is also proposed. Simulation results confirm that the proposed ISTR achieves excellent performance with relatively low complexity.

Optical wireless communication (OWC), with its abundant spectrum resources enabling ultra-high data rates, has emerged as a promising technique for the sixth generation (6G) wireless communications. To address the challenge of aligning base stations (BSs) and user terminals (UTs), as well as to enhance the number of served UTs and transmission rates per UT, this paper proposes a fiber-enabled network massive multiple-input multiple-output (MIMO) OWC system. By employing distributed passive optical antennas (POAs) comprised of fiber port arrays and lenses, BSs generate optical beams irradiating towards different directions, which can provide the optical signal coverage and spatial resolution of UTs at different positions, improving the system throughput. We establish the network channel model and design precoding vectors to maximize the system sum rate. We provide an iterative design of the precoding vectors in general case and propose an asymptotically optimal beam division multiple access (BDMA) transmission scheme with a large number of fiber ports. The simulation results demonstrate that our proposed system can achieve tens of Gbps per UT and several Tbps in system throughput. Finally, we construct an experimental system capable of achieving 10 Gbps transmission rate of each link and real-time wireless transmission of 4K video streams.

The advancement of 6G mobile communication and positioning technologies has amplified the significance of location-aware tools, such as location-indexed channel fingerprints (CFs) and channel charting, which are becoming key enablers for massive MIMO-OFDM systems. In this paper, we propose a novel channel charting with physical CFs (PCFs) and demonstrate its effectiveness in channel state information (CSI) acquisition. First, we define the PCF based on a cluster-based geometric stochastic channel model (GBSM), enabling a comprehensive representation of physical channel characteristics using a compact set of parameters. We then develop a methodology for PCF acquisition in massive MIMO-OFDM systems. By exploiting the relationship between PCFs and the space-frequency-time (SFT) domain channel, the proposed method extracts PCFs from multi-location channel measurements and constructs a structured channel charting with location-indexed PCFs. Furthermore, we propose a low-complexity algorithm to acquire beam domain statistical CSI (sCSI) using the PCFs in the channel charting. The resulting sCSI can be directly employed as prior information for channel estimation. Simulation results show that the proposed method delivers sCSI performance comparable to traditional online probing techniques, and the generated sCSI can serve as reliable prior knowledge to significantly enhance the accuracy of channel estimation. These results validate the proposed PCF as a powerful and versatile tool for channel acquisition and system design of the next-generation mobile communication.

In this paper, we investigate the decoupled designs of precoders and receivers for both downlink (DL) and uplink (UL) in massive multiple-input multiple-output (MIMO) multiple low earth orbit (LEO) satellite communication systems. We first establish the beam based satellite channel model, where the beam matrix is constructed with sampled steering vectors. Then, we propose a decoupled precoder and receiver design for both DL and UL, which allows precoders and receivers to be designed independently at each satellite and user terminal (UT), respectively, with only local statistical channel state information (sCSI). Moreover, with the established beam based channel model, the design of space domain DL precoder and UL receiver can be converted into that of lower-dimensional beam domain vectors with only local sCSI, and the resulting space domain precoder and receiver are beam structured. Furthermore, we propose a low-complexity design and implementation for the beam structured DL precoder and UL receiver by exploiting properties of the beam matrix, significantly reducing the computational complexity. Simulation results validate the proposed approaches.

Low-altitude communications can promote the integration of aerial and terrestrial wireless resources, expand network coverage, and enhance transmission quality, thereby empowering the development of sixth-generation (6G) mobile communications. As an enabler for low-altitude transmission, 3D channel fingerprints (3D-CF), also referred to as the 3D radio map or 3D channel knowledge map, are expected to enhance the understanding of communication environments and assist in the acquisition of channel state information (CSI), thereby avoiding repeated estimations and reducing computational complexity. In this paper, we propose a modularized multimodal framework to construct 3D-CF. Specifically, we first establish the 3D-CF model as a collection of CSI-tuples based on Rician fading channels, with each tuple comprising the low-altitude vehicle’s (LAV) positions and its corresponding statistical CSI. In consideration of the heterogeneous structures of different prior data, we formulate the 3D-CF construction problem as a multimodal regression task, where the target channel information in the CSI-tuple can be estimated directly by its corresponding LAV positions, together with communication measurements and geographic environment maps. Then, a high-efficiency multimodal framework is proposed accordingly, which includes a correlation-based multimodal fusion (Corr-MMF) module, a multimodal representation (MMR) module, and a CSI regression (CSI-R) module. Numerical results show that our proposed framework can efficiently construct 3D-CF and achieve at least 27.5% higher accuracy than the state-of-the-art algorithms under different communication scenarios, demonstrating its competitive performance and excellent generalization ability. We also analyze the computational complexity and illustrate its superiority in terms of the inference time.

With the development of the upcoming sixth-generation (6G) wireless networks, there is a pressing need for innovative technologies capable of satisfying heightened performance indicators. Fluid antenna system (FAS) is proposed recently as a promising technique to achieve higher data rates and more diversity gains by dynamically changing the positions of the antennas to form a more desirable channel. However, worries regarding the possibly harmful effects of electromagnetic (EM) radiation emitted by devices have arisen as a result of the rapid evolution of advanced techniques in wireless communication systems. Specific absorption rate (SAR) is a widely adopted metric to quantify EM radiation worldwide. In this paper, we investigate the SAR-aware multiuser multiple-input multiple-output (MIMO) communications assisted by FAS. In particular, a two-layer iterative algorithm is proposed to minimize the SAR value under signal-to-interference-plus-noise ratio (SINR) and FAS constraints. Moreover, the minimum weighted SINR maximization problem under SAR and FAS constraints is studied by finding its relationship with the SAR minimization problem. Simulation results verify that the proposed SAR-aware FAS design outperforms the adaptive backoff and fixed-position antenna designs.

With the widespread application of location-based services, fingerprint-based localization has demonstrated advantages in environments with complex signal propagation. Deep learning has significantly improved the efficiency of both offline training and online matching in localization processes. However, existing fingerprints only contain terminal position information without capturing motion states, and neural network designs have not fully incorporated structural features such as fingerprint sparsity. In this paper, we propose a triple-beam fingerprint (TBF) incorporating Doppler information and design a Transformer-based localization and orientation awareness network (LOA-Net) to simultaneously estimate user position and motion direction in massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We first show the correlation between TBF and multipath information, and investigate the collinearity of different TBFs, demonstrating that TBF is an effective small-size sparse fingerprint. Then, we propose LOA-Net containing a mask-augmented detection Transformer for regression (MaskDETR-Reg) module and a fusion-enhanced Transformer for direction classification (Fusion-TDC) module to process angle-delay domain information and Doppler domain information, respectively. Finally, in the simulation of indoor scenarios defined in 3GPP 38.901, the proposed method achieves significantly better localization accuracy than weighted$K$-nearest neighbors (WKNN), 2D and 3D convolutional neural networks (CNNs), and achieves satisfactory motion direction estimation accuracy.

We investigate the linear precoding for sum-rate maximization in network massive multiple-input multiple-output (MIMO) transmission, where the cooperative transmission by all base stations (BSs) enhances the capacity, reliability, and robustness. To address the growing complexity of traditional iterative algorithms in large-scale systems, we leverage the weighted minimum mean square error (WMMSE) solution and show that the precoding vectors can be fully reconstructed from a set of low-dimensional parameters. By exploiting the structure and relationship of these parameters, we reformulate the original problem in a reduced-dimensional space while preserving equivalence to the original solution. Deep learning techniques are employed to solve this reformulated problem, where equivalent scaling of the variables facilitates pre-processing for training and further reduces the dimension of the learning input. A neural network is trained on the refined low-dimensional objectives with a tailored loss, allowing the precoding vectors to be directly calculated from its output. As demonstrated by numerical results, the proposed deep learning-based precoder performs well with considerably reduced online processing complexity.

In this work, we introduce the integration of generalized low-density parity-check (GLDPC) codes with short polar component codes, termed GLDPC codes with polar component codes (GLDPC-PC). A recently proposed soft-input soft-output (SISO) decoder for polar-like codes enables effective iterative belief propagation decoding for GLDPC-PC. This SISO decoder after a post-processing exhibits little performance loss to the optimal SISO decoder when all the variable nodes have relatively low degrees. A three-step method is introduced to design protograph-based GLDPC codes. The constructed GLDPC codes are compared with 5G LDPC codes. They exhibit little performance loss in the waterfall region and possess better error floor with less iterations.

In this paper, we investigate receiver design for high frequency (HF) skywave massive multiple-input multipleoutput (MIMO) communications. We first establish a modified beam based channel model by performing uniform sampling for directional cosine with deterministic sampling interval, where the beam matrix is constructed as discrete Fourier transform (DFT)based structure. Rooted in the modified beam based channel model, we propose a beam structured turbo receiver (BSTR) involving low-dimensional beam structured signal detection for grouped user terminals (UTs). Then we present efficient implementation of the BSTR by exploiting the structure properties of the beam matrix and the beam domain channel sparsity. Simulation results validate the superior performance of the proposed BSTR with low complexity.

In this paper, we investigate the channel estimation in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The recently proposed simplified information geometry (SIG) algorithm offers a promising solution for channel estimation with relatively low computational complexity. However, the damping factors used in the SIG algorithm are based on a heuristic strategy, which results in inconsistent performance. To address this issue, we propose a deep unfolding based SIG (DU-SIG) approach in this paper. Specifically, each iteration of the SIG algorithm is unfolded into a layer-wise structure resembling a neural network and the damping factors are optimized based on deep unfolding technique. Notably, the optimized damping factors can be directly integrated into the original SIG algorithm to improve the performance of channel estimation without increasing the computational complexity. Simulation results validate the effectiveness and superiority of our proposed algorithm.

In this paper, we propose the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with symplectic optimization. In the UCN mMIMO systems, each user terminal (UT) is served by a subset of base stations (BSs) rather than all BSs, which simplifies the system implementation and reduces the dimension of the precoders to be designed. To address the high complexity of the matrix inversion in traditional linear precoders, we employ symplectic optimization. To better fit the symplectic optimization method, we transform the receive model into the real field. By utilizing the conversion between potential and kinetic energy in physics, we iteratively obtain the precoder vectors directly. Simulation results demonstrate that the proposed method outperforms the weighted minimum mean-squared error (WMMSE) and regularized zero forcing (RZF) precoders, while also exhibiting lower complexity.

Massive MIMO employs large antenna arrays to support multi-user wireless communications with high data rate. To mitigate the inter-user interference, various precoding schemes are proposed, which usually need iterative calculation and suffer from high complexity, especially for multi-cell scenarios. This paper investigates the intelligent design of network precoding schemes based on the optimal structure. Firstly, we derive an interference avoidance precoding in network massive MIMO communications, and propose an optimal precoding structure. We extract part expression in the optimal structure as parameters and propose a parameterized precoding without iterations. By using the convolutional neural network (CNN), these parameters can be trained and predicted, which significantly reduces the computational complexity. Simulation results present that the proposed precoding scheme can increase the transmission rate by 20% than the RZF scheme.

In the cell-free integrated sensing and communication (ISAC) system, distributed access points (APs) collaborate to simultaneously serve users and sense targets, which reduces the impact of signal blockage and enhances sensing accuracy. However, the cooperation among APs depends on backhaul links to exchange information with a central processing unit (CPU), which is often limited in practice. Therefore, this paper investigates beamforming design for cell-free ISAC multiple-input-multipleoutput (MIMO) systems with capacity-limited backhaul, aiming to minimize transmit power while ensuring both sensing and communication performance. Two algorithms are proposed: the global optimization algorithm and the low-complexity algorithm. The global optimization algorithm leverages the equivalence between backhaul capacity and AP-UE service pair selection, decomposing the original problem into multiple sub-problems. Each sub-problem is converted into a semidefinite programming (SDP). By selecting the best solution from all sub-problems, the globally optimal solution can be achieved. In the low-complexity algorithm, the backhaul capacity constraint is approximated by a smooth function. Successive convex approximation (SCA) is then applied to address the resulting non-convex constraint iteratively. Simulation results validate the effectiveness of the proposed algorithms.

Massive MIMO technology can meet the increasing demand for ultra-high transmission rate in modern wireless networks. However, interference among users sharing the same resource block (RB) reduces the transmission rate, and restricts the capabilities of massive MIMO systems. In this paper, we propose a multi-cell user grouping method based on graph clustering to enhance the system performance. We provide a beam based statistical channel model and establish a user grouping problem aimed at maximizing the sum rate. We analyze user grouping criteria that consider channel power and directions for users on the same RB. Subsequently, we employ unsupervised learning to develop a density-cut-based graph clustering algorithm (DGCA). Simulation results validate that our algorithm achieves a higher sum rate than the benchmark methods without extra computational complexity.

With the growth of positioning demand, fingerprintbased positioning, which utilizes multi-path information to improve positioning accuracy, has attracted academic and industrial attentions. Current position fingerprints often incur significant storage overhead and time complexity for machine learning due to their large dimension. This paper investigates positioning in massive multiple-input multiple-output (MIMO) communication systems using a low dimensional fingerprints called the angledelay power coordinates matrix (ADECM). We first use a dimensionality reduction algorithm for the widely used position fingerprint angle-delay channel power matrix (ADCPM) in existing works, and define the reduced matrix as the ADECM. Subsequently, recognizing the excellent performance of generative adversarial network in regression tasks, we propose the Feature Separation Generative Adversarial Network (FSGAN). This innovative network is specifically designed to accurately estimate the position of user terminals. Simulation results show that the proposed positioning method outperforms existing methods.

Estimating massive multiple-input multiple-output - orthogonal frequency division multiplexing (MIMO-OFDM) channels with low pilot overhead presents a significant challenge. Leveraging channel sparsity and pilot argument information (PAI), we propose a multi-group adjustable phase shift pilot (MAPSP) channel estimation method aimed at reducing pilot overhead. We first introduce a sparse channel model in angle-delay domain. Then, we propose the approach of generating phase shift pilots by dividing user terminals (UT) into groups and explore channel estimation based on the sparse channel model. We demonstrate that pilot interference can be mitigated by phase scheduling and received signal pre-processing. Capitalizing on this property, we propose a MAPSP implementation scheme. Simulation results indicate that the proposed MAPSP technique achieves a lower mean square error (MSE) of estimation than APSP and significantly enhances spectral efficiency.

The soft-output successive cancellation list (SO-SCL) decoder provides a methodology for estimating the a-posteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder for polar codes. However, the sequential nature of SCL decoding leads to a high decoding latency for the SO-SCL decoder. In this paper, we propose a soft-output fast SCL (SO-FSCL) decoder by incorporating node-based fast decoding into the SO-SCL framework. Simulation results demonstrate that the proposed SO-FSCL decoder significantly reduces the decoding latency without loss of performance compared with the SO-SCL decoder.

With knowledge of statistical channel state information (CSI) at the base station (BS), one can reduce the pilot overhead and improve the channel estimation performance in massive multiple-input multiple-output (MIMO) systems by exploiting the non-orthogonal pilot sequences. However, due to the limited uplink (UL) training resources, it is challenging to acquire the high-accuracy statistical CSI in real-time. To tackle this problem, an emerging technique is to construct a channel knowledge map (CKM) based on the statistical CSI. In order to construct such a CKM, it is necessary to analyze the spatial and temporal correlations of statistical CSI. Specifically, this paper analyzes the spatial and temporal correlations of beam domain channel power matrices (BDCPMs), obtained from channel measurements in a massive MIMO testbed. Furthermore, to verify the effectiveness of the stored aged BDCPMs, we also simulate channel estimation performance using BDCPMs measured at different dates. Both measured and simulated results demonstrate the effectiveness of the CKM based on statistical CSI.

Hybrid digital-analog precoding is a pivotal transmission technique to balance communication performance and hardware costs associated with radio frequency (RF) chains in millimeter wave (mmWave) massive multiple-input multipleoutput (MIMO). However, most existing designs utilize Shannon rate and assume an infinite blocklength, which is impractical for emerging finite blocklength (FBL) applications, such as massive machine-type communications. To fill in this gap, this paper investigates hybrid precoding optimization in the FBL regime. The aim is to maximize the weighted sumrate (WSR), while fulfilling the transmit power budget at the base station (BS) and users' minimum rate requirements. The formulated optimization problem is highly challenging to solve, particularly due to the complex and nonconcave FBL rate function and the intricate coupling between analog and digital precoders. To tackle these issues, we propose a computationally efficient solution based on the penalty dual decomposition (PDD) method, which is guaranteed to converge to the Karush-KuhnTucker (KKT) solutions under mild conditions. Simulation results demonstrate that our proposed hybrid precoding design significantly outperforms several baseline schemes, especially those ignoring the impact of blocklength and adopting Shannon rate as the performance metric.

Low Earth Orbit (LEO) satellite communication is crucial for developing sixth-generation (6G) networks. The integration of massive multiple-input multiple-output (MIMO) technology is being actively researched to enhance the performance of LEO satellite communication systems. However, the limited power resources of LEO satellites pose significant challenges to improving energy efficiency (EE) under power-constrained conditions. Typical optimization-based methods often lack real-time adaptability and computational efficiency. This paper proposes innovative solutions to address the challenges of precoding in massive MIMO LEO satellite communications. Specifically, we introduce a deep unfolding of the Dinkelbach algorithm and the weighted minimum mean square error (WMMSE) approach to achieve enhanced EE. This transformation of iterative optimization procedures into a graph neural network (GNN) leads to faster convergence and improved computational efficiency. Furthermore, we apply the Taylor expansion method to approximate matrix inversion within the GNN framework. Numerical experiments demonstrate the superiority of our proposed method in terms of complexity and robustness, achieving significant improvements over other state-of-the-art methods.

Direct-to-smartphone is becoming a popular direction of low earth orbit (LEO) satellite communications. In this paper, we investigate the downlink (DL) precoding method for multi-beam LEO satellite communication systems with asynchronous interference. Specifically, we first establish the channel model revealing that the transmitting signals between different beams are asynchronous and further derive the expression of asynchronous interference. Then, we propose a DL precoding method based on maximizing the average signal-to-leakage-plus-noise ratio (ASLNR) with statistic channel state information (CSI). The simulation results demonstrate the significant improvements of the proposed precoding method in the system sum rate.

Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

Multiple-input-multiple-output (MIMO) optical wireless communication (OWC) is a promising technology capable of providing high data rates. However, interuser interference can negatively impact system performance. Existing studies employ CVX-based beamforming designs to mitigate this issue, but these approaches suffer from high computational complexity. To tackle this challenge, this article studies the low-complexity beamforming design to maximize the sum rate. Specifically, we first utilize the fractional programming (FP) method to decompose the original beamforming problem into a series of convex subproblems and propose a block coordinate descent (BCD)-based beamforming framework. Then, we provide closed-form or semi-closed-form solutions for these subproblems. For the general approximate sum rate maximization, we drive the optimal semi-closed-form beamforming structure based on the Karush-Kuhn–Tucker (KKT) conditions. Furthermore, we propose a closed-form solution for the upper bound maximization by taking advantage of the separability of optical power constraints. Finally, numerical results indicate that the proposed beamforming designs reduce the computational complexity significantly while keeping the sum rate performance.

In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization. In UCN mMIMO systems, each user terminal (UT) is served by a subset of base stations (BSs) instead of all the BSs, facilitating the implementation of the system and lowering the dimension of the precoders to be designed. By proving that the precoder set satisfying the per-BS power constraints forms a Riemannian submanifold of a linear product manifold, we transform the constrained precoder design problem in Euclidean space to an unconstrained one on the Riemannian submanifold. Riemannian ingredients, including orthogonal projection, Riemannian gradient, retraction and vector transport, of the problem on the Riemannian submanifold are further derived, with which the Riemannian conjugate gradient (RCG) design method is proposed for solving the unconstrained problem. The proposed method avoids the inverses of large dimensional matrices, which is beneficial in practice. The complexity analyses show the high computational efficiency of RCG precoder design. Simulation results demonstrate the numerical superiority of the proposed precoder design and the high efficiency of the UCN mMIMO system.

Multi-band massive multiple-input multiple-output (MIMO) communication can promote the cooperation of licensed and unlicensed spectra, effectively enhancing spectrum efficiency for Wi-Fi and other wireless systems. As an enabler for multi-band transmission, channel fingerprints (CF), also known as the channel knowledge map or radio environment map, are used to assist channel state information (CSI) acquisition and reduce computational complexity. In this paper, we propose CF-CGN (Channel Fingerprints with Cycle-consistent Generative Networks) to extrapolate CF for multi-band massive MIMO transmission where licensed and unlicensed spectra cooperate to provide ubiquitous connectivity. Specifically, we first model CF as a multichannel image and transform the extrapolation problem into an image translation task, which converts CF from one frequency to another by exploring the shared characteristics of statistical CSI in the beam domain. Then, paired generative networks are designed and coupled by variable-weight cycle consistency losses to fit the reciprocal relationship at different bands. Matched with the coupled networks, a joint training strategy is developed accordingly, supporting synchronous optimization of all trainable parameters. During the inference process, we also introduce a refining scheme to improve the extrapolation accuracy based on the resolution of CF. Numerical results illustrate that our proposed CF-CGN can achieve bidirectional extrapolation with an error of 5 ∼ 17 dB lower than the benchmarks in different communication scenarios, demonstrating its excellent generalization ability. We further show that the sum rate performance assisted by CF-CGN-based CF is close to that with perfect CSI for multi-band massive MIMO transmission.

In this paper, we investigate the channel estimation in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Many channel estimation algorithms, such as the recently proposed original information geometry (OIG) and simplified information geometry (SIG) algorithms, require statistical channel state information (CSI) when performing instantaneous channel estimation. However, in practice, obtaining statistical CSI may require a substantial amount of additional overhead, especially when the number of users is large. To address this issue, we propose an expectation-maximization (EM) based IG approach for massive MIMO-OFDM channel estimation, which does not need to know the statistical CSI beforehand and can estimate the instantaneous channel and statistical CSI simultaneously. Specifically, the EM-based procedure is embedded within the IG algorithms’ iteration loop to learn the statistical CSI. Moreover, all of the quantities needed for the EM updates can be obtained by the IG procedure, making the overall process computationally efficient. Furthermore, we propose a low complexity implementation for the proposed EM-SIG algorithm with the adjustable phase shift pilots of multiple roots, which can significantly reduce the computational complexity of EM-SIG. Simulations confirm the excellent performance of the proposed EM-OIG and EM-SIG algorithms both in terms of channel estimation and statistical CSI estimation.

In this paper, we investigate precoder design for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first reveal the effect of the precoder on the effective channel at receivers and formulate the precoder design for a group of subcarriers as a sum-rate maximization problem, where the delay spread of the effective channel is constrained to maintain its smoothness. Then with the beam based channel model and beam domain channel sparsity, the design of space domain precoders for a group of subcarriers are transformed into that of a space-frequency (SF) beam domain vector and the resulting space domain precoder at each subcarrier is beam structured. Efficient calculation for design and implementation of the beam structured precoder (BSP) is proposed. Moreover, effective channel estimation with the BSP is discussed. Simulation results show that the proposed BSP can enhance the effective channel estimation performance and significantly improve the system performance.

In this paper, we propose a channel acquisition approach with time-frequency phase-shifted pilots (TFPSPs) for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We first present a triple-beam (TB) based channel tensor model, allowing for the representation of the space-frequency-time (SFT) domain channel as the product of beam matrices and the TB domain channel tensor. By leveraging the specific characteristics of TB domain channels, we develop TFPSPs, where distinct pilot signals are simultaneously transmitted in the frequency and time domains. Then, we present the optimal TFPSP design and provide the corresponding pilot scheduling algorithm. Further, we propose a tensor-based information geometry approach (IGA) to estimate the TB domain channel tensors. Leveraging the specific structure of beam matrices and the properties of TFPSPs, we propose a low-complexity implementation of the tensor-based IGA. We validate the efficiency of our proposed channel acquisition approach through extensive simulations. Simulation results demonstrate the superior performance of our approach. The proposed approach can effectively suppress inter-UT interference with low complexity and limited pilot overhead, thereby enhancing channel estimation performance. Particularly in scenarios with a large number of UTs, the channel acquisition method outperforms existing approaches by reducing the normalized mean square error (NMSE) by more than 8 dB.

Multi-frequency massive multi-input multi-output (MIMO) communication is a promising strategy for both 5G and future 6G systems, ensuring reliable transmission while enhancing frequency resource utilization. Statistical channel state information (CSI) has been widely adopted in multi-frequency massive MIMO transmissions to reduce overhead and improve transmission performance. In this paper, we propose efficient and accurate methods for obtaining statistical CSI in multi-frequency massive MIMO systems. First, we introduce a multi-frequency massive MIMO channel model and analyze the mapping relationship between two types of statistical CSI, namely the angular power spectrum (APS) and the spatial covariance matrix, along with their correlation across different frequency bands. Next, we propose an autoregressive (AR) method to predict the spatial covariance matrix of any frequency band based on that of another frequency band. Furthermore, we emphasize that channels across different frequency bands share similar APS characteristics. Leveraging the maximum entropy (ME) criterion, we develop a low-complexity algorithm for high-resolution APS estimation. Simulation results validate the effectiveness of the AR-based covariance prediction method and demonstrate the highresolution estimation capability of the ME-based approach. Furthermore, we demonstrate the effectiveness of multi-frequency cooperative transmission by applying the proposed methods to obtain statistical CSI from low-frequency bands and utilizing it for high-frequency channel transmission. This approach significantly enhances high-frequency transmission performance while effectively reducing system overhead.

Hybrid digital-analog precoding is essential for balancing communication performance, energy efficiency, and hardware costs in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most existing designs rely on the Shannon capacity and assume infinite blocklengths, which are impractical for emerging applications, such as massive machine-type communications, operating with finite blocklength (FBL). To address this gap, this paper pioneers a novel hybrid precoding design for mmWave massive MIMO in the FBL regime. We meticulously optimize hybrid precoding based on both the weighted sum-rate (WSR) and the max-min fairness (MMF) criteria, while fulfilling the transmit power budget and users’ minimum rate requirements. Both continuous and discrete phase shifters are considered for analog precoding. The formulated optimization problems are highly challenging to solve due to the nonconvex objective functions and nonconvex constraints. These challenges are further intensified by the nonconcave FBL rate function and the intricate coupling between analog and digital precoders. By proposing novel problem transformation and decomposition techniques, we reformulate the original complex problems into forms solvable with the penalty dual decomposition (PDD) method. We then develop two efficient iterative algorithms with parallel, and even closed-form variable updates, and guaranteed convergence to solve the WSR and MMF optimization problems, applicable to both continuous and discrete phase shifters. Simulation results show that our proposed hybrid precoding designs significantly outperform several baseline schemes, especially those adopting the Shannon capacity and infinite blocklength. Additionally, our proposed optimization algorithms enable hybrid precoding exploiting discrete phase shifters with limited quantization resolution (e.g., 3-bit) to closely match the performance of fully digital precoding in FBL scenarios.

Low Earth Orbit (LEO) satellite communication is a critical component in the development of sixth generation (6G) networks. The integration of massive multiple-input multipleoutput (MIMO) technology is being actively explored to enhance the performance of LEO satellite communications. However, the limited power of LEO satellites poses a significant challenge in improving communication energy efficiency (EE) under constrained power conditions. Artificial intelligence (AI) methods are increasingly recognized as promising solutions for optimizing energy consumption while enhancing system performance, thus enabling more efficient and sustainable communications. This paper proposes approaches to address the challenges associated with precoding in massive MIMO LEO satellite communications. First, we introduce an end-to-end graph neural network (GNN) framework that effectively reduces the computational complexity of traditional precoding methods. Next, we introduce a deep unfolding of the Dinkelbach algorithm and the weighted minimum mean square error (WMMSE) approach to achieve enhanced EE, transforming iterative optimization processes into a structured neural network, thereby improving convergence speed and computational efficiency. Furthermore, we incorporate the Taylor expansion method to approximate matrix inversion within the GNN, enhancing both the interpretability and performance of the proposed method. Numerical experiments demonstrate the validity of our proposed method in terms of complexity and robustness, achieving significant improvements over state-of-the-art methods.

Blockchain-based decentralized applications (DApps) offer enhanced security and decentralization features; nevertheless, their maintenance demands substantial computational resources and poses challenges for deployment in mobile networks. To address this, a number of studies have explored offloading blockchain mining tasks from mobile users to mobile edge computing (MEC) servers. However, the existing literature overlooks the fact that MEC servers can not only mine for mobile users but also mine for themselves, potentially explaining why MEC mining offloading has not gained broad acceptance within the industry. In this work, we exploit a more practical case and rethink the question of whether MEC servers lease computing power to mobile users by taking into account that MEC servers can mine for themselves. We establish a game model and apply backward induction to analytically characterize Nash equilibria for mining strategies adopted by MEC and mobile users. Our findings suggest that, if MEC can mine for self, MEC would mine for mobile users only under specific conditions where mobile users possess superior information gathering capability (at least better than the MEC server) or the whole blockchain system exhibits significant network value. We further provide a series of simulations to verify our conclusion and illustrate the impact of network parameters on the strategies of both sides.

Accurate channel estimation is essential in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. As the number of base station antennas further increases, high dimensional channel estimation becomes challenging. To tackle this problem, we first introduce window functions into received signal model to obtain the windowed received signal model. The channel estimation is formulated as obtaining the$\mit {a} \;\mit {posteriori}$distribution’s marginals of the beam domain channel within the information geometry (IG) framework. The calculation of the marginals is turned into an iterative projection process. We derive the windowed information geometry (WIG) approach by calculating an approximate solution of the projection in the presence of window functions. To reduce the computational complexity, we further propose a low-complexity calculation method for WIG. Specifically, by choosing suitable window functions, we can transform the original matrix multiplications into sparse matrix multiplications, which dramatically reduces the complexity. Furthermore, by adjusting the values of thresholds used in the low-complexity calculation method, we can make a superior trade-off between complexity and performance of WIG. Finally, complexity and performance of WIG are evaluated through simulations. Simulation results show that WIG can achieve similar performance to the simplified IG (SIG) while the complexity is much lower.

With the advancement of communication technology and the improvement of localization accuracy, cellular networks are gradually evolving from communication to perception-integrated networks. Addressing the research challenges of sensing-assisted communication, we propose, for the first time, the concept of Digital Twin of Channel (DToC). Specifically, we regard user terminal (UT) positions as physical objects, and statistical channel state information (CSI) as virtual digital objects. Observing the change trend of UTs’ statistical CSI caused by the changes of UT’s physical position enables predictive analytics for subsequent communication tasks. Then, we establish the relationship between physical and virtual digital objects using a Diffusion Model (DM) to achieve the DToC. Indeed, the DM can generate the desired objects by gradually denoising from noisy data using neural networks. Furthermore, we propose a conditional DM utilizing UTs’ positions, which completes the task of generating the corresponding statistical CSI under known user-specific position conditions, thus mapping UT positions to statistical CSI. Simulation results demonstrate that our DToC framework outperforms previous statistical CSI estimation methods. Without the need of pilots, our method can simultaneously generate statistical CSIs from a large number of UTs’ positions, achieving satisfactory results.

Channel knowledge map (CKM) has received widespread attention as an emerging enabling technology for environment-aware wireless communications. It involves the construction of databases containing location-specific channel knowledge, which are then leveraged to facilitate channel state information (CSI) acquisition and transceiver design. In this context, a fundamental challenge lies in efficiently constructing the CKM based on a given wireless propagation environment. Most existing methods are based on stochastic modeling and sequence prediction, which do not fully exploit the inherent physical characteristics of the propagation environment, resulting in low accuracy and high computational complexity. To address these limitations, we propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image-to-image (I2I) inpainting task, which predicts the channel knowledge at a specific location by recovering the corresponding pixel value in the image matrix. Then, inspired by the reversible and closed-form structure of the LP, we show its natural suitability for our task in designing a fast I2I mapping network. For different frequency components of LP decomposition, we design tailored networks accordingly. Besides, to encode the global structural information of the propagation environment, we introduce self-attention and cross-covariance attention mechanisms in different layers, respectively. Finally, experimental results demonstrate that the proposed scheme outperforms the benchmark, achieving higher reconstruction accuracy while with lower computational complexity. Moreover, the proposed approach has a strong generalization ability and can be implemented in different wireless communication scenarios.

Hostile jamming and eavesdropping threats bring severe challenges to reliable and secure communication demands of future networks. In light of the potentials of high-altitude platform (HAP) providing wide communication coverage with low cost and Ad-hoc network facilitating flexible access without support by hardware infrastructure, this paper proposes a multi-HAPs assisted air-terrestrial integrated Ad-hoc networks (HAIN) framework to defend against jamming and eavesdropping simultaneously. Specifically, the HAPs align the beamformer to the terrestrial users while nullifying the reception of eavesdropper. In addition, the Ad-hoc network enables cooperative anti-jamming transmission, where the cooperative users (CUs) provide communication assistance by forming anti-jamming coalition for blocked users (BUs). Building upon this framework, we aim to maximize the sum rate of BUs by jointly optimizing the beamforming and cooperative coalition formation with the imperfect channel state information (CSI). To handle the intractable problem, we first convert the imperfect CSI into the worst-case one, and then a sequential convex approximation combined with first order Taylor series expansion is proposed to optimize the beamforming. Furthermore, for the optimization of anti-jamming coalition formation, we reformulate it as the coalition formation game (CFG) and a partial best coalition preference order is put forward to enhance the sum rate of BUs. With the help of exact potential game (EPG), it’s proved that the CFG can converge to stable coalition formation by exploiting the proposed distributed anti-jamming coalition formation algorithm. Simulation results demonstrate that the proposed scheme has the superior secure transmission performance to benchmark schemes.

In high-speed railway (HSR) communications, the channel suffers from severe Doppler and channel aging effects caused by the high mobility, making the channel outdated quickly. To address this issue, we investigate the robust precoder design against channel aging and prediction inaccuracy in massive multiple-input multiple-output (MIMO) systems with matrix manifold optimization. First of all, we introduce the concept of the quadruple beams (QBs), and establish a QB based channel model with sampled quadruple steering vectors. Then, the upcoming space domain channel of interest can achieve a higher accuracy by channel prediction with the estimated QB domain channel. To further improve the performance while save the pilot overhead, we predict the forthcoming QB domain channel and integrate the prediction inaccuracy within the a posterior QB domain statistical channel model. Then, we consider the robust precoder design aiming to maximize the upper bound of the ergodic weighted sum-rate (WSR) on the Riemannian submanifold formed by the precoders satisfying the total power constraint (TPC). Riemannian ingredients are derived for matrix manifold optimization, with which the Riemannian conjugate gradient (RCG) method is proposed to solve the unconstrained problem on the manifold. The RCG method mainly involves the matrix multiplication and avoids the need of matrix inversion of the transmit antenna dimension. The simulation results demonstrate the effectiveness of the proposed channel model and the superiority of the RCG method for robust precoder design against channel aging and prediction inaccuracy.

In this paper, we investigate the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint angle-distance (AD) domain-based spherical-wavefront physical channel model that captures the inherent sparsity of XL-MIMO channels in the NF region. Leveraging the sparsity of the channel, the CE is approached as a task of reconstructing sparse signals. Anchored in this framework, we first propose a compressed sensing algorithm to acquire a preliminary channel estimation. Harnessing the powerful latent representation capability of generative artificial intelligence (GenAI), we further propose a GenAI-based approach to refine the estimated channel by employing advanced image denoising techniques. Specifically, we perceive the estimated channel as a noisy color image. Then, we derive the evidence lower bound (ELBO) of the design objective utilizing variational inference and reparameterization techniques, and propose a generative diffusion probabilistic model (GDM) dedicated to denoising. Experimental results indicate that the proposed GDM is capable of offering substantial performance gain in CE compared to existing benchmark approaches in NF XL-MIMO systems.

This paper investigates multi-user uplink transmission facilitated by holographic planar arrays (HPAs). It includes channel modeling and holographic precoding aimed at maximizing system spectral efficiency (SE) in scenarios where both users and base station are equipped with HPAs. First, we develop a holographic multiple-input multiple-output (MIMO) channel model utilizing electromagnetic field equations. Subsequently, we utilize Fourier space basis functions to discretize the continuous holographic MIMO model. Based on the discretized model, we formulate the problem of maximizing the system SE and propose a corresponding iterative water-filling algorithm to tackle it. Finally, we validate the effectiveness of the proposed scheme in enhancing the system SE and investigate the influence of physical size constraints of the transmitting and receiving arrays through simulation results.

In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization. In UCN mMIMO systems, each user terminal (UT) is served by a subset of the base stations (BSs) instead of all BSs, lowering the dimension of the precoders to be designed. Each BS in the system has a power constraint. By proving that the precoder set satisfying the constraints forms a Riemannian submanifold, we transform the constrained precoder design problem in Euclidean space as an unconstrained one on the Riemannian submanifold. Riemannian ingredients, including orthogonal projection, Riemannian gradient, retraction and vector transport, of the problem on the Riemannian submanifold are further derived, with which the Riemannian conjugate gradient (RCG) design method is proposed for solving the unconstrained problem. The proposed method avoids the inverses of large dimensional matrices. The complexity analyses show the high efficiency of RCG precoder design. Simulation results demonstrate the superiority of the proposed precoder design and the high efficiency of the UCN mMIMO system.

In this paper, we propose time-frequency phase-shifted pilots (TFPSPs) for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) channel estimation. We first present a triple-beam (TB) based channel model, establishing the relationship between the space-frequency-time (SFT) domain channel and the TB domain channel. By leveraging the specific characteristics of TB domain channels, we develop TFPSPs, where distinct pilot signals are simultaneously transmitted in the frequency and time domains. Then, we present the optimal condition on TFPSP, indicating that the optimal channel estimation performance can be achieved if the TB domain channel power distributions of different UTs do not overlap with each other by scheduling TFPSPs properly. Based on this optimal condition, we propose a low-complexity pilot scheduling algorithm. Simulation results demonstrate that, compared with conventional pilot design approaches, the proposed TFPSP approach effectively improves the accuracy of channel estimation, particularly in scenarios involving a significant number of UTs.

With the development of the upcoming sixth-generation (6G) wireless networks, there is a pressing need for innovative technologies capable of satisfying heightened performance indicators. Fluid antenna system (FAS) is proposed recently as a possible technique to achieve higher data rates and more diversity gains by dynamically changing the positions of the antennas to form a more desirable channel. However, worries regarding the possibly harmful effects of electromagnetic (EM) radiation emitted by devices have arisen due to the rapid evolution of advanced techniques in wireless communication systems. Specific absorption rate (SAR) is a widely adopted metric to quantify EM radiation worldwide. In this paper, we investigate the FAS-assisted multiuser multiple-input multiple-output (MIMO) communications with SAR constraints. In particular, an efficient algorithm is proposed to maximize the minimum weighted signal-to-interference-plus-noise ratio (SINR) under SAR and FAS constraints. Simulation results verify that the proposed SAR-aware FAS design outperforms the adaptive backoff and fixed-position antenna designs.

Non-orthogonal multiple access (NOMA) has been regarded as a promising technology for next-generation wireless networks. Most of the existing NOMA schemes are designed with the assumption of infinite blocklength (IBL), which may lead to suboptimal performance in practice. This paper investigates the reliability-oriented downlink multiple-input multiple-output (MIMO)-NOMA design with finite blocklength (FBL) transmission. Specifically, we formulate the resource allocation problem to minimize the average block error rate (BLER) of FBL-MIMO-NOMA systems. We obtain the globally optimal solutions to the problem by proposing an efficient power, rate, and blocklength allocation scheme. The superiority of our proposed design is demonstrated via simulation results.

Large residual carrier frequency offset (CFO) can severely degrade the performance of orthogonal frequency division multiplexing (OFDM) wireless communication systems when high-order modulations are adopted. In this paper, we propose a convolution neural network (CNN) enabled expectation-maximization (EM) algorithm which can blindly estimate residual CFO without extra pilots. Specifically, we first show that the effects of the residual CFO can be depicted by the phase shift existing in the equalized signal. Based on this model, we design a simple CNN to get a rough estimate of the phase shift. The output of the CNN is further used to initialize an EM algorithm. With this fine initialization, the EM algorithm can iteratively seek better estimates of the phase shift induced by the residual CFO. The combination of CNN and EM algorithm simplifies neural network design while maintaining the accuracy of the estimation. Numerical simulations verify the efficiency of the proposed method.

In this paper, we prove the convergence of the simplified information geometry approach (SIGA), which was proposed for massive MIMO-OFDM channel estimation. For a general Bayesian inference problem, we first show that the iteration of the common second-order natural parameter (SONP) is separated from that of the common first-order natural parameter (FONP). Hence, the convergence of the common SONP can be checked independently. We show that with the initialization satisfying a specific but large range, the common SONP is convergent regardless of the value of the damping factor. For the common FONP, we establish a sufficient condition of its convergence and prove that the convergence of the common FONP relies on the spectral radius of a particular matrix related to the damping factor. We give the range of the damping factor that guarantees the convergence in the worst case. Further, we determine the range of the damping factor for massive MIMO-OFDM channel estimation by using the specific properties of the measurement matrices. Simulation results are provided to confirm the theoretical results.

In this paper, we study the coordinated multicast and unicast transmission for cellular-based vehicle-to-vehicle (C-V2V) massive multiple-input multiple-output (MIMO) in the scenario where only imperfect channel state information is available at the base station side and the transmitter in each V2V communication pair. The precoding design problem is a weighted ergodic sum-rate maximization problem with the imperfect CSI including channel mean and variance information known, and the objective function is the weighted sum of the achievable ergodic unicast rate for all the V2V pairs and the achievable ergodic multicast rate for all the cellular users. The minimize-maximize (MM) algorithm and the deterministic equivalent method are utilized to solve the problem and reduce the computational complexity. The simulation results demonstrate the significant improvements of the proposed coordinated communication method in the system spectral efficiency.

As a promising technology for future networks, non-orthogonal multiple access (NOMA) has drawn great attention for its enhanced throughput with massive connectivity. Most of the existing NOMA schemes employ the ideal information rate as the performance metric, assuming perfect successive interference cancellation (SIC), which may lead to suboptimal performance in practice. In this paper, by considering the imperfect SIC and practical modulation schemes, we propose a power control scheme for uplink NOMA systems with the aim of maximizing the effective throughput. To address this problem, we first analyze the symbol error probabilities of two users employing the quadrature amplitude modulation (QAM) scheme, and then a specific expression of the effective system throughput is provided, which considers both the error performance and the data rate. Considering the complicated effective throughput expression of the uplink NOMA design, we next derive a lower bound of the effective throughput and consequently obtain an efficient power control scheme in closed form by maximizing the lower bound of the effective throughput. Numerical results are provided to demonstrate the superiority of our proposed scheme.

Channel knowledge map (CKM) has received widespread attention as an emerging enabling technology for environment-aware wireless communications. It involves the construction of databases containing location-specific channel knowledge, which are then leveraged to facilitate channel state information (CSI) acquisition and transceiver design. In this paper, we propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. Specifically, we first view the channel knowledge as a 2-D image and transform the CKM construction problem into an image to image (I2I) inpainting task, which predicts the channel knowledge at specific location by recovering the corresponding pixel value in the image matrix. Then, inspired by the reversible and closed-form frequency band decomposition structure of the LP, we design tailored subnetworks for different frequency components. In addition, to encode the global structural information of the propagation environment, we introduce self-attention and cross-covariance attention mechanisms in different layers, respectively. Experiments demonstrate that the proposed scheme can accurately reconstruct the CKM with low computational complexity. Moreover, the proposed method has a strong generalization ability to be implemented in different wireless communication scenarios.

We investigate the weighted sum-rate (WSR) max-imization linear precoder design under total power constraint (TPC) for massive MIMO downlink with matrix manifold optimization. Particularly, we prove that the precoders under TPC are on a Riemannian submanifold, and transform the constrained problem in Euclidean space to the unconstrained one on manifold. In accordance with this, Riemannian design methods using Riemannian steepest descent and Riemannian conjugate gradient are provided to design the WSR-maximization precoders under TPC. Riemannian methods are free of the inverse of large dimensional matrix, posing significant computational savings and potentially allowing to avoid ill numerical behavior in algorithms. Complexity analysis and performance simulations demonstrate the advantages of the proposed precoder design.

Symbol-level precoding exploits the symbol constellation structure and transforms multi-user interference into a useful signal by taking into account both channel state information and data symbols, which has recently emerged as a novel paradigm for future wireless communications. The symbol-level precoding highly depends on the modulation and less works consider PAM and QAM constellations. In this paper, we focus on PAM and QAM constellations and propose a symbol-level precoding design to minimize the symbol error probability (SEP). We first analyze the SEP for PAM and QAM symbols and derive an upper bound of SEP. To minimize the SEP for all users, we directly design the transmitted signal, which is a linear combination of channel vectors. In addition, to reduce the complexity brought by the SEP function, we further relax the SEP expression and propose a near-optimal solution of the transmitted signals. Simulation results demonstrate that the proposed scheme achieves superior performance than other existing precoding schemes in terms of SEP.

We investigate massive multiple-input multiple-output (MIMO) downlink (DL) transmission for multiple low-earth-orbit (LEO) satellite communication systems. We establish the signal and channel models, and reveal that the signals received by each user terminal (UT) are typically asynchronous in time and frequency. We propose a spatial linear receive processing for signal extraction and perform time and frequency compensations at each UT to achieve synchronized signal. We show that the single data stream transmission from each satellite to each UT is optimal to maximize the ergodic sum rate. Therefore, without loss of optimality, we can reduce the joint design of transmit covariance matrices and receive vectors for spatial linear processing to that of the precoding vectors and receive vectors, which we refer to as joint precoder and receiver design (JPRD). We devise a weighted minimum mean-square error (WMMSE) based JPRD algorithm by using the statistical channel state information. Simulation results validate the proposed approaches.

In this paper, we investigate multi-cell resource management for massive MIMO communications, where users within different cells reuse the same time-frequency resource further aggravating the inter-user interference. Based on re-inforcement learning, which has been a useful technique for resource management, we propose a joint user grouping and time-frequency resource allocation algorithm (JUGRA). JUGRA contains user grouping network (UGNet) and time-frequency resource allocation network (TFRANet). TFRANet is used to select time-frequency resource allocation scheme, which maximizes system throughput with fixed user grouping scheme. UGNet is built to select the multi-cell user grouping scheme and calls TFRANet to evaluate the system performance, realizing joint optimization of user grouping and resource allocation. Simulation results demonstrate that the performance of JUGRA can be improved by more than 30% over the greedy algorithm with lower computational complexity. The proposed time-frequency resource allocation algorithm based on TFRANet can achieve 95 % of exhaustive search performance.

In massive Machine Type Communication (mMTC), an enormous number of user equipments (UEs) try to access the network in a short period. It is usually challenging to support such massive access with limited resources. Fortunately, directional beams can offer spatial degrees of freedom for random access (RA). In this article, a novel technique called beam splitting/merging is proposed to improve the performance of RA in mMTC network slice with limited resources such as preambles. We describe the framework of beam splitting/merging with the preambles reused, derive the RA throughput of the split beams which is approximately linearly increased with the number of beams, and give a method to obtain beam configuration ensuring the global RA success probability. With the derived analytic results, we further give an analysis of beam configuration in a typical scenario of beam splitting. Moreover, we design an RA control algorithm based on beam splitting/merging technique, by which the mMTC slice can dynamically provision RA capacity according to current access intensity. The updated RA configuration of each beam is broadcast by system information [e.g., system information block 1 (SIB1)]. Detailed analysis and simulation results show that the proposed beam splitting/merging algorithm can effectively improve system efficiency and guarantee RA performance of mMTC network slice with little increase of signaling overhead and delay. The proposed beam splitting/merging framework is applicable to both grant-based and grant-free RA procedures.

Wireless positioning is crucial for Internet of Things (IoT) landscape, enhancing precision and reliability in location-based services. This article addresses the challenges of existing massive multiple-input–multiple-output fingerprint positioning methods, which typically require accurate channel estimation and one-by-one labeled data sets. We propose a semisupervised representation contrastive learning technique that leverages a partially labeled received pilot signal data set readily available from the base station. Our approach employs data augmentation to generate a large number of positive and negative sample pairs, which are then used to pretrain an encoder with a contrastive loss function in the self-supervision way. During pretraining, the encoder learns to encode positive samples close to an anchor, while keeping negative samples far away in the representation space. A fully connected layer is added on top of the encoder for position regression, and the encoder and regression networks are fine-tuned with a small labeled subdataset for the downstream positioning task. Simulation results demonstrate that our pretraining and fine-tuning approach outperforms the previous methods, significantly improving positioning accuracy, avoiding exact channel estimation and achieving labeling efficiency.

In this paper, we address robust precoding in high-frequency (HF) skywave massive multiple-input multiple-output (MIMO) systems with imperfect channel state information (CSI). We first employ a sparse beam baseda posteriorichannel model and demonstrate that robust precoding can be efficiently solved in the Slepian transform domain with a large number of base station (BS) antennas. Next, we introduce two Slepian transform based robust precoding methods, including a joint approach that leverages inverse fast Fourier transform (IFFT) for reduced complexity with a large number of user terminals (UTs). We then establish a local optimum for the Slepian transform domain robust precoder (STRP) design using the majorization minimization (MM) algorithm, taking advantages of HF skywave massive MIMO channel sparsity and Slepian sequence properties. Further, two distinct designs are presented: separate STRP (SSTRP) and joint STRP (JSTRP). Simulation results confirm the effectiveness of proposed robust precoders, showcasing their excellent ergodic sum-rate performance and low complexity.

We investigate massive multiple-input multiple-output (MIMO) downlink (DL) transmission for multiple low-earth-orbit satellite communication systems. We establish the signal and channel models, and reveal that the signals received by each user terminal (UT) are typically asynchronous in time and frequency. We propose a spatial linear receive processing for signal extraction and perform time and frequency compensations at each UT to achieve synchronized signal. We prove that the single data stream transmission from each satellite to each UT is optimal to maximize the ergodic sum rate. Therefore, without loss of optimality, we can reduce the joint design of the transmit covariance matrices and receive vectors for spatial linear processing to that of the precoding vectors and receive vectors, which we refer to as joint precoder and receiver design (JPRD). We devise a weighted minimum mean-square error (WMMSE) based JPRD algorithm by using the statistical channel state information. Further, we approximate the optimal design with an ergodic sum rate upper bound, for which the optimality of single data stream transmission still holds. We derive a condition under which the inter-satellite interference can be eliminated, and develop a low-complexity WMMSE based JPRD algorithm with the upper bound. Simulation results validate the proposed approaches.

The advent of blockchain technology in wireless networking has spawned a novel decentralized paradigm that engenders the establishment of multi-party trust, secure and efficient resource sharing, and equitable distribution of benefits. However, blockchain often results in lengthy access latencies and can hardly guarantee the quality of off-chain services. The problems stem from the intrinsic flaws of blockchain in dealing with off-chain services such as wireless access. In this study, we propose a unified framework that guarantees service quality and low establishment latency for blockchain-based systems, and incentivizes clients and service providers (SPs) to cooperate without a trusted third party. We use wireless access service in blockchain radio access network (B-RAN) as a typical example and provide a detailed implementation design. We model the service delivery process in our framework and identify the necessary condition for achieving trusting cooperation. Interestingly, we find the necessary condition is also sufficient for cooperation if the service framework is properly designed. Furthermore, we uncover a trade-off between system robustness and transmission efficiency, which offers insightful guidance for framework design in practice. Finally, we present simulation results to illustrate the effectiveness of our proposed wireless access scheme.

In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two-dimensional (2D) beam-based statistical channel model (BSCM) in massive multi-input multi-output (MIMO) systems. The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple received pilot signals. A received signal model showing the relation between the statistical properties of the received pilot signals and the BDCPMs is derived. On the basis of the received signal model, we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without the estimates of instantaneous CSI is proposed. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application,i.e., estimation of the angle domain channel power matrix. Simulation results show that the proposed method has good convergence and can obtain sparse BDCPMs. Compared with the regularized multiple measurement vector focal underdetermined system solver (RM-FOCUSS) algorithm, the proposed algorithm obtains overall more accurate statistical CSI with much lower complexity and brings significant performance gains when used in channel estimation.

With antenna spacing much less than half a wavelength in confined space, holographic multiple-input multiple-output (HMIMO) technology presents a promising frontier in next-generation mobile communication. We delve into the research of the multi-user uplink transmission with both the base station and the users equipped with holographic planar arrays. To begin, we construct an HMIMO channel model utilizing electromagnetic field equations, accompanied by a colored noise model that accounts for both electromagnetic interference and hardware noise. Since this model is continuous, we approximate it within a finite-dimensional space spanned by Fourier space series, which can be defined as the communication mode functions. We show that this channel model samples Green’s function in the wavenumber domain in different communication modes. Subsequently, we tackle the challenging task of maximizing the spectral efficiency (SE) of the system, which involves optimizing the continuous current density function (CDF) for each user. Using the aforementioned approximation model, we transform the optimization variables into expansion coefficients of the CDFs on a finite-dimensional space, for which we propose an iterative water-filling algorithm. Simulation results illustrate the efficacy of the proposed algorithm in enhancing the system SE and show the influence of the colored noise and the system parameters on the SE.

In this paper, we investigate signal detection for HF skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce beam based channel models (BBCM) in the space domain at each subcarrier and in the space-frequency domain for all subcarriers. Based on the BBCM in the space domain, we propose a beam structured detector (BSD) for each subcarrier. Specifically, we prove that the space domain detector design can be transformed into that of a beam domain detector without sacrificing optimality, and the asymptotically optimal space domain detector is beam structured with a low-dimensional beam domain detector, thus significantly reducing the design and implementation complexities. Furthermore, we extend the BSD to the space-frequency domain based on the BBCM jointly for all subcarriers. The design of space-frequency domain detector is also converted to that of a low-dimensional beam domain detector, which enables a very efficient design and implementation of BSD. Simulation results demonstrate the low complexity and satisfactory performance of the proposed detectors.

In this paper, we investigate high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. Based on the triple-beam (TB) based channel model and the channel sparsity in the TB domain, we propose a beam structured channel estimation (BSCE) approach. Specifically, we show that the space-frequency-time (SFT) domain estimator design for each TB domain channel element can be transformed into that of a low-dimensional TB domain estimator and the resulting SFT domain estimator is beam structured. We also present a method to select the TBs used for BSCE. Then we generalize the proposed BSCE by introducing window functions and a turbo principle to achieve a superior trade-off between complexity and performance. Furthermore, we present a low-complexity design and implementation of BSCE by exploiting the characteristics of the TB matrix. Simulation results validate the proposed theory and methods.

The radio environment map (REM) visually displays the spectrum information over the geographical map and plays a significant role in monitoring, management, and security of spectrum resources. In this paper, we present an efficient 3D REM construction scheme based on the sparse Bayesian learning (SBL), which aims to recover the accurate REM with limited and optimized sampling data. In order to reduce the number of sampling sensors, an efficient sparse sampling method for unknown scenarios is proposed. For the given construction accuracy and the priority of each location, the quantity and sampling locations can be jointly optimized. With the sparse sampled data, by mining the sparsity of the spectrum situation and channel propagation characteristics, a SBL-based spectrum data hierarchical recovery algorithm is developed to estimate the missing data of unsampled locations. Finally, the simulated three-dimensional (3D) REM data in the campus scenario are used to verify the proposed methods as well as to compare with the state-of-the-art. We also analyze the recovery performance and the impact of different parameters on the constructed REMs. Numerical results demonstrate that the proposed scheme can ensure the construction accuracy and improve the computational efficiency under the low sampling rate.

Extremely large-scale multiple-input multiple-output (XL-MIMO) constitutes the design trend for base stations of future wireless communication systems, being capable of offering pencil-like beamforming that confronts path loss in an energy-efficient manner. However, wideband wireless applications with XL-MIMO antenna arrays are usually subject to near-field signal propagation conditions, frequency selectivity, and the spatial-wideband effect, whose ignorance in the beamforming optimization process will severely degrade the achievable performance. In this paper, we present an algorithmic framework for designing near-field reception beamforming of wideband multi-user XL-MIMO systems realized with holographic metasurface-based antenna arrays (HMAs). We first present a spherical-wave-propagation channel model, including the near-field effect, frequency selectivity, as well as the spatial-wideband effect. Based on this model, we formulate an HMA-based reception beamforming optimization problem for the uplink of multi-user XL-MIMO communications, whose optimal solution is challenging to obtain due to the nonlinear coupling between the high-dimensional analog combining weights and the digital combiner. To efficiently address the proposed framework via a convergent iterative approach, the considered sum-rate design objective is transformed into a sum-mean-square-error-minimization one. Our extensive numerical investigations showcase that the proposed HMA-based combining scheme can effectively deal with the practical effects under investigation, achieving a higher sum rate than conventional phase-shifter-based hybrid analog and digital combiners having the same antenna aperture.

Integrated communications and localization (ICAL) will play an important part in future sixth generation (6G) networks for the realization of Internet of Everything (IoE) to support both global communications and seamless localization. Massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems have great potential in providing wide coverage with enhanced gains, and thus are strong candidates for realizing ubiquitous ICAL. In this paper, we develop a wideband massive MIMO LEO satellite system to simultaneously support wireless communications and localization operations in the downlink. In particular, we first characterize the signal propagation properties and derive a localization performance bound. Based on these analyses, we focus on the hybrid analog/digital precoding design to achieve high communication capability and localization precision. Numerical results demonstrate that the proposed ICAL scheme supports both the wireless communication and localization operations for typical system setups.

Massive multiple-input multiple-output (MIMO) multicasting is a promising approach for simultaneously delivering common messages to multiple users in next-generation wireless networks. However, existing studies have exclusively focused on multicast beamforming designs based on the Shannon capacity, assuming the infinite blocklength (IBL) for transmission. This assumption may lead to strictly suboptimal designs for practical multicast transmissions with finite blocklength (FBL), especially in ultra-reliable low-latency communications. In this paper, we explore the beamforming design for massive MIMO multi-group multicasting in the FBL regime. Our study considers both the max-min fairness and the weighted sum rate criteria for a comprehensive treatment. Due to the non-concave FBL rate function, the resulting optimization problems are known to be notoriously hard. We characterize the necessary and sufficient condition for the non-negative FBL rate to be a concave function of the received signal-to-interference-plus-noise ratio (SINR). Considering a finite number of transmit antennas, we propose low-complexity majorization-minimization (MM) type algorithms, which update variables in either closed or semi-closed form, to achieve locally optimal solutions of the formulated optimization problems. We further show that, as the number of transmit antennas becomes large, the optimal beamformer of each group aligns asymptotically with a linear combination of the channel vectors of that group of users, where the optimal normalized combining coefficients are derived in closed form. Subsequently, we obtain the globally optimal multicast beamformers by optimizing the power allocation using low-complexity iterative algorithms. Simulation results show that the proposed schemes outperform several existing methods, especially those employing the Shannon capacity as the performance metric. Moreover, the proposed algorithms exhibit complexities that only slightly grow with the number of transmit antennas and they can notably reduce the computation time by up to two orders of magnitude over the benchmarks, making them highly beneficial for massive MIMO applications.

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for enabling massive connectivity and achieving high spectral efficiency in future wireless communication. In the literature, most of the existing NOMA schemes are designed with the assumption of infinite blocklength, which may lead to suboptimal performance in practical communication systems. Thus, this paper investigates the downlink NOMA system with finite blocklength (FBL) transmission, namely the downlink FBL-NOMA system. We focus on developing a joint resource allocation scheme from single-channel to multi-channel systems, aiming at minimizing the average block error rate (BLER) for the downlink FBL-NOMA. Specifically, for single-channel systems, the optimal rate and power allocation scheme are derived in semi-closed form. For multi-channel systems, we conduct the convexity analysis of the minimum BLER problem and provide the optimal solution in waterfilling form for convex cases, as well as an efficient majorization-minimization (MM)-based algorithm for general cases. We also propose an efficient method to jointly optimize channel assignment, rate allocation, and power allocation for multi-channel FBL-NOMA systems. Simulation results demonstrate the superiority of the proposed designs over the existing NOMA and orthogonal multiple access (OMA) schemes in terms of reliability and efficiency.

The future sixth generation (6G) networks will feature great importance on the integration of communications and localization, to realize the Internet of Everything (IoE). In this paper, we investigate the hybrid precoding design for the integrated communications and localization (ICAL) in the massive multiple-input multiple-output (MIMO) low Earth orbit (LEO) systems. In particular, we first derive an upper bound of the communication spectral efficiency (SE) and the squared position error bound (SPEB) of localization. Then, we formulate a multi-objective optimization problem to simultaneously operate communications and localization. Simulation results demonstrate the satisfactory performance of the proposed massive MIMO LEO ICAL system for typical setups.

The current multiple-input multiple-output (MIMO) systems are still mainly implemented based on the centralized architecture, which thus has to process a huge amount of base-band data. In particular, the central processing unit (CPU) needs a large bus bandwidth to accommodate the prohibitive baseband data transmission, which hinders effective system implementation, especially for the massive MIMO systems. Moreover, the centralized scheme lacks flexibility and scalability when facing varying sizes of antenna arrays and diverse applications. This paper proposes an efficient decentralized bidirectional-chain (DBC) equalizer architecture. The advantages of the DBC architecture are two-fold. First, it can reduce the data traffic transmitted from the antennas to the processing unit by categorizing them into clusters, each of which is equipped with a local processing unit (LPU). Second, it can reduce the time delay by updating all clusters in parallel. To sufficiently exploit the proposed DBC architecture, we further propose efficient parallel iterative algorithms. The DBC-based parallelizable iterative algorithms achieve the state-of-the-art performance in terms of convergence rate and bit error rate. Finally, simulation results are provided to confirm the effectiveness and superiority of our proposal.

In this paper, we address the peak-to-average power ratio (PAPR) reduction problem of an orthogonal frequency division multiplexing (OFDM) system under error vector magnitude (EVM) and out-of-band emissions (OOBE) constraints. Unlike the existing methods, we first equivalently transform the non-convex objective function—PAPR into the difference between the numerator and denominator, and then analyze its monotonicity of the auxiliary variable. Subsequently, we propose a novel algorithm based on bisection searching to tackle the equivalent PAPR optimization problem. Furthermore, an efficient PAPR reduction algorithm is developed to overcome the challenge of high computational complexity, and a simplified version of this algorithm is provided. Finally, the performance of the proposed algorithms is compared with the state-of-the-art schemes. The simulation results demonstrate the superiority of the proposed algorithms.

The capacity region for single input single out broadcast channel (SISO BC) is achieved by non-orthogonal multiple access (NOMA), which utilizes successive interference cancellation (SIC) to mitigate the inter-user interference. However, the complexity of SIC is high. To balance between the sum rate performance and the complexity of receivers, in this paper, we explore rate splitting multiple access (RSMA) in visible light communications (VLC). We formulate the joint rate allocation and power control problem to maximize the sum-rate under both quality of service (QoS) and SIC constraints. To solve this non-convex problem, a successive convex approximation (SCA) based algorithm is proposed to obtain a local optimal solution. Numerical results show that 1-layer RSMA is able to achieve very close performance to NOMA with much reduced complexity. Jointly considering the performance and complexity of the system, 1-layer RSMA is an attractive alternative to NOMA in SISO VLC networks.

In this paper, we investigate signal detection for HF skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce beam based channel model (BBCM) in the space domain and reveal the sparsity of the channel in the space-beam domain. Based on the BBCM in the space domain, we propose a beam structured detector (BSD) for each subcarrier. Specifically, we prove that the space domain detector design can be transformed to that of a beam domain detector without sacrificing optimality, and the asymptotically optimal space domain detector is beam structured with a low-dimensional beam domain detector, thus significantly reducing the design and implementation complexities. Furthermore, we provide a beam selection criterion to choose the beams that are used for the BSD. Simulation results demonstrate the low complexity and satisfactory performance of the proposed detector.

In this paper, we investigate the channel estimation for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We revisit the information geometry approach (IGA) for massive MIMO-OFDM channel estimation. By using the constant magnitude property of the entries of the measurement matrix and the asymptotic analysis, we find that the second-order natural parameters (SONPs) of the distributions on all the auxiliary manifolds (AMs) are equivalent to each other at each iteration of IGA, and the first-order natural parameters (FONPs) of the distributions on all the AMs are asymptotically equivalent to each other at the fixed point. Motivated by these results, we simplify the iterative process of IGA and propose a simplified IGA for massive MIMO-OFDM channel estimation. It is proved that at the fixed point, the a posteriori mean obtained by the simplified IGA is asymptotically optimal. The simplified IGA allows efficient implementation with fast Fourier transformation (FFT). Simulations confirm that the simplified IGA can achieve near the optimal performance with low complexity in a limited number of iterations.

We propose a cost efficient cross-subcarrier pre-coder design (CSPD) for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) downlink with imperfect channel state information (CSI). To reduce the high computational complexity caused by individual precoder design for each subcarrier, we design transform domain precoding vectors (TDPVs), from which the precoders for a set of subcarriers can be obtained through a transform. The number of TDPVs is much less than that of subcarriers, and the number of total parameters to be designed can be reduced significantly. The main objective is to maximize an upper bound of the ergodic sum-rate by exploiting the a posteriori beam-based statistical channel model. We provide a concave minorizing function of the upper bound of the ergodic sum-rate and then derive the stationary points of a concave quadratic optimization problem with this minorizing function. To reduce the dimension of the matrix inversion in the stationary points, we propose an algorithm by using block coordinate descent (BCD) method with power allocation.

Multi-frequency communication is a potential strategy to overcome the limitations caused by frequency scarcity. This paper investigates the acquisition of statistical channel state information (CSI) in multi-frequency systems. We first analyze the multi-frequency channel model and reveal the relationship between spatial covariance matrices of different frequency bands. Based on the relationship, we propose a linear autoregressive (AR) method, directly establishing the mapping relationship of covariance elements between different frequency bands. In addition, with the acquired spatial covariance, we estimate APS with the maximum entropy (ME) criterion and use it to benefit downlink transmission. Simulation results verify the accuracy of the AR spatial covariance extrapolation method and show that the ME method can estimate APS with high resolution. Meanwhile, the results validate that the estimated statistical CSI can aid the realization of multi-frequency cooperative robust transmission.

This paper investigates the distributed precoding for network massive multi-input multi-output (MIMO) communications without data sharing between cells. In order to restrict the information exchange, which imposes significant requirements on signaling overhead, we first reformulate the original weighted sum-rate maximization problem into a cell-specific form. With this reformulated problem, we take a virtual weighted sumrate, whose expression only depends on precoders in a single cell and some initial values, as the objective function of an approximated problem. A stationary point of this non-concave virtual weighted sum-rate maximization problem is then achieved iteratively through the minorize-maximize (MM) algorithm. After exchanging a virtual covariance matrix generated locally, each base station (BS) can solely optimize its precoding matrix in parallel without any exchange during the optimization procedure. Numerical results show that the proposed method performs well in the sense of achievable sum-rate.

Over the past few years, the prevalence of wireless devices has become one of the essential sources of electromagnetic (EM) radiation to the public. Facing with the swift development of wireless communications, people are skeptical about the risks of long-term exposure to EM radiation. As EM exposure is required to be restricted at user terminals, it is inefficient to blindly decrease the transmit power, which leads to limited spectral efficiency and energy efficiency (EE). Recently, rate-splitting multiple access (RSMA) has been proposed as an effective way to provide higher wireless transmission performance, which is a promising technology for future wireless communications. To this end, we propose using RSMA to increase the EE of massive MIMO uplink while limiting the EM exposure of users. In particularly, we investigate the optimization of the transmit covariance matrices and decoding order using statistical channel state information (CSI). The problem is formulated as non-convex mixed integer program, which is in general difficult to handle. We first propose a modified water-filling scheme to obtain the transmit covariance matrices with fixed decoding order. Then, a greedy approach is proposed to obtain the decoding permutation. Numerical results verify the effectiveness of the proposed EM exposure-aware EE maximization scheme for uplink RSMA.

In this paper, a novel three-dimensional (3D) non-stationary massive multiple-input multiple-output (MIMO) channel model for shortwave communication systems is proposed. Three transmission modes, i.e., groundwave, near vertical incident skywave (NVIS), and long-distance skywave are considered to eliminate the blind area and realize the full-coverage for shortwave communication. The ionospheric absorption loss and surface reflection loss during multi-hop transmissions are explored in the proposed channel model. In addition, new massive MIMO channel characteristics including the near-field spherical wavefront effect and spatial non-stationarity are considered. Temporal and frequency non-stationarities are also modeled due to the receiver (Rx) mobility and large relative bandwidth, respectively. The analytical and simulated space cross-correlation function (SCCF), time autocorrelation function (TACF), and frequency correlation function (FCF) of the proposed model are compared. The simulated path loss and singular value spread (SVS) are compared with those of the corresponding channel measurements, illustrating good fittings. In addition, the delay power spectral density (PSD) and Doppler PSD, and channel capacity are also simulated and analyzed. The proposed model can be used as a basis for the design and construction of shortwave communication systems.

Visible light communication (VLC) emerges as a promising technology for the explosively growing wireless services and demands. However, the system performance is severely impaired by the inherent nonlinearity of the VLC channel. In existing studies, the Hammerstein and Wiener models are widely used and often assumed for VLC channels due to the simple structure and low complexity. Yet, their effectiveness remains unclear and controversial. This work aims to figure out which one between the Hammerstein and Wiener models is more suitable for characterizing the VLC channel. We first design a single-tone test for qualitative analysis and further conduct an experiment based on multi-level pseudorandom sequences for quantitative evaluation. From the two well-designed experiments, we obtain a consistent conclusion that the Hammerstein model is more proper for describing the VLC nonlinearity and also more effective for post-distortion in VLC systems.

In this paper, we investigate the downlink robust precoding with imperfect channel state information (CSI) for massive multiple-input-multiple-output (MIMO) communications. With the estimated channel and channel error statistics, the general design of the robust precoder is to maximize the ergodic sum rate subject to the total transmit power constraint. To make the problem more tractable, we find a lower bound of the ergodic sum rate and propose the robust weighted minimum mean-squared-error (WMMSE) precoder to maximize the bound. We characterize the structure of the precoding vectors by low-dimensional parameters, which are learned directly from the available CSI through a neural network. As such, the precoding vectors can be immediately computed without iterations. To extend the deep learning design to multi-antennas users, we present a flexible approach that allows the various antenna configurations at the user side to be handled. Simulation results show that the deep learning design can significantly reduce the computational complexity compared with the existing precoder designs while achieving near optimal performance.

Massive multiple-input multiple-output (MIMO) channels are distinctly characterized by their array non-stationarity, which has not been considered in the existing beam domain channel models (BDCMs). In this paper, the array non-stationarity of massive MIMO channels is modeled by the spatially consistent visibility regions (VRs) over a large uniform planar array (UPA) in terms of individual multipath components (MPCs). Based on this, a novel three-dimensional (3D) BDCM incorporating the effects of array non-stationarity is proposed. Statistical properties of the proposed BDCM including channel power, power leakage, space-time-frequency correlation function (STF-CF), and beam spread are derived. The ergodic and outage capacities are evaluated. The impacts of array non-stationarity on those statistics and channel capacity are analyzed. Results suggest that the beamwidths or spatial resolutions of the BDCM for different directions are not equal due to the array non-stationarity. This in turn increases the power leakage and correlation between channel elements and reduces the beam domain channel capacity.

In this paper, we investigate the massive multiple-input multiple-output orthogonal frequency division multiplexing channel estimation for low-earth-orbit satellite communication systems. First, we use the angle-delay domain channel to characterize the space-frequency domain channel. Then, we show that the asymptotic minimum mean square error (MMSE) of the channel estimation can be minimized if the array response vectors of the user terminals (UTs) that use the same pilot are orthogonal. Inspired by this, we design an efficient graph-based pilot allocation strategy to enhance the channel estimation performance. In addition, we devise a novel two-stage channel estimation (TSCE) approach, in which the received signals at the satellite are manipulated with per-subcarrier space domain processing followed by per-user frequency domain processing. Moreover, the space domain processing of each UT is shown to be identical for all the subcarriers, and an asymptotically optimal vector for the per-subcarrier space domain linear processing is derived. The frequency domain processing can be efficiently implemented by means of the fast Toeplitz system solver. Simulation results show that the proposed TSCE approach can achieve a near performance to the MMSE estimation with much lower complexity.

In this paper, we investigate channel acquisition for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce the concept of triple beams (TBs) in the space-frequency-time (SFT) domain and establish a TB based channel model using sampled triple steering vectors. With the established channel model, we then investigate the optimal channel estimation and pilot design for pilot segments. Specifically, we find the conditions that allow pilot reuse among multiple user terminals (UTs), which significantly reduces pilot overhead and increases the number of UTs that can be served. Moreover, we propose a channel prediction method for data segments based on the estimated TB domain channel. To reduce the complexity, we formulate the channel estimation as a statistical inference problem and then obtain the channel by the proposed constrained Bethe free energy minimization (CBFEM) based channel estimation algorithm, which can be implemented with low complexity by exploiting the structure of the TB matrix together with the chirp z-transform (CZT). Simulation results demonstrate the superior performance of the proposed channel acquisition approach.

Future wireless communications are largely inclined to deploy massive numbers of antennas at the base stations (BSs) by leveraging cost- and energy-efficient as well as environmentally friendly antenna arrays. The emerging technology of dynamic metasurface antennas (DMAs) is promising to realize such massive antenna arrays with reduced physical size, hardware cost, and power consumption. The goal of this paper is the optimization of the energy efficiency (EE) performance of DMA-assisted massive multiple-input multiple-output (MIMO) wireless communications. Focusing on the uplink, we propose an algorithmic framework for designing the transmit precoding of each multi-antenna user and the DMA tuning strategy at the BS to maximize the EE performance, considering the availability of either instantaneous or statistical channel state information (CSI). Specifically, the proposed framework is shaped around Dinkelbach’s transform, alternating optimization, and deterministic equivalent methods. In addition, we obtain a closed-form solution to the optimal transmit signal directions for the statistical CSI case, which simplifies the corresponding transmission design for the multiple-antenna case. Our numerical results verify the good convergence behavior of the proposed algorithms, and showcase the considerable EE performance gains of the DMA-assisted massive MIMO transmissions over the baseline schemes.

In this paper, we investigate the robust precoding with imperfect channel state information (CSI) for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. Starting with a sparse beam based a posteriori channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust precoder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization (MM). Furthermore, we develop a low-complexity BDRP design with an ergodic sum-rate upper bound, simplifying the MM based design algorithm. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

We investigate the electromagnetic (EM) exposure constrained spectral efficiency (SE) optimization design in uplink multiuser multiple-input multiple-output (MIMO) communications assisted by the reconfigurable intelligent surface (RIS) and dynamic metasurface antennas (DMAs). By adopting the alternating optimization (AO) method, the transmit covariance, RIS phase shift, and DMA weight matrices are jointly optimized. Specifically, we propose a modified SE maximization water-filling algorithm to obtain the optimal solutions of transmit covariance matrices. Then, the optimization of the RIS phase shift matrix is addressed via exploiting the weighted minimum mean square error, block coordinate descent, and minorize-maximization methods. Furthermore, we express the closed form solution of the unconstrained DMA weight matrix optimization problem and then design the DMA weights satisfying the constraint through an AO algorithm. Numerical results indicate the effectiveness of our proposed EM exposure aware SE maximization transmission scheme over the conventional algorithms.

In this paper, we investigate channel estimation for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce the concept of triple beams (TBs) in the space-frequency-time (SFT) domain and establish a TB based channel model using sampled triple steering vectors. With the established channel model, we then investigate the optimal channel estimation and pilot design for pilot segments. Specifically, we find the conditions that allow pilot reuse among multiple user terminals (UTs), which significantly reduces pilot overhead. To reduce the complexity, we are able to formulate the channel estimation as a sparse signal recovery problem due to the channel sparsity in the TB domain and then obtain the channel by the proposed constrained Bethe free energy minimization (CBFEM) based channel estimation algorithm. Simulation results demonstrate the superior performance of the proposed channel estimation approach.

In this paper, we investigate the robust precoding for high frequency skywave massive multiple-input multiple-output communications with imperfect channel state information (CSI). Starting with a sparse beam based a posteriori channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust pre-coder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

This paper investigates robust downlink precoding for three-dimensional (3D) massive multi-input multi-output (MIMO) configuration with matrix manifold optimization. Starting with a posteriori channel model, we formulate the robust precoder design to maximize an upper bound of ergodic weighted sum-rate under a total power budget. We derive the generalized eigenvector structure for optimal precoder with matrix manifold optimization. However, since the precoding of multiple users is coupled in the structure, we maximize the objective function for each user in alternation and prove the solution of each individual problem is the generalized eigenvector corresponding to the maximum generalized eigenvalue. In accordance with this, we present an iterative algorithm to design the precoder. Furthermore, we propose a Riemannian conjugate gradient (RCG) method to solve the generalized eigenvalue problem (GEP) for higher efficiency in the precoder design algorithm.

Abstract In this paper, massive multiple‐input‐multiple‐output (MIMO) wireless communication systems are considered to investigate joint transceiver beamforming. A base station (BS) equipped with a uniform planar array (UPA) serves several multi‐antennas users in a single cell. Based on the channel state information (CSI), the low complexity design of transceiver beamforming to minimize the transmit power subject to some quality of service (QoS) constraints is investigated. As the upper bound of the transmit power performance, the existing iteration‐based algorithms are leveraged as a reference. A general deep learning (DL)‐based framework and deep neural network (DNN) structure are proposed to reduce the complexity of the existing algorithms, where the properly trained DNN structure can learn directly from CSI. Consider the complexity of the DNN structure itself, a heuristic algorithm is proposed to replace the DNN structure, which takes the max‐eigenvalue‐eigenvector of the CSI as the direction of receive beamforming directly. The DNN structure is trained in the offline stage, therefore, only the complexity in the online stage is taken into consideration. Based on the numerical simulation, the complexity of the proposed DL‐based framework and the transceiver beamforming algorithms is reduced significantly while maintaining nearly the optimal performance compared with the existing iterative algorithms.

Considering great benefits brought by massive multiple-input–multiple-output (MIMO) technologies in the Internet of Things (IoT), it is of vital importance to analyze new massive MIMO channel characteristics and develop corresponding channel models. In the literature, various massive MIMO channel models have been proposed and classified with different but confusing methods, i.e., physical versus analytical method and deterministic versus stochastic method. To have a better understanding and usage of massive MIMO channel models, this work summarizes different classification methods and presents an up-to-date unified classification framework, i.e., artificial intelligence (AI)-based predictive channel models and classical nonpredictive channel models, which further clarify and combine the deterministic versus stochastic and physical versus analytical methods. Furthermore, massive MIMO channel measurement campaigns are reviewed to summarize new massive MIMO channel characteristics. Recent advances in massive MIMO channel modeling are surveyed. In addition, typical nonpredictive massive MIMO channel models are elaborated and compared, i.e., deterministic models and stochastic models, which include the correlation-based stochastic model (CBSM), geometry-based stochastic model (GBSM), and beam-domain channel model (BDCM). Finally, future challenges in massive MIMO channel modeling are given.

The space-air-ground-sea integrated network (SAGSIN) plays an important role in offering global coverage. To improve the efficient utilization of spectral and hardware resources in the SAGSIN, integrated sensing and communications (ISAC) has drawn extensive attention. Most existing ISAC works focus on terrestrial networks and cannot be straightforwardly applied in satellite systems due to the significantly different electromagnetic wave propagation properties. In this work, we investigate the application of ISAC in massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems. We first characterize the statistical wave propagation properties by considering beam squint effects. Based on this analysis, we propose a beam squint-aware ISAC technique for hybrid analog/digital massive MIMO LEO satellite systems exploiting statistical channel state information. Simulation results demonstrate that the proposed scheme can operate both the wireless communications and the target sensing simultaneously with satisfactory performance, and the beam-squint effects can be efficiently mitigated with the proposed method in typical LEO satellite systems.

This paper investigates the downlink (DL) transmit design for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication systems, where only the slow-varying statistical channel state information is exploited at the transmitter. The channel model for the DL massive MIMO LEO satellite system is established, in which both the satellite and the user terminals (UTs) are equipped with uniform planar arrays. Observing the rank-one property of the channel matrices, we show that the single-stream precoding for each UT is the optimal choice that maximizes the ergodic sum rate. This favorable result simplifies the complicated design of transmit covariance matrices into that of precoding vectors without any loss of optimality. Then, an efficient algorithm is devised to compute the precoding vectors. Furthermore, we formulate an approximate transmit design based on the upper bound on the ergodic sum rate, for which the optimality of single-stream precoding still holds. We show that, in this case, the design of precoding vectors can be simplified into that of scalar variables, for which an effective algorithm is developed. In addition, a low-complexity learning framework is proposed for optimizing the scalar variables. Simulation results demonstrate that the proposed approaches can achieve significant performance gains over the existing schemes.

The massive multiple-input multiple-output (MIMO) transmission technology has recently attracted much attention in the non-geostationary, e.g., low earth orbit (LEO) satellite communication (SATCOM) systems since it can significantly improve the energy efficiency (EE) and spectral efficiency. In this work, we develop a hybrid analog/digital precoding technique in the massive MIMO LEO SATCOM downlink, which reduces the onboard hardware complexity and power consumption. In the proposed scheme, the analog precoder is implemented via a more practical twin-resolution phase shifting (TRPS) network to make a meticulous tradeoff between the power consumption and array gain. In addition, we consider and study the impact of the distortion effect of the nonlinear power amplifiers (NPAs) in the system design. By jointly considering all the above factors, we propose an efficient algorithmic approach for the TRPS-based hybrid precoding problem with NPAs. Numerical results show the EE gains considering the nonlinear distortion and the performance superiority of the proposed TRPS-based hybrid precoding scheme over the baselines.

This work focuses on providing robust line-of-sight (LoS) spatial multiplexing at flexible communications distances and directions. Considering oblique LoS uniform linear arrays, we first derive the rank-deficient and orthogonal conditions for the LoS MIMO channel matrices. With this discovery, the topology of high spatial-resolution on one side of the link is shown with a wide full-rank-channel guarantee interval over distance and direction variations. Additionally, to reduce the implementation costs and power consumption, we propose to use low amplitude-resolution quantizers at the side of high spatial-resolution. With numerical evaluations on systems having few-bit analog-to-digital converters (ADCs), the proposed system design is shown to simultaneously achieve a higher spectrum efficiency and higher energy efficiency compared to a conventional single-stream high-amplitude-resolution over a wide signal-to-noise-ratio (SNR) range. Furthermore, we investigate channel equalization under the extreme case of using 1-bit ADCs. After providing a new viewpoint on the generalized approximate message passing (GAMP) algorithm from constrained Bethe free energy minimization, our simulations on bit-error-rates show that the GAMP algorithm can significantly reduce the performance degradation due to coarse quantization and can significantly outperform the Bussgang decomposition based linear minimum-mean-square-error estimator, especially at high SNRs.

Distributed computing systems, such as Hadoop, have been widely studied and used for executing and analyzing large data. In this paper, we investigate an emerging resource allocation problem for wireless distributed computing systems consisting of multifunctional nodes in charge of both numerical computation and wireless communication with master nodes. We focus on a computation power consumption model based on CMOS devices and a communication power consumption model involving multiple antenna transceivers against mutual interference. We present a joint optimization problem for workload scheduling and power allocation for achieving maximum computational speed under total power constraint. We simplify the joint optimization into two sub-problems. For workload scheduling as an integer programming sub-problem, we relax the integer constraint and establish the equivalence between relaxed and original problems. For the power allocation sub-problem, we maximize a difference of convex functions by utilizing the concave-convex procedure. We prove our proposed algorithm to converge to a stationary point of the original program. Simulation results confirm the efficiency and near-optimal performance of our proposed algorithms.

In the massive grant-free orthogonal frequency division multiple access (OFDMA), the timing and frequency offsets between users impose new challenges on joint active user detection (AUD) and channel estimation (CE) for the subsequent data recovery. In the asynchronous OFDMA, the timing and frequency offset effects can be modeled as the phase-shifting on the pilot matrix. As such, by constructing the measurement matrix with timing and frequency offsets, the joint estimation problem can be formulated as a multiple measurement vector (MMV) recovery problem with structured sparsity. However, such structured sparsity cannot be tackled by the existing compressed sensing (CS) techniques. To address this issue, we develop an efficient structured generalized approximate message passing (S-GAMP) algorithm, which includes the parallel AMP-MMV algorithm as a particular case. To deal with the high dimensionality of the measurement matrix, we propose the dynamic S-GAMP algorithm with a dynamic measurement matrix to reduce the computational complexity. Simulation results confirm the superiority of the proposed algorithms in grant-free OFDMA with both timing and frequency offsets.

This paper investigates robust downlink precoding for three-dimensional (3D) massive multi-input multi-output (MIMO) configuration with matrix manifold optimization. Starting witha posteriorichannel model, we formulate the robust precoder design to maximize an upper bound of ergodic weighted sum-rate under a total power budget. We derive the generalized eigenvector structure for optimal precoder with matrix manifold optimization. However, since the precoding of multiple users is coupled in the structure, we maximize the objective function for each user in alternation and prove the solution of each individual problem is the generalized eigenvector corresponding to the maximum generalized eigenvalue. In accordance with this, we design an iterative algorithm and present its convergence analysis. Furthermore, we propose a Riemannian conjugate gradient (RCG) method to solve the generalized eigenvalue problem (GEP) for higher efficiency in the precoder design algorithm.

Massive multiple-input multiple-output (MIMO) is promising for low earth orbit (LEO) satellite communications due to the potential in enhancing the spectral efficiency. However, the conventional fully digital precoding architectures might lead to high implementation complexity and energy consumption. In this paper, hybrid analog/digital precoding solutions are developed for the downlink operation in LEO massive MIMO satellite communications, by exploiting the slow-varying statistical channel state information (CSI) at the transmitter. First, we formulate the hybrid precoder design as an energy efficiency (EE) maximization problem by considering both the continuous and discrete phase shift networks for implementing the analog precoder. The cases of both the fully and the partially connected architectures are considered. Since the EE optimization problem is nonconvex, it is in general difficult to solve. To make the EE maximization problem tractable, we apply a closed-form tight upper bound to approximate the ergodic rate. Then, we develop an efficient algorithm to obtain the fully digital precoders. Based on which, we further develop two different efficient algorithmic solutions to compute the hybrid precoders for the fully and the partially connected architectures, respectively. Simulation results show that the proposed approaches achieve significant EE performance gains over the existing baselines, especially when the discrete phase shift network is employed for analog precoding.

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. Considering the large antenna array aperture and increased signal bandwidth, the effect of the propagation delay across the large-scale antenna array cannot be ignored, and thus the steering vectors vary across different subcarriers. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error (MMSE) based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. In order to reduce the design complexities of the MMSE receiver and precoder, we derive a polynomial expansion based design using a deterministic equivalent. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

The development of an integrated satellite-terrestrial communication network has become one of the focuses in both academic and industry in order to provide genuine seamless coverage. For the integrated satellite and terrestrial 5G commu-nication systems, positioning information of user terminals (UTs) can be beneficial in addressing several challenges. In this paper, we propose to utilize 5G new radio synchronization signals to perform positioning. To simultaneously guarantee synchronization and positioning performances for UTs in any place of a cell coverage, we investigate the precoding design at the satellite side for joint synchronization and positioning (JSP) in 5G integrated satellite-terrestrial networks. By considering the missed detection probabilities and angle of departure estimation for the UTs, we provide the precoding design criteria for synchronization and positioning, respectively. Then we introduce the constraint of equal transmit power on every antenna. Based on the criteria and constraint, we formulate the optimization problem for JSP and exploit the conjugate gradient algorithm under the manifold op-timization framework to design the precoder. Simulation results show that the proposed precoder can ensure that JSP achieves satisfactory performances within the whole cell coverage.

Massive multiple-input multiple-output (MIMO) technology has attracted much attention in low earth orbit (LEO) downlink satellite communication (SATCOM) systems recently since the energy efficiency (EE) and spectral efficiency can be significantly improved. In order to reduce the power consumption for the massive MIMO LEO SATCOM systems, we focus on the hybrid analog/digital architecture in this work. Considering the limited resolution of the phase shifters in practical MIMO SATCOM systems, a twin-resolution phase shifting (TRPS) network is proposed to make a meticulous tradeoff between the power consumption and array gains. In addition, we examine the impact of the distortion, introduced by the power amplifiers (PAs) to the system design, by considering nonlinear PA models. Moreover, we propose an efficient algorithm for TRPS-based hybrid precoding with nonlinear PAs. Numerical results show the EE gains considering nonlinear distortion and the performance superiority of the proposed hybrid architecture compared with the baselines.

This paper studies the energy efficiency (EE) optimization of a single-cell multiuser massive multiple-input multiple-output (MIMO) uplink system, where configurable dy-namic metasurface antennas (DMAs) are deployed at the base station (BS). To maximize the system EE, we present a framework for the joint optimization of the users' transmit precoding and the BS DMAs' weights, which is based on Dinkelbach's transform and an alternating optimization algorithm. Since the physical structure constraint of DMAs exhibits a non-convex form, we firstly obtain the optimal unconstrained DMAs' weights in closed form. Then, we configure those weights with the non-convex constraint and approximate them with the optimal unconstrained solutions. Our numerical results showcase that our DMAs-based systems can achieve much higher EE performance than those based on conventional antenna arrays and beamforming architectures. It is also demonstrated that the EE performance of DMAs-based uplink massive MIMO systems can be further improved by adjusting the number of microstrips and the number of meta-atoms per microstrip.

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. The steering vectors vary across different subcarriers due to the effect of the propagation delay across the largescale antenna array. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

In this paper, we consider massive multiple-input multiple-output (MIMO) communication systems with a uniform planar array (UPA) at the base station (BS) and investigate the downlink precoding with imperfect channel state information (CSI). By exploiting both instantaneous and statistical CSI, we aim to design precoding vectors to maximize the ergodic rate subject to a total transmit power constraint. By maximizing an upper bound of the ergodic rate instead, we leverage the corresponding Lagrangian formulation and identify the structural characteristics of the optimal precoder as the solution to a generalized eigenvalue problem. As such, the high-dimensional precoder design problem turns into a low-dimensional power control problem. The Lagrange multipliers play a crucial role in determining both precoder directions and power parameters, yet are challenging to be solved directly. To figure out the Lagrange multipliers, we develop a deep learning approach underpinned by a properly designed neural network that learns directly from CSI. With the offline pre-trained neural network, the online computational complexity of precoding is substantially reduced compared with the existing iterative algorithm while maintaining nearly the same performance.

We investigate the downlink (DL) transmit strategy for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication (SATCOM) systems, in which only the slow-varying statistical channel state information is known at the transmitter side. First, we derive the massive MIMO LEO satellite channel model, when the uniform planar arrays are deployed at both the satellite and user terminals (UTs). Building on the rank-one property of the satellite channel matrices, we show that transmitting a single data stream to each UT is optimal in the sense that the ergodic sum rate is maximized. This result is of great importance for massive MIMO LEO SATCOM systems, since the sophisticated design of transmit covariance matrices is turned into that of precoding vectors, without loss of optimality. Furthermore, we develop an algorithm to compute the precoding vectors. Simulation results show the significant performance gains of the proposed approaches over the existing schemes.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

In order to provide reliable and efficient connections between unmanned aerial vehicles (UAVs) and ground stations (GSs), realistic UAV-to-ground channel models are indispensable. In this article, we propose a novel 3-D nonstationary geometry-based stochastic model (GBSM) for UAV-to-ground multiple-input-multiple-output (MIMO) channels. Distinctive UAV-to-ground channel characteristics, such as time-domain nonstationarity, distinctions between different altitudes, spatial consistency, and 3-D arbitrary UAV movement trajectories, are taken into account. By adjusting parameter settings, the proposed channel model framework is sufficiently general to support multiple frequency bands and multiple scenarios, including millimeter wave (mmWave) and massive MIMO configurations. Statistical properties, including power delay profile (PDP), stationary interval, space-time correlation function (STCF), and root-mean-square (RMS) delay spread are derived and analyzed for different frequencies and scenarios. The accuracy of the proposed model is validated by comparing its statistical properties with corresponding available channel measurements. The proposed channel model will provide a fundamental support for the design, performance evaluation, and optimization of future UAV integrated sixth-generation (6G) wireless networks.

This article studies massive access in cell-free massive multi-input multi-output (MIMO)-based Internet of Things and solves the challenging active user detection (AUD) and channel estimation (CE) problems. For the uplink transmission, we propose an advanced frame structure design to reduce the access latency. Moreover, by considering the cooperation of all access points (APs), we investigate two processing paradigms at the receiver for massive access: cloud computing and edge computing. For cloud computing, all APs are connected to a centralized processing unit (CPU), and the signals received at all APs are centrally processed at the CPU. While for edge computing, the central processing is offloaded to part of APs equipped with distributed processing units, so that the AUD and CE can be performed in a distributed processing strategy. Furthermore, by leveraging the structured sparsity of the channel matrix, we develop a structured sparsity-based generalized approximated message passing (SS-GAMP) algorithm for reliable joint AUD and CE, where the quantization accuracy of the processed signals is taken into account. Based on the SS-GAMP algorithm, a successive interference cancellation-based AUD and CE scheme is further developed under two paradigms for reduced access latency. Simulation results validate the superiority of the proposed approach over the state-of-the-art baseline schemes. Besides, the results reveal that the edge computing can achieve the similar massive access performance as the cloud computing, and the edge computing is capable of alleviating the burden on CPU, having a faster access response, and supporting more flexible AP cooperation.

In this paper, a novel three-dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) is proposed for the sixth generation (6G) terahertz (THz) wireless communication systems. The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users. Also, the generality of the proposed channel model is demonstrated by the fact that it can easily be reduced to different simplified channel models to fit specific scenarios by properly adjusting model parameters. The proposed general channel model takes into consideration the non-stationarities in space, time, and frequency domains caused by ultra-massive MIMO, long traveling paths, and large bandwidths of THz communications, respectively. Statistical properties of the proposed general THz channel model are investigated. The accuracy and generality of the proposed channel model are verified by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding channel measurements.

While massive multiple-input multiple-output (MIMO) has achieved tremendous success in both theory and practice, it faces a crisis of sharp performance degradation in moderate or high-mobility scenarios (e.g., 30 km/h), due to the breach of uplink-downlink channel duality. Such a “curse of mobility” has spurred the research on channel prediction in high-mobility scenarios. Instead of predicting channel response matrix in the space-frequency domain, we investigate it in the angle-delay domain by utilizing the high angle-delay resolution of wideband massive MIMO systems. Specifically, we study the general angle-delay domain channel characterization and obtain that: 1) the correlations between the angle-delay domain channel response matrix (ADCRM) elements are decoupled significantly; 2) when the number of antennas and bandwidth are limited, the decoupling is insufficient and residual correlations between the neighboring ADCRM elements exist. Then focusing on the ADCRM, we propose two channel prediction methods: a spatio-temporal autoregressive (ST-AR) model-driven unsupervised-learning method and a deep learning (DL) based data-driven supervised-learning method. While the model-driven method provides a principled way for channel prediction, the data-driven method is generalizable to various channel scenarios. In particular, ST-AR exploits the residual spatio-temporal correlations of the channel element with its most neighboring elements, and DL realizes element-wise angle-delay domain channel prediction utilizing a complex-valued neural network (CVNN). Simulation results under the 3GPP non-line-of-sight (NLOS) scenarios indicate that, compared to the state-of-the-art Prony-based angular-delay domain (PAD) prediction method, both the proposed ST-AR and the CVNN-based channel prediction methods can enhance the channel prediction accuracy.

In this paper, we investigate the massive multi-input multi-output (MIMO) transmission for the satellite communication systems equipped with a uniform rectangular array (URA) and aim to design the precoder with broad coverage radiation power pattern to improve the performance of time and frequency synchronizations. The modified Cramér-Rao vector bounds (MCRVB) are chosen as the benchmark of symbol timing offset and frequency offset estimation problem, which can be viewed as the time and frequency synchronization performance metric for the broad coverage precoder design. By considering the minimax MCRVB criterion and the per-antenna equal power constraint, the precoder design approach is formulated as the non-convex constrained minimax problem over the discrete radiation power pattern. This optimization problem is solved by the smoothing techniques combined with the manifold optimization method. To reduce the calculation complexity, the nonmonotone conjugate gradient method is applied. Simulation results show that the average and the minimum received powers in the coverage area of the proposed scheme are higher than those of the scheme based on fairness criterion. Compared with the existing omnidirectional and broad coverage schemes, the proposed scheme has the best synchronization performance among all the contrast solutions.

The continually rising demand for wireless services and applications in the era of Internet of things (IoT) and artificial intelligence (AI) presents a significant number of unprecedented challenges to existing network structures. To meet the rapid growth need of mobile data services, blockchain radio access network (B-RAN) has emerged as a decentralized, trustworthy radio access paradigm spurred by blockchain technologies. However, many characteristics of B-RAN remain unclear and hard to characterize. In this study, we develop an analytical framework to model B-RAN and provide some basic fundamental analysis. Starting from block generation, we establish a queuing model based on a time-homogeneous Markov chain. From the queuing model, we evaluate the performance of B-RAN with respect to latency and security considerations. We uncover a more comprehensive picture of the achievable performance of B-RAN by connecting latency and security. At last, we present experimental results via an innovative prototype and validate the proposed model.

In this paper, we consider massive multiple-input-multiple-output (MIMO) communication systems with a uniform planar array (UPA) at the base station (BS) and investigate the downlink precoder design with imperfect channel state information (CSI). By exploiting channel estimates and statistical parameters of channel estimation error, we aim to design precoding vectors to maximize the utility function on the ergodic rates of users subject to a total transmit power constraint. By employing an upper bound of the ergodic rate, we leverage the corresponding Lagrangian formulation and identify the structural characteristics of the optimal precoder as the solution to a generalized eigenvalue problem. The Lagrange multipliers play a crucial role in determining both precoding directions and power parameters, yet are challenging to be solved directly. To figure out the Lagrange multipliers, we develop a general framework underpinned by a properly designed neural network that learns directly from CSI. To further relieve the computational burden, we obtain a low-complexity framework by decomposing the original problem into computationally efficient subproblems with instantaneous and statistical CSI handled separately. With the offline pre-trained neural network, the online computational complexity of precoder is substantially reduced compared with the existing iterative algorithm while maintaining nearly the same performance.

This work proposes a fiber-enabled optical wireless communication (FE-OWC) system for bidirectional communications between the base station (BS) and a number of mobile user terminals (UTs) via full beam coverage, aimed at facilitating ultra-high data rate communications. The FE-OWC system comprises optical antennas, optical chains, and baseband units at both BS and UTs. The innovative optical antenna consists of an array of fiber ports and a transceiver lens, which can form a number of transmit and receive optical beams and provide a full beam coverage for simultaneous downlink and uplink connections with a number of UTs, respectively. We present analysis to characterize downlink and uplink channel models and gains, including both optical and electrical parts, between the BS and UTs and conduct a complete link budget analysis. We further design downlink and uplink multiuser multiple-input multiple-output (MIMO) as well as massive MIMO transmission protocols and develop asymptotically optimal schemes for a large number of fiber ports. Numerical results illustrate that the FE-OWC system has the potential to support over 10 Gbps data rate per UT and Tbps system throughput required in future 6G mobile communication systems.

This paper investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices designing problem into a power allocation one. Then, to lower the computational complexity, we utilize an asymptotic approximation expression for the problem objective. Moreover, for the power allocation design, we adopt the minorization maximization method to address the non-convexity of the ergodic rate, and use Dinkelbach’s transform to convert the max-min fractional problem into a series of convex optimization subproblems. To tackle the transformed subproblems, we propose a centralized iterative water-filling scheme. For reducing the backhaul burden, we further develop a distributed algorithm for the power allocation problem, which requires limited inter-cell information sharing. Finally, the performance of the proposed algorithms are demonstrated by extensive numerical results.

In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.

In this article, delay and energy efficiency (EE) are investigated in fog radio access networks (F-RANs) with hybrid caching. With multiple caching and transmission strategies, hybrid caching offers great flexibility for file placement and file fetching. By using tools from stochastic geometry, we firstly derive tractable expressions of delay for coded cached, non-partitioned cached and uncached files. Then, we derive tractable expressions of EE by jointly considering power consumed in circuits, transmissions and fronthaul links. To balance delay and EE, the corresponding multi-objective optimization problem is formulated to obtain the optimal hybrid caching strategy. Furthermore, considering the NP-hard complexity of the problem, we first theoretically analyze the optimal structure of the caching result. Then, we convert the original problem into a classification problem. We further propose a gradual-replacement greedy algorithm to obtain a near optimal hybrid caching strategy, which ensures high accuracy with low complexity. Numerical results show a significant performance gain of the proposed near optimal hybrid caching strategy over baselines and flexibility in delay-sensitive and EE-sensitive scenarios.

In this paper, we investigate a sparse channel estimation problem for broadband massive multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We propose a hidden Markov model to capture the structured sparsity and temporal dependency characteristic of massive MIMO-OFDM channels in the angle-delay domain, and this probability model exhibits extensive adaptability to different realistic propagation scenarios. Then we solve the channel estimation problem based on a novel optimization framework named constrained Bethe free energy (BFE) minimization, which is valid for a generic statistical model. Under this systematic theoretical framework, a hierarchical hybrid message passing (HHMP) algorithm is proposed to track dynamic channel parameters recursively. The proposed method can adaptively learn the sparse structure and temporal correlation of multiuser channels without requiring the knowledge of hidden Markov channel parameters. Numerical simulations demonstrate that the proposed HHMP algorithm can accurately estimate angle-delay domain channels with reduced iteration times and pilot overhead.

Although the combination of the orthogonal time frequency space (OTFS) modulation and the massive multiple-input multiple-output (MIMO) technology can make communication systems perform better in high-mobility scenarios, there are still many challenges in downlink channel estimation owing to inaccurate modeling and high pilot overhead in practical systems. In this paper, we propose a channel state information (CSI) acquisition scheme for downlink massive MIMO-OTFS in presence of the fractional Doppler, including deterministic pilot design and channel estimation algorithm. First, we analyze the input-output relationship of the single-input single-output (SISO) OTFS based on the orthogonal frequency division multiplexing (OFDM) modem and extend it to massive MIMO-OTFS. Moreover, we formulate an accurate model for the practical system in which the fractional Doppler is considered and the influence of subpaths is revealed. A deterministic pilot design is then proposed based on the model and the structure of the pilot matrix to reduce pilot overhead and save memory consumption. Since channel geometry changes very slowly relative to the communication timescale, we put forward a modified sensing matrix based channel estimation (MSMCE) algorithm to acquire the downlink CSI. Simulation results demonstrate that the proposed downlink CSI acquisition scheme has significant advantages over traditional algorithms.

In this article, we investigate multi-cell scheduling for massive multiple-input-multiple-output (MIMO) communications with only statistical channel state information (CSI). The objective of multi-cell scheduling is to activate a subset of users so as to maximize the ergodic sum rate subject to per-cell total transmit power constraint. By adopting beam division multiple access based on the statistical CSI, i.e., channel-coupling matrix (CCM), we simplify multi-cell scheduling as a power control problem in the beam domain, by which the ergodic sum rate is maximized. To reduce the computational burden on finding the ergodic sum rate, we propose a learning-to-compute strategy, which directly computes the complex ergodic rate function from CCMs via a deep neural network. Specifically, by modeling the probability density function of the ordered eigenvalues of the Hermitian CCM matrices as exponential family distributions, a properly designed hybrid neural network makes the ergodic rate computation feasible. With the learning-to-compute strategy, the online computational complexity of multi-cell scheduling is substantially reduced compared with the existing Monte Carlo or deterministic equivalent (DE) based methods while maintaining nearly the same performance.

Non-orthogonal multiple access (NOMA) enables massive connectivity and achieves high spectral efficiency. The vast majority of the NOMA literature has adopted the ideal information rate as performance metric assuming perfect successive interference cancellation (SIC) without any error propagation, which, however, may lead to NOMA designs adverse to SIC. In this paper, we take into account imperfect SIC and practical modulation schemes for power-domain NOMA design. To characterize the error propagation, we derive the bit error rates (BERs) of the users for arbitrary-order quadrature amplitude modulation (QAM) schemes. Then, we propose a minimum error probability NOMA (MEP-NOMA) design, minimizing the average BER of the users via power allocation. Considering the complicated error probability expressions of the MEP-NOMA design, we derive lower and upper bounds on the average BER, based on which a simple closed-form power allocation is obtained. We show that the proposed power allocation minimizes both the lower and upper bounds on the average BER for a sufficiently large power budget and provides near-optimal error performance. On this basis, we theoretically prove the superiority of MEP-NOMA over existing OMA and NOMA schemes in terms of error performance. Comprehensive numerical results are provided to verify the accuracy of the error probability analysis of the considered practical NOMA scheme with imperfect SIC and to demonstrate the efficacy of the proposed MEP-NOMA design.

In this paper, we investigate user positioning in massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems where the base station (BS) is equipped with a uniform planar array (UPA). Taking advantage of the UPA geometry and wide bandwidth, we advocate the use of the angle-delay channel power matrix (ADCPM) as a new type of fingerprint to replace the traditional ones. The ADCPM embeds the stable and stationary multipath characteristics, e.g., delay, power, and angles in the vertical and horizontal directions, which are beneficial to positioning. We further exploit the sparsity of the ADCPM to reduce the noise contamination in the ADCPM. Taking ADCPM fingerprints as the inputs, we propose a novel three-dimensional (3D) convolution neural network (CNN) enabled learning method to localize the 3D positions of the mobile terminals (MTs). In particular, such a 3D CNN model consists of a convolution refinement module to refine the elementary feature maps from the ADCPM fingerprints, three extended Inception modules to extract the advanced feature maps, and a regression module to estimate the 3D positions. By intensive simulations, the proposed 3D CNN-enabled positioning method is demonstrated to achieve higher positioning accuracy than the traditional searching-based ones, with reduced computational complexity and storage overhead, and robust to noise contamination.

This paper considers the application of reconfigurable intelligent surfaces (RISs) (a.k.a. intelligent reflecting surfaces (IRSs)) to assist multiuser multiple-input multiple-output (MIMO) uplink transmission from several multi-antenna user terminals (UTs) to a multi-antenna base station (BS). For reducing the signaling overhead, only partial channel state information (CSI), including the instantaneous CSI between the RIS and the BS as well as the slowly varying statistical CSI between the UTs and the RIS, is exploited in our investigation. In particular, an optimization framework is proposed for jointly designing the transmit covariance matrices of the UTs and the RIS phase shift matrix to maximize the system global energy efficiency (GEE) with partial CSI. We first obtain closed-form solutions for the eigenvectors of the optimal transmit covariance matrices of the UTs. Then, to facilitate the design of the transmit power allocation matrices and the RIS phase shifts, we derive an asymptotically deterministic equivalent of the objective function with the aid of random matrix theory. We further propose a suboptimal algorithm to tackle the GEE maximization problem with guaranteed convergence, capitalizing on the approaches of alternating optimization, fractional programming, and sequential optimization. Numerical results substantiate the effectiveness of the proposed approach as well as the considerable GEE gains provided by the RIS-assisted transmission scheme over the traditional baselines.

Variational message passing (VMP), belief propagation (BP) and expectation propagation (EP) have found their wide applications in complex statistical signal processing problems. In addition to viewing them as a class of algorithms operating on graphical models, this article unifies them under an optimization framework, namely, Bethe free energy minimization with differently and appropriately imposed constraints. This new perspective in terms of constraint manipulation can offer additional insights on the connection between different message passing algorithms and is valid for a generic statistical model. It also founds a theoretical framework to systematically derive message passing variants. Taking the sparse signal recovery (SSR) problem as an example, a low-complexity EP variant can be obtained by simple constraint reformulation, delivering better estimation performance with lower complexity than the standard EP algorithm. Furthermore, we can resort to the framework for the systematic derivation of hybrid message passing for complex inference tasks. Notably, a hybrid message passing algorithm is exemplarily derived for joint SSR and statistical model learning with near-optimal inference performance and scalable complexity.

Integrated satellite-terrestrial communications networks aim to exploit both the satellite and the ground mobile communications and thus provide genuine ubiquitous coverage. For 5G integrated low earth orbit (LEO) satellite communication (SatCom) systems, the timing advance (TA) is required to be estimated in the initial random access procedure of communications in order to facilitate the uplink frame alignment among different users. However, due to the inherent characteristics of LEO SatCom systems, the existing 5G terrestrial uplink TA scheme is not applicable in the satellite networks. In this paper, we investigate location-based TA estimation for 5G integrated LEO SatCom systems. We propose to take the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained in the downlink timing and frequency synchronization phase for geographical location estimation, which are made from the satellite at different time instants. The location estimation is then formulated as a quadratic optimization problem. We propose an approximation method based on iteratively performing a linearization procedure on the quadratic equality constraints to solve this problem. Numerical results show that the proposed method can effectively assure uplink frame alignment among different users in typical LEO SatCom systems.

In machine-type communication scenarios, sparse code multiple access (SCMA) is a promising non-orthogonal multiple access (NOMA) scheme owing to shaping gain by combining constellation modulation and spreading patterns together. In this paper, to fully exploit the channel knowledge contained in received data sequences, we propose the joint active user detection (AUD), channel estimation (CE), multi-user detection (MUD), and decoding receiver without knowing users' activity parameters in uplink grant-free massive multiple-input multiple-output (MIMO) SCMA systems. To avoid the permutation and scaling ambiguities of estimation results, the proposed receiver estimates channel based on both received short pilot and data sequences. We introduce auxiliary active state indicators in AUD to describe the sporadic transmission feature. The joint CE and MUD module is constructed as a bilinear inference problem with joint column-wise sparsity. Furthermore, we exploit the SCMA codewords sparsity feature and put Gaussian approximations on modulated symbols in joint CE and MUD module to reduce the receiver complexity. Simulation results show that the proposed unified receiver has substantial performance improvement and lower computational complexity than the conventional two-stage receiver and joint receiver in the literature.

We study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in multiuser multiple-input multiple-output (MIMO) uplink communications aided by a reconfigurable intelligent surface (RIS) equipped with discrete phase shifters. For reducing the required signaling overhead and energy consumption, our design is based on the partial channel state information (CSI), including the statistical CSI between the RIS and user terminals (UTs) and the instantaneous CSI between the RIS and the base station. To investigate the EE-SE tradeoff, we develop a framework for the joint optimization of UTs' transmit precoding and RIS reflective beamforming to maximize a metric called resource efficiency. Based on the closed-form solutions of all UTs' optimal transmit subspace and an asymptotic objective expression, an optimization framework is proposed via exploiting the quadratic transformation, the homotopy, accelerated projected gradient, and majorization-minimization methods. Numerical results illustrate the effectiveness of our optimization framework for the considered RIS-aid communications.

To alleviate the blockage effects involved in millimeter-wave propagation, we investigate network massive multiple-input multiple-output (MIMO) transmission where only statistical channel state information is available at base stations (BSs). We first establish a network massive MIMO transmission model over millimeter-wave bands using per-beam synchronization. We Figure out that the beam domain is in favor of performing transmission in this scenario. We also demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence is further proposed to solve the problem. Numerical results reveal that the proposed network massive MIMO transmission approach can effectively alleviate the blockage effects and provide substantial performance gains over the existing transmission approaches.

Power constraints and channel acquisition pose practical challenges in multi-beam satellite communications. Motivated by this, we investigate robust energy-efficient multigroup multicast beamforming in multi-beam satellite communications with full frequency reuse in this paper. Specifically, we consider the problem of minimizing the total power while guaranteeing that the energy efficiency (EE) of each group is above a prescribed threshold. The considered problem is challenging in the sense that the average rates in the definition of the EE generally do not admit an explicit expression and the optimization problem is NP-hard and nonconvex. To tackle this problem, we first adopt a closed-form tight approximation for the average rates. Then the semidefinite relaxation and the concave-convex procedure are utilized to transfer the nonconvex problem into a convex problem. Finally, based on the ranks of the solutions, the eigenvalue decomposition or the Gaussian randomization approach is invoked to generate the final feasible solutions. Numerical results validate the high accuracy of the average rate approximation, and demonstrate that our proposed robust approach significantly outperforms the conventional one, especially for the case with large channel phase error variances.

In this paper, we investigate broad coverage pre-coder design for 3D massive multi-input multi-output (MIMO) systems. We focus on the synchronization performance in the cell coverage. The log-distance path loss model in the line-of-sight (LoS) scenario is formulated. The synchronization performance can be characterized by the missed detection (MD) probability. By considering the equal MD probability in the cell, we formulate a criterion for the precoder design. Moreover, we consider the equal transmit power constraint on each antenna to efficiently utilize the power amplifier (PA) capacity of the BS. By using the manifold optimization framework, we design the precoder under the aforementioned criterion and constraint. Simulation results show that the fairness among all the users in this cell can be ensured. Compared with the precoders designed by half power beam-width (HPBW), the proposed scheme causes much less inter-cell interference, and has a better synchronization performance.

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based on federated learning. Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users' context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters using the stochastic variance reduced gradient (SVRG) algorithm. Finally, federated learning based model integration is proposed to construct the global popularity prediction model based on local models by combining the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only predicts content popularity accurately, but also significantly reduces computational complexity. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to the traditional policies.

In this paper, we investigate the three-dimensional (3D) user positioning in massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems with the base station (BS) equipped with a uniform planner antenna (UPA) array. Taking advantage of the UPA array geometry and wide bandwidth, we advocate the use of the angle-delay channel power matrix (ADCPM) as a new type of fingerprint to replace the traditional ones. The ADCPM embeds the stable and stationary multipath characteristics, e.g., delay, power, and angles in the vertical and horizontal directions, which are beneficial to positioning. Taking ADCPM fingerprints as the inputs, we propose a novel 3D convolution neural network (CNN) enabled learning method to localize users' 3D positions. By intensive simulations, the proposed 3D CNN-enabled positioning method is demonstrated to achieve higher positioning accuracy than the traditional searching-based ones, with reduced computational complexity and storage overhead, and robust to noise contamination.

This paper considers the application of reconfigurable intelligent surfaces (RISs) to assist multiuser multiple-input multiple-output multiple access channel (MIMO-MAC) systems. In contrast to most existing works on RIS-assisted systems assuming the availability of full channel state information (CSI), only partial CSI is required in our investigation, including the instantaneous CSI of the channel from a RIS to a base station and the statistical CSI of the channels from user terminals (UTs) to the RIS. We investigate the joint design of both the transmit covariance matrices of the UTs and the RIS phase shift matrix under the system global energy efficiency (GEE) maximization criterion. To maximize the GEE, we first obtain closed-form solutions for the eigenvectors of the optimal transmit covariance matrices of the UTs. Then, we derive an asymptotic expression of the objective function with the aid of random matrix theory to reduce the computational cost. We further propose a low-complexity algorithm to tackle the GEE maximization problem with guaranteed convergence, capitalizing on the approaches of alternating optimization, fractional programming, and sequential optimization. Numerical results substantiate the effectiveness of the proposed approach as well as the GEE performance gains provided by RIS-assisted MIMO-MAC systems.

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO downlink (DL) transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems by exploiting statistical channel state information (sCSI) at the transmitter. We first establish a massive MIMO channel model for LEO satellite communications and propose Doppler and time delay compensation techniques at user terminals (UTs). Then, we develop a closed-form low-complexity sCSI based DL precoder by maximizing the average signal-to-leakage-plus-noise ratio (ASLNR). Motivated by the DL ASLNR upper bound, we further propose a space angle based user grouping algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems.

A novel beam domain channel model (BDCM) for massive multiple-input multiple-output (MIMO) communication systems has been proposed in this paper. The near-field effect and spherical wavefront are firstly assumed in the proposed model, which is different from the conventional BDCM for MIMO based on the far-field effect and plane wavefront assumption. The proposed novel BDCM is the transformation of an existing geometry-based stochastic model (GBSM) from the antenna domain into beam domain. The space-time non-stationarity is also modeled in the novel BDCM. Moreover, the comparison of computational complexity for both models is studied. Based on the numerical analysis, comparison of cluster-level statistical properties between the proposed BDCM and existing GBSM has shown that there exists little difference in the space, time, and frequency correlation properties for two models. Also, based on the simulation, coherence bandwidths of the two models in different scenarios are almost the same. The computational complexity of the novel BDCM is much lower than the existing GBSM. It can be observed that the proposed novel BDCM has similar statistical properties to the existing GBSM at the cluster-level. The proposed BDCM has less complexity and is therefore more convenient for information theory and signal processing research than the conventional GBSMs.

Millimeter wave (mmWave) bands have been utilized for the fifth generation (5G) communication systems and will no doubt continue to be deployed for beyond 5G (B5G). However, the underlying channels are not fully investigated at multi-frequency bands and in multi-scenarios by using the same channel sounder, especially for the outdoor, multiple-input multiple-output (MIMO), and vehicle-to-vehicle (V2V) conditions. In this paper, we conduct multi-frequency multi-scenario mmWave MIMO channel measurements with 4 × 4 antennas at 28, 32, and 39 GHz bands for three cases, i.e., the human body and vehicle blockage measurements, outdoor path loss measurements, and V2V measurements. The channel characteristics, including blockage effect, path loss and coverage range, and non-stationarity and spatial consistency, are thoroughly studied. The blockage model, path loss model, and time-varying channel model are proposed for mmWave MIMO channels. The channel measurement and modeling results will be of great importance for further mmWave communication system deployments in indoor hotspot, outdoor, and vehicular network scenarios for B5G.

Mobility and blockage are two critical challenges in wireless transmission over millimeter-wave (mmWave) and Terahertz (THz) bands. In this paper, we investigate network massive multiple-input multiple-output (MIMO) transmission for mmWave/THz downlink in the presence of mobility and blockage. Considering the mmWave/THz propagation characteristics, we first propose to apply per-beam synchronization for network massive MIMO to mitigate the channel Doppler and delay dispersion effects. Accordingly, we establish a transmission model. We then investigate network massive MIMO downlink transmission strategies with only the statistical channel state information (CSI) available at the base stations (BSs), formulating the strategy design as an optimization problem to maximize the network sum-rate. We show that the beam domain is favorable to perform transmission, and demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence performance is further proposed for beam domain power allocation. Numerical results reveal that the proposed network massive MIMO transmission approach with the statistical CSI can effectively alleviate the blockage effects and provide mobility enhancement over mmWave and THz bands.

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

In this paper, we investigate broad coverage precoder design for 3D massive multi-input multi-output (MIMO) systems, where the base station (BS) is equipped with a uniform rectangular array (URA). We focus on the synchronization performance in the cell coverage. The flat fading channel model is formulated. The synchronization performance can be characterized by the missed detection (MD) probability. By considering the MD probability fairness in the cell, we formulate a criterion for the precoder design. Moreover, we consider the equal transmit power constraint on each antenna to efficiently utilize the power amplifier (PA) capacity of the BS. By using the manifold optimization framework, we design the precoder under the aforementioned criterion and constraint. Simulation results show that the fairness among all the users in this cell can be ensured. Compared with the precoders designed by half power beam-width (HPBW), the proposed scheme causes much less inter-cell interference and has a better synchronization performance.

In this paper, we investigate the omnidirectional precoding for three dimensional (3D) massive multi-input multi-output (MIMO) with uniform planar arrays (UPAs). The omnidirectional precoder is designed for public information transmission, where the channel state information (CSI) is usually not available at the base station (BS) and we would like to guarantee a performance to all users regardless of their angular positions. Thus, the first design objective of the precoding matrices is to satisfy the omnidirectional property, which means that the received mean power is constant at any angle. To ensure the power efficiency of each antenna, the per-antenna constant power constraint is also used in the design. Furthermore, the vectorized precoding matrices need to be mutually orthogonal to guarantee the spectral efficiency. The first two constraints can be satisfied by two dimensional Welti codes or two dimensional Golay arrays, whereas the third constraint is not necessarily satisfied by them. Furthermore, the methods to construct the Welti codes and Golay arrays are only given for certain array sizes. In this paper, we propose a novel and simple array design to construct the precoding matrices that satisfy the three properties. Based on the proposed array design, the design of the precoding matrices reduces to the design of a pair of complementary vectors having special structure, and the design of two sets of complementary orthonormal vectors with their aperiodic cross-correlation being zero. Finally, several examples of the omnidirectional precoding matrices for UPA generated by the proposed method are provided to verify the analytic results.

The low cost and versatility of optical devices make it possible to pack a large number of optical transceivers into arrays and exploit massive multiple-input multiple-output (MIMO) transmission in optical wireless communications. Nevertheless, optical massive MIMO presents several distinct challenges such as, the line-of-sight propagation and intensity modulation, incompatible with existing radio frequency massive MIMO techniques. This paper presents a networked optical massive MIMO system that consists of multiple base stations (BSs), each equipped with a transmit lens and an optical transmitter array, cooperatively serving a number of user terminals (UTs), each equipped with a receive lens and a photodetector array. We establish the optical massive MIMO channel model, analyze its asymptotic behavior, and evaluate the potential of networked optical massive MIMO on system throughput improvement. To achieve high throughput, we propose optical beam division multiple access (BDMA) transmission schemes under the total and per transmitter power constraints with the asymptotic optimality. Our results show that the system sum rate increases proportionally to the number of BSs and UTs using the optical BDMA transmission. Further numerical results show that the proposed optical massive MIMO system along with the optical BDMA transmission is able to achieve high throughput with low complexity.

We investigate energy efficiency (EE) optimization for single-cell massive multiple-input multiple-output (MIMO) downlink transmission with only statistical channel state information (CSI) available at the base station. We first show that beam domain transmission is favorable for energy efficiency in the massive MIMO downlink, by deriving a closed-form solution for the eigenvectors of the optimal transmit covariance matrix. With this conclusion, the EE optimization problem is reduced to a real-valued power allocation problem, which is much easier to tackle than the original large-dimensional complex matrix-valued precoding design problem. We further propose an iterative water-filling-structured beam domain power allocation algorithm with low complexity and guaranteed convergence, exploiting the techniques from sequential optimization, fractional optimization, and random matrix theory. Numerical results demonstrate the near-optimal performance of our proposed statistical CSI aided EE optimization approach.

In this paper, we investigate channel estimation and robust signal detection for uplink massive multi-input multioutput orthogonal frequency division multiplexing systems with in-phase and quadrature-phase imbalances. By processing the real and imaginary parts of the received signal individually, we perform minimum mean square error (MMSE) estimation for the effective channel. Adjustable phase shift pilots (APSPs) are used for reducing the pilot overhead. Motivated by the optimal conditions to achieve the lower bound of the MSE of the effective channel estimation, a pilot scheduling algorithm is provided. We further propose an MMSE criterion based detection scheme which is robust to the channel estimation error. An analytical expression for the asymptotic achievable sum rate of the proposed detection is derived via using operator valued free probability theory. The performance of the channel estimation with APSPs and the robust MMSE detection are demonstrated by numerical results.

Fingerprint technique is a promising enabler for mobile terminals (MTs) localization in rich scattering environments, such as urban areas and indoor corridors. In this paper, we investigate fingerprint-based localization for massive multiple- input multiple-output (MIMO) orthogonal frequency- division multiplexing (OFDM) systems with deep convolutional neural networks (DCNNs). By taking full advantage of the high resolution in the angle domain and the delay domain in massive MIMO-OFDM systems, we first propose an efficient angle-delay channel amplitude matrix (ADCAM) fingerprint extraction method. Then a DCNN enabled localization method is proposed, in which the modeling error for fingerprint similarity calculation can be overcome. Both DCNN classification and DCNN regression are considered. For practical implementation, a hierarchical DCNN architecture is proposed. Numerical simulation results demonstrate that DCNN performs well in achieving high localization accuracy as well as reducing storage overhead and computational complexity.

We investigate energy efficiency (EE) optimization for massive multiple-input multiple-output (MIMO) transmission in a single cell downlink scenario where the base station has only access to statistical channel state information (CSI) of the user terminals. To maximize the system EE, we first figure out a solution for the eigenvectors of the optimal transmit covariance matrices in a closed form. Notably, such a solution indicates that it is more favorable to perform energy efficient transmission in the beam domain for massive MIMO downlink, by which we reformulate the original complicated EE optimization precoding design to a simpler power allocation problem in the beam domain. Exploiting the approaches of sequential optimization, fractional optimization, and deterministic equivalent, we further propose an iterative algorithm for power allocation in the beam domain with guaranteed convergence to a stationary point. Numerical results demonstrate the superior performance and the fast convergence of our proposed statistical CSI aided EE optimization approach for massive MIMO downlink.

We investigate physical layer massive multiple-input-multiple-output (MIMO) multicasting transmit design with statistical channel state information at the base station. We first establish the relationship between the transmit design problems under the quality of service and max-min fair criteria. Then we focus on the transmit designs under the latter criterion. We show that the eigenvectors of optimal input covariance are given by the columns of the discrete Fourier transform matrix for the uniform linear array, which reveals the optimality of beam domain transmission in massive MIMO multicasting. We further propose a dual algorithm together with stochastic programming to specify the eigenvalues of input covariance. In addition, a simplified input covariance optimization by applying the deterministic equivalent technique is presented to reduce the complexity involved in stochastic programming. Simulation results demonstrate the performance of the proposed algorithms.

We investigate the multicast precoding design in multicell massive multiple-input multiple-output (MIMO) systems with finite-alphabet inputs. Focusing on the multicast transmission with only statistical channel state information at the base station, we derive a lower bound on the achievable ergodic rate for finite-alphabet inputs, from which we utilize the concave-convex procedure (CCCP) to devise a CCCP-based algorithm maximizing the minimum weighted achievable ergodic rate lower bound. The CCCP-based algorithm is proven to converge to a local optimum. Furthermore, exploiting the channel characteristic in massive MIMO systems, we prove that the optimal precoding vectors should be linear combination of columns of eigenmatrix of transmit correlation matrices in order to maximize the minimum weighted rate lower bound with lower computational complexity. Then, a relation-based algorithm is developed to obtain the optimal solution of the weighted max-min fairness (MMF) problem by using the duality between the MMF and quality of service problem. Numerical results demonstrate the tightness of the achievable ergodic rate lower bound and the significant performance of the proposed algorithms.

We investigate physical layer security in massive multiple-input multiple-output multicast transmission where the base station only knows the statistical channel state information of the legitimate user terminals and the eavesdropper. We first introduce a tight lower bound of the achievable secrecy multicast rate as the design objective. Then, we find the closed-form transmit directions, i.e., the eigenvectors of the optimal multicast transmit covariance matrix, which simplifies the matrix-valued multicast transmit strategy design into a beam domain power allocation problem. We further propose an efficient iterative power allocation algorithm with guaranteed convergence to a local optimal solution by invoking the concave-convex procedure. We also derive the deterministic equivalent of the optimization objective to reduce the computation complexity. Numerical results demonstrate the performance gains of the proposed approach over the conventional approach.

In visible light communication (VLC), using multiple colors is an efficient way to enhance data rate, leading to multi-color VLC (MC-VLC). However, the performance of MC-VLC is jeopardized by the spectral overlaps of different colors. Thin-film optical filters, as the key component of MC-VLC systems, are usually adopted to separate colors. The passband bandwidth (BW) and center wavelength (CWL) of optical filters are critical to mitigate the crosstalk among colors and, thus, must be carefully designed. Moreover, due to the intrinsic wavelength shift of the CWL with the varying of the angle of incidence, it is challenging to support mobility for MC-VLC. In this paper, we consider a joint design of multiple optical filters for MC-VLC by properly selecting the BW and CWL of each filter. We first investigate the optical filter design for a fixed receiver location. Then, to support mobility, we propose two robust optical filter designs, namely, statistically and worst case robust designs, which do not rely on the exact receiver location. Efficient methods are developed to solve the corresponding design problems and obtain the optimized optical filters. Compared with the existing optical filters, the proposed optical filters exhibits much better performance in various scenarios.

In this paper, we design the precoding matrix with broad coverage for massive multi-input multi-output public channels. In order to guarantee the efficient use of power amplifiers and improve the achievable ergodic rate, equal transmit power per-antenna and semi-unitary constraints on the precoding matrix are considered simultaneously. Within the framework of manifold optimization, the precoding matrix design under the above two constraints becomes an optimization problem over the intersection of the oblique manifold and the Stiefel manifold. We propose to use the steepest descent method on the intersection of these two manifolds to obtain the optimal solution. By using the alternating projections method, the search direction of the steepest descent method is derived. Meanwhile, the convergence analysis for the proposed approach is also provided. The proposed approach can be used for both omnidirectional and sector-shaped power pattern design. Simulation results show that the power pattern of our designed precoder has less variation in different spatial directions within the cell coverage compared with the existing Zadoff-Chu scheme.

In this paper, the design of robust linear precoders for the massive multi-input-multi-output (MIMO) downlink with imperfect channel state information (CSI) is investigated. The imperfect CSI for each UE obtained at the BS is modeled as statistical CSI under a jointly correlated channel model with both channel mean and channel variance information, which includes the effects of channel estimation error, channel aging, and spatial correlation. The design objective is to maximize the expected weighted sum-rate. By combining the minorize-maximize (MM) algorithm with the deterministic equivalent method, an algorithm for robust linear precoder design is derived. The proposed algorithm achieves a stationary point of the expected weighted sum-rate maximization problem. To reduce the computational complexity, two low-complexity algorithms are then derived. One for the general case, and the other for the case when all the channel means are zeros. For the later case, it is proved that the beam domain transmission is optimal, and thus the precoder design reduces to the power allocation optimization in the beam domain. Simulation results show that the proposed robust linear precoder designs apply to various mobile scenarios and achieve high spectral efficiency.

This paper presents a novel massive multiple-input multiple-output (MIMO) transmission in beam domain for optical wireless communications. The optical base station equipped with massive optical transmitters communicates with a number of user terminals (UTs) through a transmit lens. Focusing on LED transmitters, we analyze light refraction of the lens and establish a channel model for optical massive MIMO transmissions. For a large number of LEDs, channel vectors of different UTs become asymptotically orthogonal. We investigate the maximum ratio transmission and regularized zero-forcing precoding in the optical massive MIMO system and propose a linear precoding design to maximize the sum rate. We further design the precoding when the number of transmitters grows asymptotically large and show that beam division multiple access (BDMA) transmission achieves the asymptotically optimal performance for sum rate maximization. Unlike optical MIMO without a transmit lens, BDMA can increase the sum rate proportionally to$2K$and$K$under the total and per transmitter power constraints, respectively, where$K$is the number of UTs. In the non-asymptotic case, we prove the orthogonality conditions of the optimal power allocation in beam domain and propose efficient beam allocation algorithms. Numerical results confirm the significantly improved performance of our proposed beam-domain optical massive MIMO communication approaches.

This paper investigates the interference coordination for multi-cell full-dimension multiple-input multiple-output (FD- MIMO) cellular networks. We assume only statistical channel state information (CSI) at each base station (BS). Since the edge users suffer from much more inter-cell interference compared to the center users, we focus on the interference coordination for cell-edge regions. We propose a fractional-frequency-reuse (FFR) scheme for the considered systems, in which all the edge users share the same frequency resources. Based on the proposed FFR scheme, a joint 3D beamforming transmission algorithm is proposed to mitigate the inter-cell interference for the cell-edge users. The simulation results show that the proposed interference coordination strategy performs well in terms of the cell-edge rate, as well as the achievable sum rate.

Air-to-ground (A2G) communication plays an important role in ensuring reliable communication links between unmanned aerial vehicles (UAVs) and ground terminals. This paper presents a wideband truncated ellipsoidal shaped scattering region (TESR) geometry based stochastic model (GBSM) for Multiple-Input-Multiple-Output (MIMO) A2G channels. The proposed model contains a line-of-sight (LoS) component, a ground reflection component, and truncated ellipsoid scattering components. Based on the proposed GBSM, some important statistical properties like space-time-correlation-function (STCF) and Doppler power spectrum density (PSD) are derived. The impacts of elevation angle and UAV altitude on A2G channel characteristics are analyzed. Finally, excellent agreement is achieved between measurement data and simulation results of temporal auto correlation functions (ACFs), demonstrating applicability of the proposed model.

In this paper, we design the precoding matrix with broad coverage for massive multi-input multi-output (MIMO) public channels. To sufficiently utilize the power amplifier (PA) capacity of the base station (BS), all the rows of the precoding matrix should have the same 2-norm to guarantee equal transmit power on each antenna. In the meanwhile, the precoding matrix is semi-unitary, which can maximize the achievable ergodic rate for the independent and identically distributed (i.i.d.) channel. Within the framework of manifold optimization, the precoding matrix design under the above two constraints becomes an optimization problem over the intersection of the oblique manifold and the Stiefel manifold. We propose to use the steepest descent method on the intersection of these two manifolds to obtain the optimal solution. By using the alternating projections method, the search direction of the steepest descent method is derived. In the meanwhile, the convergence analysis for the proposed approach is also provided. Simulation results show that the power pattern of our designed precoder has less variation in different spatial directions within the cell compared with the existing schemes.

Link Adaptation can maximize system throughput while maintaining transmission reliability. With the growing demand for high-speed data transmission, multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies have been widely used in wireless communication systems. However, performing link adaptation in MIMO systems is challenging due to the complexity of channel and coupling among equalization, precoding, spatial mode, modulation and coding scheme (MCS). In this paper, we present a link adaptation scheme in MIMO systems through machine learning algorithms to maximize spectral efficiency while maintaining transmission reliability. We propose to use autoencoder model to extract feature from channel state information (CSI), combined with logical regression algorithms to select modulation and coding scheme. Spatial mode can be chosen based on the objective of maximizing the spectral efficiency. Simulation results demonstrate the improved performance and validate the application of the proposed learning based framework in MIMO systems.

We investigate robust multigroup multicast precoding for frame-based multi-beam satellite communication systems with full frequency reuse. To mitigate the effect of outdated channel state information (CSI), we first investigate robust multigroup multicast precoding that minimizing per beam transmission power while guaranteeing a predetermined average signal to interference plus noise ratio at each user. We then propose a low complexity precoder for it based on semidefinite relaxation and Gaussian randomization techniques. Simulation results demonstrate that the proposed robust approach can provide substantial performance gains over the conventional approach in multibeam satellite communication systems.

In this paper, a low-complexity compressive channel estimation method is proposed for massive multiple-input multiple-output (MIMO) uplink transmission over frequency selective fading channels. Based on the physical channel model, the structured sparsity in the beam-delay domain is investigated and the overcomplete discrete Fourier transform matrix is utilized to mitigate channel power leakage in the beam-delay domain. Specifically, a nonorthogonal uplink pilot design scheme is employed to reduce the pilot overhead, then the compressive sensing model is established. By exploiting the structured sparsity and energy concentration property of the beam-delay domain channel, the energy-concentration based channel estimation algorithm is proposed by utilizing the sparsity level of channels. Simulation results demonstrate that the newly proposed algorithm outperforms other existing estimators with relatively low complexity.

In this paper, we consider the power allocation problem for downlink multiceli massive multiple-input multiple-output (MIMO) communications over Rician fading channels. Each link between a user equipment (UE) and a base station (BS) forms a jointly correlated Rician channel, on which some properties of the channel covariance matrices in the massive MIMO scenario are presented. Based on these properties, assuming perfect channel state information (CSI) at UEs and statistical CSI, i.e. the channel coupling matrix (CCM) and the line-of-sight (LOS) component, at BSs, we design a near-optimal power allocation algorithm in terms of maximizing the deterministic equivalent of the ergodic sum-rate in the beam domain. The closed-form objective function of the power allocation problem turns out to be a difference of concave functions, thus we search for the solutions iteratively. Numerical results show that the proposed method performs well in terms of achievable sum-rate.

Visible light communication (VLC) equipped with multiple light-emitting diodes (LEDs) can provide near ubiquitous indoor coverage for both communication and illumination. This paper introduces the concept of biased beamforming to explore the full potential of multicarrier multi-LED VLC systems. Biased beamforming includes two components to be jointly designed: a direct current (DC) bias on each LED and a beamforming vector on each subcarrier. We first analyze the impact on clipping in multi-LED VLC systems. Next, we consider the joint optimization of beamforming and biasing to maximize data rate. We find the optimal beamformer and analytically characterize its structure. We further optimize the bias on each LED and provide the globally optimal solution in closed form for flat channels, which leads to several critical insights that are helpful for practical systems. We also derive a simplified near-optimal solution for dispersive channels and develop an efficient method for biased beamforming. The performance of the proposed biased beamforming design outperforms existing solutions.

The unprecedented growth in the number of mobile data and connected machines ever-fast approaches limits of fourth generation technologies to address this enormous data demand. Therefore, the development of the fifth generation (5G) wireless communication technologies is a priority issue currently. The evolution towards 5G wireless communications will be a cornerstone for realizing the future human-centric and connected machine-centric networks, which achieve near-instantaneous, zero distance connectivity for people and connected machines. On the other hand, wireless networks have been widely used in civilian and military applications and become an indispensable part of our daily life. People rely heavily on wireless networks for transmission of important/private information, such as credit card information, energy pricing, e-health data, command, and control messages. Therefore, security is a critical issue for future 5G wireless networks. Physical layer security techniques can be used to either perform secure data transmission directly or generate the distribution of cryptography keys for conventional cryptography techniques in the 5G networks. With careful management and implementation, physical layer security can be used as an additional level of protection on top of the existing security schemes. As such, they will formulate a well-integrated security solution together that efficiently safeguards the confidential and privacy communication data in 5G wireless networks. The main goal of this IEEE JSAC Special Issue on “Physical Layer Security for 5G Wireless Networks” is to bring together leading researchers in both academia and industry from diversified backgrounds to advance the theory and practice of physical layer security for 5G wireless networks.

Physical layer security which safeguards data confidentiality based on the information-theoretic approaches has received significant research interest recently. The key idea behind physical layer security is to utilize the intrinsic randomness of the transmission channel to guarantee the security in physical layer. The evolution toward 5G wireless communications poses new challenges for physical layer security research. This paper provides a latest survey of the physical layer security research on various promising 5G technologies, including physical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, and so on. Technical challenges which remain unresolved at the time of writing are summarized and the future trends of physical layer security in 5G and beyond are discussed.

The unprecedented growth in the number of mobile data and connected machines ever-fast approaches limits of fourth generation technologies to address this enormous data demand. Therefore, the development of the fifth generation (5G) wireless communication technologies is a priority issue currently. The evolution towards 5G wireless communications will be a cornerstone for realizing the future human-centric and connected machine-centric networks, which achieve near-instantaneous, zero distance connectivity for people and connected machines. On the other hand, wireless networks have been widely used in civilian and military applications and become an indispensable part of our daily life. People rely heavily on wireless networks for transmission of important/private information, such as credit card information, energy pricing, e-health data, command, and control messages. Therefore, security is a critical issue for future 5G wireless networks. Physical layer security techniques can be used to either perform secure data transmission directly or generate the distribution of cryptography keys for conventional cryptography techniques in the 5G networks. With careful management and implementation, physical layer security can be used as an additional level of protection on top of the existing security schemes. As such, they will formulate a well-integrated security solution together that efficiently safeguards the confidential and privacy communication data in 5G wireless networks. The main goal of this IEEE JSAC Special Issue on “Physical Layer Security for 5G Wireless Networks” is to bring together leading researchers in both academia and industry from diversified backgrounds to advance the theory and practice of physical layer security for 5G wireless networks.

Provides an overview of the technical articles and features presented in this issue. Our regular papers this month focus on 5G related topics such as multipleinput– multipleoutput transmission using finite input signals, and achieving ultrareliable and low-latency wireless communication.

Multiple antennas have played an essential role in spatial multiplexing and diversity transmission for a wide range of communication applications. Most advances in the design of high-speed wireless multiple-input-multiple-output (MIMO) systems have been based on information-theoretic principles that demonstrate how to efficiently transmit signals conforming to Gaussian distribution. However, although the Gaussian signal is capacity-achieving, practical systems transmit signals belonging to finite and discrete constellations. Therefore, capacity-achieving transceiver processing based on a Gaussian input signal can be quite suboptimal for practical MIMO systems with discrete constellation input signals. To address this shortcoming, this paper aims to provide a comprehensive overview of MIMO transmission design with finite input signals. It first summarizes existing fundamental results for MIMO systems with finite input signals. Next, focusing on basic point-to-point MIMO systems, it examines transmission schemes based on the three most important criteria for communication systems: mutual-information-driven designs, mean-square-error-driven designs, and diversity-driven designs. In particular, a unified framework is developed for the design of low-complexity transmission schemes applicable to massive MIMO systems in forthcoming 5G wireless networks for the first time. Furthermore, adaptive transmission designs are proposed that switch among these criteria based on channel conditions to formulate the best transmission strategy. A survey is then given of transmission designs with finite input signals for multiuser MIMO scenarios, including MIMO uplink transmission, MIMO downlink transmission, MIMO interference channel, and MIMO wiretap channel. Additionally, transmission designs with finite input signals are discussed for other multi-antenna systems. Finally, a number of technical challenges that remain unresolved at the time of writing are highlighted, and future trends in transmission design with finite input signals are discussed.

In this paper, we design the precoding matrices at the base station side and the combining matrices at the user terminal side for initial downlink synchronization in millimeter wave massive multiple-input multiple-output systems. First, we demonstrate two basic requirements for the precoding and combining matrices, including that all the entries therein should have constant amplitude under the implementation architecture constraint, and the average transmission power over the total K time slots taking for synchronization should be constant for any spatial direction. Then, we derive the optimal synchronization detector based on generalized likelihood ratio test. By utilizing this detector, we analyze the effect of the precoding and combining matrices to the missed detection probability and the false alarm probability, respectively, and present the corresponding conditions that should be satisfied. It is shown that, both of the precoding and combining matrices should guarantee the perfect omnidirectional coverage at each time slot, i.e., the average transmission power at each time slot is constant for any spatial direction, which is stricter than the second basic requirement mentioned earlier. We also show that such omnidirectional precoding matrices and omnidirectional combining matrices exist only when both of the number of transmit streams and the number of receive streams are equal to or greater than two. In this case, we propose to utilize Golay complementary pairs and Golay-Hadamard matrices to design the precoding and combining matrices. Simulation results verify the effectiveness of the propose approach.

In this paper, we investigate the inter-tier interference coordination for a heterogeneous network (HetNet), where both macro and small cells work in the frequency-division duplexing mode and share the same spectrum. A 2-D large-scale antenna array is deployed at a macro-base station (BS), while conventional multiple antenna array is deployed at each small cell. First, we derive an approximation of macro-users' signal-to-interference-plus-noise ratio (SINR), under the assumption of only statistical channel-state information (CSI) at macro-BS, and a 3-D beamforming transmission scheme is applied for macro-users. A lower-bound of small-cell users' expected SINR is also derived using the property of complex Wishart matrix. Based on these, simple metrics are derived to measure the inter-tier interference. Then, new interference coordination algorithms are proposed to achieve a good tradeoff for the performance and traffic between macro-cells and small cells. The proposed algorithms require only a few additional statistical CSI. Moreover, we derive tractable ergodic rate approximation of both the macro-cell and small-cell users which are shown to match well with the Monte Carlo results.

In this paper, we design space-time block codes (STBCs) to broadcast the common information omnidirectionally in a massive multiple-input multiple-output downlink. To reduce the burden of the downlink channel estimation and achieve partial spatial diversity from base station (BS) transmit antennas, we propose channel-independently precoded low-dimensional STBC. The precoding matrix and the signal constellation in the low-dimensional STBC are jointly designed to guarantee omnidirectional coverage at any instant time and sufficiently utilize the power amplifier capacities of BS transmit antennas, and at the same time, achieve the full diversity of the low-dimensional STBC. Under this framework, several designs are presented. To provide transmit diversity order of two, a precoded Alamouti code is proposed, which has a fast symbolwise maximum-likelihood (ML) decoding. To provide transmit diversity order of four, a precoded quasi-orthogonal STBC is proposed, which has a pairwise ML decoding. Moreover, a precoded no-zero-entry Toeplitz code and a precoded no-zero-entry overlapped Alamouti code are also proposed. These two codes can achieve a higher diversity order with linear receivers.

This paper proposes a novel concept of biased beamforming to explore the full potential of multicarrier visible light communication (VLC) systems equipped with multiple light-emitting diodes (LEDs). Biased beamforming requires joint optimization of both: a direct current bias on each LED and a beamforming vector on each subcarrier. We first analyze the impact of clipping in multi-LED multicarrier systems, before carrying out the joint optimization of beamforming and biasing. We derive the optimal biased beamforming in closed form, along with several critical insights that are helpful for practical systems. The proposed biased beamforming strategy outperforms existing solutions, as shown by our simulation results.

Fingerprint techniques are promising localization strategies in rich scattering environments, such as urban areas and indoor corridors. However, most existing approaches rely on multiple base station (BS) cooperation and suffer from multipath propagation. In this paper, we propose a fingerprint based single-site localization method for massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The new angle delay channel power matrix (ADCPM) fingerprint is extracted from instantaneous channel state information (CSI) by taking full advantage of the high resolution in the angle and delay domains for massive MIMO-OFDM systems. The applicable fingerprint similarity criterion，, as well as location estimation method, are proposed to reduce measurement, storage, and matching overheads. Numerical results demonstrate the desirable performance of the proposed localization method.

Most channel models in the literature are based on the assumption that the mobile station (MS) moves along a straight line with a constant speed. In a realistic environment, the MS may experience changes in their speeds and trajectories. In this paper, a three-dimensional (3-D) non-stationary wideband multiple-input multiple-output (MIMO) channel model allowing for velocity variations of the MS is proposed. The parameters are obtained from the WINNER+ channel model to make the simulations more realistic. Statistical properties including spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), and Doppler power spectral density (PSD) are derived and analyzed. Our findings show that a variation of the velocity of the MS has a significant impact on the statistical properties of the channel model. Furthermore, the proposed channel model can be used as a basic framework for future non-stationary channel modeling.

Due to radio-frequency (RF) circuit mismatch, the channel reciprocity of time-division duplex massive multiple-input multiple-output system is impaired. Under this condition, there exist several different approaches for base station (BS) to obtain the downlink (DL) channel estimate based on the minimum mean-square-error (MMSE) estimation method. We show that with the RF mismatch parameters BS will obtain the same DL channel estimates via these different approaches. As the DL channel estimate is usually imperfect, we propose an MMSE based multiuser precoding technique, which is robust to the estimation error. Furthermore, we derive an asymptotic approximation of the ergodic sum rate for the robust MMSE precoding using the large dimensional random matrix theory, which is tight as the numbers of both antennas at BS and user terminals tend to infinity with a fixed non-zero and finite ratio. Our results are verified by the simulations.

In this paper, the design of linear precoders for simultaneously wireless information and power transfer (SWIPT) over multi-input multi-output (MIMO) broadcast channels with discrete input signals is investigated. The considered system model consists of one base station (BS), one information receiver (IR) and one energy receiver (ER). The design objective is to maximize the input-output mutual information of the IR subject to the harvested energy requirement for the ER. The structure of the optimal precoder is derived by using the methods of manifold optimization, and an algorithm is proposed to find the optimal precoder. Simulation results show that the proposed algorithm can achieve better performance than the time sharing scheme and the optimal precoder designed for Gaussian inputs.

Two-stage precoding is a promising transmission strategy for multi-user frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its large multiplexing gain with overhead reduction in both downlink channel estimation and channel state information (CSI) feedback. The performance of existing two-stage precoding schemes mainly depends on appropriate selection and clustering of the users, which is sometimes difficult to realize in a realistic scenario with limited number of users having different covariance eigenspace. In this paper, we propose a new agglomerative clustering method for user grouping which can be easily implemented in realistic scenario. We also develop an average signal-to-leakage-plus-noise ratio (SLNR) based downlink group scheduling method to achieve combination of user groups in a particular time-frequency slot. Numerical results validate the performance improvement of the proposed methods over existing methods.

We propose beam division multiple access (BDMA) with per-beam synchronization (PBS) in time and frequency for wideband massive multiple-input multiple-output (MIMO) transmission over millimeter-wave (mmW)/Terahertz (THz) channels. Based on a physically motivated beam domain channel model, we first show that the envelopes of the beam domain channel elements tend to be independent of time and frequency when both the numbers of antennas at base station and user terminals (UTs) tend to infinity. Motivated by this, we then propose PBS for massive MIMO. We show that both the effective delay and Doppler frequency spreads of massive MIMO channels with PBS are reduced by a factor of the number of UT antennas compared with the conventional synchronization approaches. Subsequently, we apply PBS to BDMA and investigate beam scheduling to maximize the achievable ergodic rates for BDMA. Simulation results verify the effectiveness of BDMA with PBS for mmW/THz massive MIMO in typical mobility scenarios.

Owing to the advantage of low-complexity, message passing algorithms have been extensively studied for massive multiple-input multiple-output (MIMO) detection. Most of message passing algorithms assume that the channel state information (CSI) is completely known by receiver, which is unrealistic in wireless communication. In this paper, we investigate a robust approximate message passing (RAMP) detection algorithm with imperfect CSI based on minimizing Bethe free energy. For given pilot structure and channel estimation methods, the results of channel estimation should be denoted by a probability density function of CSI rather than its estimation values. Based on such observations, the MIMO detection issue in the presence of channel estimation error is formulated as a Bethe free energy minimization subject to appropriately imposed constraints and the given statistical model of CSI. The Lagrange multiplier theory is employed to identify the stationary points of the constrained Bethe free energy, which give us back the fixed-point equations. This results in an iterative algorithm for detection in massive MIMO systems. Numerical experiments corroborate its superiority in terms of SER performance when the CSI is imperfect.

In this paper, we investigate the uplink multi-user generalized approximate message passing (GAMP) detection for massive MIMO system. As practical channels are spatially correlated, the conventional GAMP performs poorly and its fixed points fall into locally-optimal solutions. In order to analyse the fixed points of GAMP, we regard the detection problem of massive MIMO systems as the Gibbs free energy minimization and derive GAMP by Bethe method to upper-bound Gibbs free energy. To improve the convergence performance of GAMP detection, we propose linear preprocessing with row-orthogonalization for GAMP (RO-GAMP) at the receiver before GAMP detection is executed. Firstly, we derive the structure of linear preprocessing consisted of four design principles: orthogonality of rows of sensing matrices, irrelevance of noise, low-dimension of observation vector and equivalence of Bethe free energy minimization. Secondly, some conditions are presented on preprocessing matrix to satisfy these design principles. Then, we propose two optimal preprocessing matrices for RO-GAMP. When these two matrices are used for massive MIMO OFDM with slow-varying channels, a low-complexity preprocessing method is presented finally. Our numerical results demonstrate the advantage of RO- GAMP over GAMP, in terms of symbol error rate (SER) and convergence rate, for practical massive MIMO channels which exhibits spatial correlation.

The recent concept of beamspace multiple-input multiple-output (MIMO) is capable of significantly reducing the number of radio-frequency (RF) chains required by millimeter-wave (mmWave) massive MIMO systems. However, the fundamental limit of the existing beamspace MIMO is that, the number of supported users cannot be higher than the number of RF chains using the same time-frequency resources. To break this limit, beamspace MIMO is integrated with non-orthogonal multiple access (NOMA) in the proposed MIMO-NOMA system in this paper, where the number of supported users can be higher than the number of RF chains. To reduce the inter-beam interference, a transmit precoding (TPC) scheme based on the principle of zero-forcing (ZF) is designed. Furthermore, a dynamic power allocation scheme is proposed for maximizing the achievable sum rate. Moreover, a low-complexity iterative optimization algorithm is conceived for dynamic power allocation. Simulation results show that the proposed beamspace MIMO-NOMA achieves a higher spectrum and energy efficiency than the existing beamspace MIMO for mmWave communications.

We propose beam division multiple access (BDMA) with per-beam synchronization (PBS) in time and frequency for wideband massive multiple-input multiple-output (MIMO) transmission over millimeter-wave (mmW)/Terahertz (THz) bands. We first introduce a physically motivated beam domain channel model for massive MIMO and demonstrate that the envelopes of the beam domain channel elements tend to be independent of time and frequency when both the numbers of antennas at base station and user terminals (UTs) tend to infinity. Motivated by the derived beam domain channel properties, we then propose PBS for mmW/THz massive MIMO. We show that both the effective delay and Doppler frequency spreads of wideband massive MIMO channels with PBS are reduced by a factor of the number of UT antennas compared with the conventional synchronization approaches. Subsequently, we apply PBS to BDMA, investigate beam scheduling to maximize the ergodic achievable rates for both uplink and downlink BDMA, and develop a greedy beam scheduling algorithm. Simulation results verify the effectiveness of BDMA with PBS for mmW/THz wideband massive MIMO systems in typical mobility scenarios.

In this paper, we investigate the design of linear precoders for simultaneously wireless information and power transfer (SWIPT) in a multi-input multi-output (MIMO) broadcasting system with discrete input signals. The considered system model consists of one base station (BS), one information receiver (IR), and one energy receiver (ER). The design objective is to maximize the input-output mutual information of the IR subject to the power constraint and the harvested energy requirement for the ER. We derive the structure of the optimal linear precoder by using manifold optimization, and propose an algorithm to find the optimal precoder. Simulation results show that the proposed algorithm can achieve better performance than the time sharing scheme and the Gaussian optimal precoder when Gaussian inputs are replaced by discrete input signals.

Massive multiple-input multiple-output (MIMO) systems are expected to operate in the frequency-division duplex (FDD) mode, which is feasible in the channel environment with limited scattering. Since accurate channel estimation is critical for gaining unprecedented capacity, we investigate beam-domain channel estimation and feedback for FDD massive MIMO systems. In particular, we focus on the threshold-based method for channel estimation, where an enhanced estimator is proposed to exploit the common support among all beam-domain channels. For threshold-based estimation, we derive its closed-form mean-squared error (MSE) expression, and obtain an optimal threshold as a function of sparsity, noise variance, and channel variance, and a simplified threshold, which is a function of noise variance only. For the enhanced estimator, we present a threshold to identify the common support, with which an algorithm is designed to improve the estimation accuracy. As for channel feedback, we suggest to feed back only significant elements (above the given threshold) in the beam domain. Numerical results validate our derived MSE expression and demonstrate the superior performance of proposed threshold-based estimators.

This paper considers the beamforming design for a multiuser multiple-input single-output (MU-MISO) downlink with an arbitrary number of (context-specific) shaping constraints. In this setup, the state-of-the- art beamforming schemes cannot attain the well-known performance bound promised by the semidefinite program (SDP) relaxation technique. To close the gap, we propose a linear space-time beamforming (LiST-BF) scheme, consisting of a circulant space-time symbol mapper followed by the beamforming design with orthogonality constraints. It is shown that the proposed LiST-BF scheme can perform general rank-$K$ beamforming for user symbols in a low-complexity and structured manner. Sufficient conditions are derived to guarantee that the LiST-BF scheme always achieves the SDP bound for linear beamforming schemes. Based on such conditions, an efficient algorithm is then developed to obtain the optimal LiST-BF solution in polynomial time. Numerical results demonstrate that the proposed scheme enjoys substantial performance gains over the existing alternatives.

Channel state information (CSI) is usually necessary for downlink precoding, power allocation, etc. in multiple-input multiple-output (MIMO) systems. When the base stations (BS) are equipped with massive elements, the training overhead required by conventional CSI estimation methods becomes overwhelming, leading to unacceptable loss of spectrum efficiency. In this paper, we investigate pilot design and CSI acquisition issues for downlink massive MIMO transmission. By exploiting the sparsity of beam domain channel, we first derive the optimal pilot structure in case of non-orthogonal pilots with compressive sensing (CS) framework. A deterministic sensing matrix design method is then proposed that satisfies the restricted isometry property (RIP). As beam domain channels are usually approximately sparse, we propose a modified subspace pursuit (SP) algorithm to recover the signals with tradeoff between noise and approximation error. Numerical results demonstrate that the proposed sensing matrices have better performance than conventional random CS matrices, and the new channel estimation scheme achieves significant performance improvement with reduced pilots consumption over conventional least square (LS) method.

In this paper, we investigate the sum rate performance of a downlink transmission scheme for massive multiple-input multiple-output (MIMO) systems with a two-dimensional (2D) antenna array at the base station (BS). Assuming Rician fading conditions, we derive some properties of the channel's line-of-sight (LOS) component. Using these properties and under the assumption of only LOS information at the BS, the optimal beamforming vector for each user as well as the main guidelines for user scheduling are derived to maximize an approximation of the ergodic sum rate. Based on the optimal beamforming vector and the guidelines, a statistical three-dimensional (3D) beamforming downlink transmission scheme exploiting only the LOS information of each user is proposed. Numerical results show that the proposed scheme can achieve considerable sum rates while requiring much less channel state information (CSI) overhead at the BS, and is more attractive than the traditional zero-forcing and match-filtering schemes when the CSI at the BS is imperfect.

This paper investigates low-complexity multi-user detectors for massive MIMO systems in practical channels. Beam domain message passing algorithm on the sparse-graph is proposed by exploiting the channel sparsity in beam domain, which has much lower computational complexity compared to the dense-graph based detections. As the massive MIMO channels are usually not i.i.d Gaussian distribution and central limit theorem (CLT) will no longer apply, conventional approximate message passing detector (AMP) may diverge. To improve the performance of AMP in practical channels, we further propose energy spread transform (EST) based massive MIMO transmission, which guarantee that the equivalent channels are i.i.d. distributed. Furthermore, by deriving the state evolution (SE) equations, the convergence analysis of the proposed detector is also provided. Simulation results show that, the proposed detector outperforms the conventional AMP and minimum mean square error (MMSE) detectors, and has fast convergence rate.

Beam division multiple access (BDMA) transmission scheme is one of the transmission schemes for massive multiple-input multiple-output (MIMO) systems. However, when taking radio frequency (RF) gains of different antennas into account, the performance of BDMA transmission may degrade severely. In this paper, we investigate the calibration method of RF mismatches for BDMA transmission. We first analyze the performance impact of amplitude mismatches and phase mismatches respectively. Then, we focus on the main factor, phase mismatches. After deriving the relationship between sparsity of channel vector and its L1-norm, we propose a phase calibration method based on L1-norm minimization. In the proposed method, initial feasible solution is obtained in one sub-carrier and channel state information (CSI) in multiple sub-carriers is applied to obtain the calibration matrix. Simulation results verify the feasibility of the proposed method and show the significant improvement of system performance.

In this paper, we propose a user scheduling and beam allocation algorithm for massive MIMO frequency-division-duplexing (FDD) systems by using a two-stage precoding method. We demonstrate that the precoding in beam domain is optimal when different users transmit data in non-overlapping beams. To satisfy the condition of optimality, a greedy algorithm with low complexity is proposed to select users and allocate beams for transmission based on statistical channel state information (CSI) by maximizing the deterministic equivalent approximation of the average sum rate. Simulations demonstrate the near-optimal performance of the proposed two-stage precoding method.

This paper investigates the pilot design and channel estimation issue for massive MIMO-OFDM uplink transmission. By exploiting the channel sparsity in the beam-delay domain, the channel estimation issue for multiuser uplink transmission is formed into a sparse compressed sensing (CS) model. We propose a cyclic shift pilot scheme, which satisfies Restricted Isometry Property under the CS frameworks. As the sparsity is efficiently utilized, the proposed scheme significantly reduces the pilot overhead, especially when the number of users becomes large. Furthermore, based on approximate message passing (AMP) algorithm, an approximate Bayesian inference can be conducted for channel estimation with low complexity. Simulation results show that the proposed AMP-based algorithm with the pilot scheme outperforms several existing baselines, especially when the BS is equipped with large antenna arrays.

In this paper, we propose Subcarrier Index Modulation (SIM) OFDM for multiuser uplink transmission in MIMO systems. In the proposed scheme, information bits are divided into two parts according to the quadrature amplitude modulation order: the first part of bits are mapped to the indexes of inactive subcarriers implicitly carrying information, the remaining part of bits are mapped onto the signal constellation. To mitigate multiuser interference at the base station, an iterative detector based on the Generalized Approximate Message Passing (GAMP) algorithm is developed. Compared to the classical multiuser OFDM system, our proposed scheme can lower the peak to average power ratio (PAPR), improve both the energy efficiency (EE) and detection performance without sacrificing the spectral efficiency (SE). The simulation results confirm the efficiency of our proposed scheme.

In this paper, we derive the Bayes-Optimal estimator based on approximate message passing (AMP) algorithm in massive multiple-input multiple-output (MIMO) systems, which requires statistical channel state information (CSI). According to the analysis of channel model in beam domain, the covariance matrix is derived for CSI acquisition. With the aid of statistical CSI, the convergence of the proposed algorithm has significant improvement in comparison with which use the expectation-maximization (EM) algorithm to fit the statistical CSI. Simulations show great mean squared error (MSE) performance that approximates the Minimum Mean Square Error (MMSE) estimator, and better convergence performance than other AMP algorithm can be achieved. Besides, the results prove that performance of the random pilot in this algorithm is close to that of the orthogonal pilot based on Zadoff-Chu sequences.

In this paper, we investigate a downlink transmission algorithm for single-cell multiuser systems with two-dimensional (2-D) large-scale antenna array at the base station (BS) over Rician fading channels. We first derive some properties of the channel's line-of-sight (LOS) component in the large-scale antenna array scenario. Next, based on these properties and under the assumption of only statistical channel state information (CSI), i.e., the LOS component and Rician $K$-factor, at the BS, we derive the optimal beamforming vector for each user in maximizing an approximation of the ergodic sum rate. The main guidelines for user scheduling are also presented. Then, a three-dimensional (3-D) beamforming downlink transmission algorithm exploiting only the statistical CSI of each user is proposed. For this algorithm, we derive an exact analytical closed-form expression for the achievable ergodic rate and present tractable approximations. Based on our analysis, we gain some valuable insights. The proposed algorithm is shown to perform well in achieving considerable sum rate while requiring much less CSI at the BS. It requires three scalar values at the BS for each user, and can achieve an ergodic sum rate closer to the ergodic sum rate achieved by the matched-filter precoding with perfect CSI at the BS.

We consider a low complexity polynomial expansion (PE) detector in a massive multiple-input multiple-output (MIMO) uplink channel. In contrast to most massive MIMO systems in the literature, where single antenna user equipments (UEs) are assumed, multiple antenna UEs are employed in this paper. Moreover, the channel between a base station (BS) and a UE is a jointly correlated Rician fading channel. The PE detector reduces the computational complexity of the minimum mean square error (MMSE) detector by replacing the matrix inversion with an approximate matrix polynomial. The coefficients of the approximate matrix polynomial are computed from the deterministic equivalents of the moments of the channel Gram matrix. We use operator-valued free probability, which is a more general version of free probability, to derive the deterministic equivalents. In particular, we use the operator-valued moment-cumulant formula. The proposed low complexity PE detector is easy to compute. Simulation results show that the proposed detector can achieve performance close to the MMSE detector.

Common signals in public channels of cellular systems should be transmitted omnidirectionally from the base station (BS) to ensure cell-wide coverage. In this paper, we propose an omnidirectional precoding (OP) based transmission for public channels in massive MIMO systems, which leads to a significant reduction in the downlink pilot overhead while providing omnidirectional signaling. For this OP based transmission, we present three necessary conditions that the OP matrix should satisfy, to meet the requirements for omnidirectional transmission for reliable cell-wide coverage, equal average power on each antenna to sufficiently utilize all the available power amplifier capacities of BS antennas, and achievable ergodic rate maximization for the i.i.d. channel, respectively. Then, several examples of the OP matrix satisfying the three necessary conditions simultaneously are designed by utilizing the Zadoff-Chu sequence and its properties. We also analyze the system performance in terms of achievable ergodic rate, outage probability, and peak-to-average power ratio (PAPR) for these designs. It is shown that a dedicated design of the OP matrix has the following advantages: 1) it asymptotically maximizes the achievable ergodic rate, maximizes the achievable diversity order, and minimizes the outage probability in the large-scale array regime, not only for the i.i.d. channel, but also for spatially correlated channels; 2) it preserves the PAPR of the transmitted signal after precoding.

In this paper, a free deterministic equivalent is proposed for the capacity analysis of the multi-input multi-output (MIMO) multiple access channel (MAC) with a more general channel model compared to previous works. In particular, a MIMO MAC with one base station (BS) equipped with several distributed antenna sets is considered. Each link between a user and a BS antenna set forms a jointly correlated Rician fading channel. The analysis is based on operator-valued free probability theory, which broadens the range of applicability of free probability techniques tremendously. By replacing independent Gaussian random matrices with operator-valued random variables satisfying certain operator-valued freeness relations, the free deterministic equivalent of the considered channel Gram matrix is obtained. The Shannon transform of the free deterministic equivalent is derived, which provides an approximate expression for the ergodic input-output mutual information of the channel. The sum-rate capacity achieving input covariance matrices are also derived based on the approximate ergodic input-output mutual information. The free deterministic equivalent results are easy to compute, and simulation results show that these approximations are numerically accurate and computationally efficient.

In this paper, a low complexity polynomial expansion (PE) detector for massive multi-input multi-output (MIMO) uplink transmissions is proposed. In contrast to most massive MIMO systems in the literature, where single-antenna user equipments (UEs) are assumed, multiple-antenna UEs are employed in this paper. Furthermore, each link between a user and the base station forms a jointly correlated Rician fading channel. The PE detector reduces the complexity of the minimum mean square error (MMSE) detector by replacing the matrix inversion with an approximate matrix polynomial. In the design of the low complexity PE detector, the approximations of the moments of the channel Gram matrix are needed. We use operator- valued free probability to derive these approximations. The low complexity PE detector is easy to compute. Simulation results show that it can achieve performance close to the MMSE detector.

Common signals in public channels of cellular systems should be transmitted omnidirectionally from the base station (BS) to ensure cell-wide coverage. In this paper, we propose two omnidirectional space-time block codes (STBCs), being referred to as precoded Alamouti code and precoded quasi-orthogonal STBC (QOSTBC), respectively, to provide omnidirectional signaling with spatial diversity for public channels in massive MIMO systems. Both of these two schemes are with low downlink pilot overhead and can guarantee omnidirectional transmission and equal-power transmission per antenna simultaneously, to ensure reliable cell-wide coverage and sufficiently utilize all the available power amplifier capacities at BS antennas, respectively. The precoded Alamouti code provides diversity order 2 and has fast single-symbol maximum-likelihood (ML) decoding. The precoded QOSTBC provides a higher diversity order 4, but at the expense of a higher decoding complexity, since the ML decoding must be done on each pair of the modulation symbols.

In this paper, we propose a free deterministic equivalent for the capacity analysis of multi-input multi-output (MIMO) multiple access channel (MAC) with distributed antenna sets. In the analysis, we use the operator-valued free probability framework, which is much more straightforward than the widely used methods, i.e., the Bai and Silverstein method and the Gaussian method. By replacing independent random Gaussian variables with freely independent circular entries, we obtain the free deterministic equivalent of our channel model. To evaluate the capacity, we use the Shannon transform of the free deterministic equivalent to approximate that of the channel model. The free deterministic equivalent results are easy to compute, and simulation results show that these approximations are numerically accurate and computationally efficient.

We study a multiuser multicarrier downlink communication system in which the base station (BS) employs a large number of antennas. By assuming frequency-division duplex operation, we provide a beam domain channel model as the number of BS antennas grows asymptotically large. With this model, we first derive a closed-form upper bound on the achievable ergodic sum-rate before developing necessary conditions to asymptotically maximize the upper bound, with only statistical channel state information at the BS. Inspired by these conditions, we propose a beam division multiple access (BDMA) transmission scheme, where the BS communicates with users via different beams. For BDMA transmission, we design user scheduling to select users within non-overlapping beams, work out an optimal pilot design under a minimum mean square error criterion, and provide optimal pilot sequences by utilizing the Zadoff-Chu sequences. The proposed BDMA scheme reduces significantly the pilot overhead, as well as, the processing complexity at transceivers. Simulations demonstrate the high spectral efficiency of BDMA transmission and the advantages in the bit error rate performance of the proposed pilot sequences.

In this paper, we consider the uplink of a dense small cell network (SCN) using pilot reuse in channel training and maximum ratio combining (MRC) for data detection. Taking into account imperfect channel state information (CSI) caused by pilot contamination, we derive exact closed-form expressions of the per-user achievable ergodic rate with arbitrary pilot reuse factors. After that, we first reveal that the user terminals, which are geographically separated with large distance between each other, can reuse pilot, suffering only low pilot contamination. Based on this insight, we further propose a low-complexity pilot reuse algorithm based on the minimum sum of estimation error criterion. Simulation results verify our theoretical analysis and demonstrate that the proposed pilot reuse algorithm is very effective in suppressing pilot contamination in SCN.

A multiuser scheduling multiple-input multiple-output (MIMO) cognitive radio network (CRN) with space-time block coding (STBC) is considered in this paper, where one secondary base station (BS) communicates with one secondary user (SU) selected from K candidates. The joint impact of imperfect channel state information (CSI) in BS → SUs and BS → PU due to channel estimation errors and feedback delay on the outage performance is firstly investigated. We obtain the exact outage probability expressions for the considered network under the peak interference power IPat PU and maximum transmit power Pmat BS which cover perfect/imperfect CSI scenarios in BS → SUs and BS → PU. In addition, asymptotic expressions of outage probability in high SNR region are also derived from which we obtain several important insights into the system design. For example, only with perfect CSIs in BS → SUs, i.e., without channel estimation errors and feedback delay, the multiuser diversity can be exploited. Finally, simulation results confirm the correctness of our analysis.

A multi-pair two-way amplify-and-forward relaying system is considered in this paper, where the K-pair users exchange information within pairs, with the help of a shared relay station. Each user is equipped with a single antenna while the relay station is equipped with a very large number of antennas N, where N≫2K. The impact of imperfect channel state information (CSI) due to the channel estimation errors on spectral efficiency (SE) and energy efficiency (EE) is investigated. Asymptotic expressions for EE and SE are obtained in this paper for three power-scaling schemes when maximum-ratio combining/maximum-ratio transmission (MRC/MRT) is adopted. Our analytical results reveal that when the number of relay station antennas N→∞, the effect of the small-scale fading can be averaged out; the residual self-interference and additional noise generated by channel estimation errors will completely vanish, and the inter-pair interference will disappear. However, the imperfect CSI deteriorates EE and SE. When there are no estimation errors, our analytical results reduce to the existing results. Simulation results confirm the validity of our analysis.

Energy efficiency (EE) of wireless communications has attracted growing attention in recent years. In this paper, we focus on the EE optimization in cognitive radio networks (CRN) from a perspective of designing MAC frame structure, namely scheduling the sensing-then-transmission time slots. Considering secondary users adopting channel handoff to avoid collisions with primary users, the EE of CRN is modeled, which is a function of the sensing time and the transmission time. Under the constraint of protecting primary users sufficiently, an EE optimization problem is formulated, and the energy- efficient MAC frame is thus obtained by solving the problem. Simulation results confirm theoretical analysis, i.e., the EE of CRN depends on the sensing capability and interference constraint. Moreover, the proposed optimization method for cognitive MAC frame can enhance the EE of CRN effectively.

This study addresses the pilot scheduling problem in multiuser multi‐cell massive multiple‐input–multiple‐output (MIMO) systems, aiming at mitigating the inter‐cell interference (pilot contamination), which constitutes a bottleneck of the system performance. First, the authors investigate the pilot reuse and scheduling problem in cellular systems with unlimited number of base station antennas, only considering the large fading coefficients. Three low‐complexity pilot scheduling schemes are then proposed by maximising the system achievable sum rate, including the greedy algorithm, the tabu search (TS) algorithm and the greedy TS algorithm. Second, they investigate the pilot reuse problem among the inter‐cell user terminals (UTs) under spatially correlated channels for massive MIMO transmission, trying to distinguish UTs sharing the same time‐frequency resources from the spatial domain. A closed‐form expression of the non‐asymptotic downlink achievable rate is derived by exploiting the second‐order channel statistical information. On the basis of the degree of the UTs’ covariance matrices overlap with each other, they further propose a spatial orthogonality‐based greedy pilot scheduling algorithm. The proposed approaches can provide much better performance in the presence of pilot contamination. Theoretical analysis and numerical results both verify the effectiveness of the proposed algorithms.

This study investigates the scheduling problem for a single‐cell downlink distributed antenna systems (DASs) with a massive number of remote access units (RAUs). To reduce signalling overhead under limited backhaul capacity, the authors make use of local long‐term channel state information (CSI) in coordinated scheduling design. They first derive the ergodic rate expressions for both the single RAU transmission and the cooperative RAU transmission modes as functions of long‐term CSI. Then greedy scheduling algorithms (GSAs) aiming for the maximum ergodic sum rate for the massive DAS using long‐term CSI are proposed. To mitigate the intra‐cell interference, a two‐stage GSA with hybrid transmission mode is devised. Asymptotic analysis reveals that as the number of RAUs goes to infinity, intra‐cell interference can be effectively mitigated. Simulation results verify the analysis and demonstrate that the two‐stage GSA exhibits a higher ergodic sum‐rate.

In this paper, we consider a free-space optical (FSO) communication scheme with the help of a variable gain relay where the links from the source to the relay are radio-frequency (RF) links while the links between the relay and the destination are FSO links with M-distribution. To mitigate the effects of multipath fading and atmospheric turbulence, we apply transmit diversity at the source and selection combining (SC) at the destination, while the relay is only equipped with single RF receive antenna for receiving signal and one aperture for relaying the information. With this setup, we first derive expressions for the outage probability, bit-error rate, and the average capacity. We further investigate the effect of pointing errors on the system performance.

We study multicarrier multiuser multiple-input multiple-output (MU-MIMO) systems, in which the base station employs an asymptotically large number of antennas. We analyze a fully correlated channel matrix and provide a beam domain channel model, where the channel gains are independent of sub-carriers. For this model, we first derive a closed-form upper bound on the achievable ergodic sum-rate, based on which, we develop asymptotically necessary and sufficient conditions for optimal downlink transmission that require only statistical channel state information at the transmitter. Furthermore, we propose a beam division multiple access (BDMA) transmission scheme that simultaneously serves multiple users via different beams. By selecting users within non-overlapping beams, the MU-MIMO channels can be equivalently decomposed into multiple single-user MIMO channels; this scheme significantly reduces the overhead of channel estimation, as well as, the processing complexity at transceivers. For BDMA transmission, we work out an optimal pilot design criterion to minimize the mean square error (MSE) and provide optimal pilot sequences by utilizing the Zadoff-Chu sequences. Simulations demonstrate the near-optimal performance of BDMA transmission and the advantages of the proposed pilot sequences.

We investigate the sum-rate-optimal precoding for the uplink of the multi-cell large-scale MIMO systems. Specifically, we focus on transmitter precoder design based only on the statistical channel state information (CSI). We first consider the partial cooperation system, where only the CSI is shared among cells, and its base station (BS) decodes its in-cell users by treating out-cell signals as colored Gaussian noise. We derive the ergodic sum rate and its deterministic approximation for the regime where both the number of total user antennas and the number of BS antennas are large. We obtain the necessary conditions to maximize the deterministic approximation of the sum rate under the transmit power constraint, based on which a gradient search algorithm is proposed to find the local optimal precoders. Furthermore, a super cell system is also considered, where users from all cells are jointly decoded. The deterministic approximation to the sum rate and optimal precoder design for such systems are given. Numerical experiments show that the proposed precoders achieve significant performance gains over systems with no precoding. Compared with the existing precoding methods that require perfect CSI, the proposed precoders achieve similar sum rates and require only the statistical CSI.

This paper considers a multipair massive multiple-input-multiple-output two-way relay network, in which multiple pairs of users are served by a relay station with a large number of antennas, which uses maximum ratio combining/maximum ratio transmission and a fixed amplification factor for reception/ transmission. First, the users' ergodic rates are derived for the case with a finite number of antennas, and then, the rate gain is analyzed when the transmit power of the senders and the relay is sufficiently large. We show that the ergodic rates increase with the number of antennas at the relay, i.e., N, but decrease with the number of user pairs, i.e., K, both logarithmically. The energy efficiency for the network is also investigated when the number of antennas grows to infinity. It is further revealed that the ergodic sum-rate can be maintained while the users' transmit power is scaled down by a factor of 1/N or the relay power by a factor of 2K/N. This indicates that users obtain an energy efficiency gain of N, but the relay has an energy efficiency gain of N divided by the number of users, i.e., 2K.

In this paper, we investigate the design of linear precoders for the multiple-input-multiple-output (MIMO) multiple access channel (MAC). We assume that statistical channel state information (CSI) is available at the transmitters and consider the problem under the practical finite alphabet input assumption. First, we derive an asymptotic (in the large system limit) expression for the weighted sum rate (WSR) of the MIMO MAC with finite alphabet inputs and Weichselberger's MIMO channel model. Subsequently, we obtain the optimal structures of the linear precoders of the users maximizing the asymptotic WSR and an iterative algorithm for determining the precoders. We show that the complexity of the proposed precoder design is significantly lower than that of MIMO MAC precoders designed for finite alphabet inputs and instantaneous CSI. Simulation results for finite alphabet signaling indicate that the proposed precoder achieves significant performance gains over existing precoder designs.

We propose pilot reuse (PR) in single cell for massive multiuser multiple-input multiple-output (MIMO) transmission to reduce the pilot overhead. For spatially correlated Rayleigh fading channels, we establish a relationship between channel spatial correlations and channel power angle spectrum when the base station antenna number tends to infinity. With this channel model, we show that sum mean square error (MSE) of channel estimation can be minimized provided that channel angle of arrival intervals of the user terminals reusing the pilots are non-overlapping, which shows feasibility of PR over spatially correlated massive MIMO channels with constrained channel angular spreads. Since channel estimation performance might degrade due to PR, we also develop the closed-form robust multiuser uplink receiver and downlink precoder that minimize sum MSE of signal detection, and reveal a duality between them. Subsequently, we investigate pilot scheduling, which determines the PR pattern, under two minimum MSE related criteria, and propose a low complexity pilot scheduling algorithm, which relies on the channel statistics only. Simulation results show that the proposed PR scheme provides significant performance gains over the conventional orthogonal training scheme in terms of net spectral efficiency.

In this paper, we consider a cognitive radio (CR) network in which a secondary multiantenna base station (BS) attempts to communicate with multiple secondary users (SUs) using the radio frequency spectrum that is originally allocated to multiple primary users (PUs). Here, we employ partially-projected regularized zero-forcing (PP-RZF) precoding to control the amount of interference at the PUs and to minimize inter-SUs interference. The PP-RZF precoding partially projects the channels of the SUs into the null space of the channels from the secondary BS to the PUs. The regularization parameter and the projection control parameter are used to balance the transmissions to the PUs and the SUs. However, the search for the optimal parameters, which can maximize the ergodic sum-rate of the CR network, is a demanding process because it involves Monte-Carlo averaging. Then, we derive a deterministic expression for the ergodic sum-rate achieved by the PP-RZF precoding using recent advancements in large dimensional random matrix theory. The deterministic equivalent enables us to efficiently determine the two critical parameters in the PP-RZF precoding because no Monte-Carlo averaging is required. Several insights are also obtained through the analysis.

The common signals in public channels of cellular networks should be transmitted omni-directionally from the base station to guarantee reliable coverage. In this paper, we propose a transmission scheme for public channels in massive MIMO systems based on the precoding-extended Alamouti code. This proposed space-time code has the following four features: 1) the transmitted signal is omni-directional, i.e., its DFT has constant-envelope; 2) the transmitted signal on each antenna has constant-envelope to achieve the highest power-efficiency; 3) the maximum diversity order 2 can be obtained; 4) it is easy for downlink channel estimation at the user side. Furthermore, we propose two different constellation designs for the two symbols in this space-time code, i.e., these two symbols can be modulated independently or jointly. The independent design has the advantage in fast decoding while the joint design has the advantage in superior performance. Simulations verify the effectiveness of our scheme.

A multi-pair two-way relay system sharing with a single relay with large numbers of antennas is considered. In this paper, we investigate the ergodic achievable rates when maximum ratio combining/maximum ratio transmission is used at the relay station. The analytical results for the achievable rates are derived and the asymptotic analysis is conducted when the number of antennas, N, grows to infinity. Results demonstrate a number of phenomena including the logarithmic increase of the average rates with the number of antennas, and the logarithmic decrease of the average rates with the number of interference pairs, K-1, when the transmit power of both relay and users are fixed.

In this paper, we investigate the design of linear precoders for multiple-input multiple-output (MIMO) multiple access channels (MAC). We assume that statistical channel state information (CSI) is available at the transmitters and consider the problem under the practical finite alphabet input assumption. First, we derive an asymptotic (in the large-system limit) weighted sum rate (WSR) expression for the MIMO MAC with finite alphabet inputs and general jointly-correlated fading. Subsequently, we obtain necessary conditions for linear precoders maximizing the asymptotic WSR and propose an iterative algorithm for determining the precoders of all users. In the proposed algorithm, the search space of each user for designing the precoding matrices is its own modulation set. This significantly reduces the dimension of the search space for finding the precoding matrices of all users compared to the conventional precoding design for the MIMO MAC with finite alphabet inputs, where the search space is the combination of the modulation sets of all users. As a result, the proposed algorithm decreases the computational complexity for MIMO MAC precoding design with finite alphabet inputs by several orders of magnitude. Simulation results for finite alphabet signalling indicate that the proposed iterative algorithm achieves significant performance gains over existing precoder designs, including the precoder design based on the Gaussian input assumption, in terms of both the sum rate and the coded bit error rate.

We propose pilot reuse (PR) in single cell for massive multiuser multiple-input multiple-output (MIMO) transmission to reduce the pilot overhead. For the spatially correlated Rayleigh fading channels, we establish a relationship between the channel spatial correlations and the channel power angle spectrum when the base station antenna number tends to infinity. With this channel model, we first show that the sum mean square error of the channel estimation (MSE-CE) can be minimized if the channel angle of arrival intervals of the user terminals reusing the pilots are non-overlapping, which shows the feasibility of PR in massive MIMO channels with constrained channel angular spreads. Then we design the pilot scheduler under the minimum MSE-CE criterion. With the channel estimation error due to PR taken into account, we also develop the closed-form robust uplink receiver and downlink precoder that minimize the sum MSE of the signal detection. Simulation results show that the proposed PR scheme provides significant performance gains over the conventional orthogonal training scheme in terms of the net spectral efficiency.

In this paper, we propose a coordinated pilot reuse (CPR) scheme for multi-cell massive multi-input multi-output systems to reduce the pilot overhead. Unlike the conventional pilot reuse scheme which allows pilots to be reused only among different cells, CPR allows inter- and/or intra-cell user equipments to reuse the same pilots, efficiently reducing the pilot overhead. For the spatially correlated Rayleigh fading channels, we first present the CPR-based channel estimation and a greedy pilot allocation algorithm. With the channel estimation errors due to CPR taken into account, we then develop a statistically robust transceiver which can guarantee the transmission performance. Monte-Carlo simulations show that compared to the conventional multi-cell pilot reuse scheme, the proposed CPR scheme provides significant performance gains in terms of the net spectral efficiency.

The large-scale multiple-input multiple-output (MIMO) system achieves high spectral efficiency, and its performance is ultimately limited by pilot contamination. Since the channel energy usually concentrates in a significant angle region, some mean-square-error (MSE)-based schedule methods have been proposed to mitigate the contamination, but these methods cannot guarantee the quality of service (QoS) for each served terminal. In order to overcome this deficiency, we derive an asymptotic achievable rate for each served terminal under the spatially correlated channel. Further, we analyze how the achievable rate is affected by the degree of channel overlap in the angle region. Based on the analysis result, we design a low-complexity schedule algorithm to guarantee the QoS and find the optimal number of served terminals. Numerical results show that the proposed schedule algorithm outperforms the MSE-based one in terms of QoS and fairness, and can maximize the number of served terminals.

In this paper, the turbo minimum mean square error- parallel interference cancelation (Turbo MMSE-PIC) receiver is incorporated with link adaptation schemes to achieve promising performance gains for uplink MIMO transmission, in parallel, adapt the link in the actual channel conditions with assist from performance prediction technology. We first predict the turbo receiver's performance as a basic metric for link adaptation scheme. Then, a new strategy of selecting the precoding matrices, the number of spatially multiplexed layers as well as modulation coding schemes are proposed, resulting in the proposed three-steps selection. In this method, the number of layers and modulation coding schemes to be feedback is initialized and used to set a shortened range of them. Then the number of layers is updated to take full advantage of iterative processing, instead of the initial value in the conventional strategy. In so doing, the benefit of turbo receivers is efficiently claimed while low computational complexity can be achieved.

In this work, we consider a relay-assisted free-space optical (FSO) communication scheme in which the relay services multiple users and only the best user is selected so that the channel fluctuations can be effectively exploited to produce a selection diversity gain. We assume that the link from the source to the relay is a radio-frequency (RF) link while the link between the relay and the destination is an FSO link. More specifically, we first present a statistical analysis for the systems under consideration over both weak and strong atmospheric turbulence channels. Based on these results, the capacity of these systems with and without adaptive transmission is analyzed.

In this study, the authors focus on the tradeoff between spectrum sensing and energy efficiency of cognitive radio networks (CRN). Considering two interference‐avoidance schemes, that is, channel handoff and stop‐and‐wait, the authors, respectively, model the energy efficiency (EE) of CRN as the mean of the throughput‐to‐power ratio. Research shows that stop‐and‐wait scheme is a special case of channel handoff from an EE perspective. Based on the proposed EE model, the authors build up an EE optimisation problem under the constraint of the sensing quality, and formulate the sensing‐energy efficiency tradeoff (SET) for CRN. The similarity and difference between the SET and the sensing‐throughput tradeoff are also investigated. Simulation analysis confirms that there is indeed an optimal sensing time to make the EE maximum, and it is larger than that for maximum throughput. Another interesting result is that the EE and the throughput of CRN can be enhanced together by optimising MAC frame structure in combination with sensing bandwidth adjustment and power control. Our work provides some insights for green CRN in view of MAC frame optimisation.

This paper investigates the scheduling problem for a single-cell distributed multiple-input multiple-output (d-MIMO) downlink system with a massive number of remote access units (RAUs), N. We first derive the ergodic rate expressions for both the single RAU transmission (SRT) and the cooperative RAU transmission (CRT) modes as functions of long-term channel state information (CSI). Then, greedy scheduling algorithms aiming for maximizing the ergodic sum rate for the massive d-MIMO system using local long-term CSI are proposed. To mitigate intra-cell interference, a two-stage greedy scheduling algorithm (GSA) is developed to further improve the ergodic sum rate. Asymptotic analysis reveals that with infinite N intra-cell interference can be efficiently mitigated. Simulation results verify the derived expressions and demonstrate that the two-stage GSA exhibits a higher ergodic sum rate.

This paper investigates the linear precoder design for multiple-input multiple-output (MIMO) K-user interference channels with finite alphabet inputs. We first obtain the general explicit expressions of the achievable rate of each user in MIMO interference channel systems. We study optimal transmission strategies in both high signal-to-noise ratio (SNR) and low SNR regions. We show that given finite alphabet inputs, a simple power allocation design can achieve optimal performance. In contrast, the well-known interference alignment technique for Gaussian input scenarios, only utilizes a partial interference-free signal space for transmission and leads to a constant performance loss when it is applied to finite-alphabet input scenarios. We determine this constant rate loss at high SNR. Moreover, we establish necessary conditions for the linear precoder design of the weighted sum-rate maximization. We also develop an efficient iterative algorithm for determining precoding matrices of all the users. Our numerical results show that for the practical digital modulated signals from discrete constellations, the proposed iterative algorithm achieves considerably higher sum-rate than the existing methods.

In this paper, the uplink sum-rate of the multi-cell multi-user massive MIMO system is studied under correlated Rayleigh fading channels. First, by considering pilot contamination effect, the equivalent system model of the massive MIMO is given with MMSE channel estimation. Then, the lower bound of the sum-rate is derived, and its asymptotical performance is studied when the base station antenna number goes to infinity. The result is general and is very accurate under the physical channel models.

This paper proposes a new codebook structure called Kronecker-product based codebook (KPC), where each codeword is the Kronecker product of two oversampled DFT codewords in both the horizontal and vertical domains. The KPC is especially suitable for the three-dimensional (3D) multiuser multi-input multi-output (MU-MIMO) systems. Besides, channel state information feedback based on the best companion cluster scheme is investigated. Since all codewords have been grouped into several clusters, each user feeds back its best precoding matrix index, best interference cluster index and channel quality information, then the BS pairs and schedules users according to the received feedback. Different codewords clustering methods affect the performance of the limited feedback schemes. We proposes two kinds of codewords clustering methods based on 3D beam patterns, including both the symmetric and asymmetric one. Simulation shows that with properly clustered codewords, our proposed 3D MU-MIMO feedback scheme has a significant throughput gain against 2D MU-MIMO feedback scheme.

This paper proposes an adaptive mode switching scheme for downlink MIMO in heterogeneous networks (HetNet) where low-power remote radio head (RRH) nodes are deployed within the coverage area of an existing macro network, aiming at enhancing the system sum capacity. Under the assumption that only statistical channel state information (CSI) is available at the transmitter, we derive the capacity approximations for both cooperative and non- cooperative transmission modes from the exact capacity expressions with instantaneous CSI. According to the results of the capacity analysis, simulations are presented for the proposed adaptive scheme in comparison with the exact mode switching based on Monte Carlo method using instantaneous CSI, which demonstrate its efficiency and performance gains.

As a large percentage of the energy required by a cellular network is consumed at base station (BS) sites, how to reduce the energy consumption at BS sites has recently received much attention. In this paper, we propose an energy efficient link adaptation scheme to improve the BS's energy efficiency (EE) for long term evalution (LTE) systems. Based on the traditional spectral- efficiency centered link adaptation scheme, the proposed scheme treats transmit power as a new feedback parameter, aiming to maximize the BS's EE under a certain block error rate constraint. In addition, in order to reduce the transmit power adjustment frequency, a semi-static power control scheme is presented. Simulation results indicate that our proposed schemes can improve the BS's EE significantly.

Multiple base station (BS) cooperation and denser BS deployment have been recognized as two promising ways to improve the system capacity of the next generation wireless communication systems. In practice, to avoid the formidable overhead of the whole network cooperation, BSs can form disjoint or overlapped groups (i.e., clusters) and adopt limited cooperation. In this paper, we investigate the performance of BS clustering in homogeneous small cell networks (SCNs). Based on the clustering methods for macrocell networks, a dynamic clustering algorithm is proposed to adapt to the SCN where the number of BSs is much larger than the scheduled users'. Simulations show that the dynamic clustering with proper beamforming method leads to significant sum-rate gains over the non-cooperative one and the proposed algorithm outperforms the conventional channel strength based ones in our network structure. Performance of dynamic clustering in a SCN where each BS schedules a single user is also evaluated.

In this paper, we investigate the optimal pilot for multiple-input multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) systems. The optimal pilot is obtained by minimizing the mean-square-error (MSE) of the linear minimum MSE (LMMSE) channel estimation, which is related to the channel correlation matrices in space, time, and frequency domains. We obtain that the optimal pilot is the statistical precoded sequence in the transmit eigenmode with proper power allocation. In general, the power allocation has no closed-form solution, so we present some suboptimal schemes to approximate the optimal one. Simulations show that the proposed statistical precoded pilot outperforms the conventional one, and the suboptimal schemes can well approximate the optimal one.

In this paper, a deterministic equivalent of ergodic sum rate and an algorithm for evaluating the capacity-achieving input covariance matrices for the uplink large-scale multiple-input multiple-output (MIMO) antenna channels are proposed. We consider a large-scale MIMO system consisting of multiple users and one base station with several distributed antenna sets. Each link between a user and an antenna set forms a two-sided spatially correlated MIMO channel with line-of-sight (LOS) components. Our derivations are based on novel techniques from large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the terminals approach to infinity with a fixed ratio. The deterministic equivalent results (the deterministic equivalent of ergodic sum rate and the capacity-achieving input covariance matrices) are easy to compute and shown to be accurate for realistic system dimensions. In addition, they are shown to be invariant to several types of fading distribution.

In this paper, we investigate the outage performance for a decode-and-forward two-way relay network in the presence of multiple strong interferers at the source/destination terminals. We first derive closed-form expressions for the outage probability of the system under asymmetrical and symmetrical cases, of whether the received powers at the relay from both source terminals are the same or different. Based on the analytical expressions, we perform asymptotic analysis in the case where the power of relay or/and the power of terminals become infinite. Our analysis shows that the outage probability is lower when there is one dominating interferer than when there are several equal-power interferers. In addition, optimal power allocation between the two source terminals and the diversity order of the system are investigated in asymptotic analysis. Simulation results demonstrate that our analytical results are in excellent agreement with the Monte Carlo simulations.

We propose a multiuser downlink transmission scheme exploiting both statistical and outdated channel state information (CSI) at the transmitter. Based on the outdated CSI-aided transmission scheme introduced in (denoted as MAT), the proposed scheme reduces the original K-user MAT system to a two-user virtual MAT system, through statistical precoding in the first two transmission slots. Thus, the proposed scheme (denoted as V-MAT) reduces efficiently the implementation complexity, while increasing the achievable rate at finite signal-to-noise ratios (SNRs). For the V-MAT scheme, we derive an analytical high SNR rate approximation for correlated Rayleigh fading. Furthermore, for independent and identically distributed Rayleigh fading, we derive an exact rate expression at high SNRs, as well as a tight lower bound which applies for arbitrary SNRs. Then, precoder design is investigated, where an efficient near-optimal solution is proposed for arbitrary number of transmit antennas, and a closed-form optimal solution is derived for the two-antenna case. It is demonstrated that the proposed V-MAT scheme yields higher achievable rate than the original MAT scheme at practical SNRs. Moreover, by combining the V-MAT scheme and the generalized MAT scheme of , where precoding is implemented in the third transmission slot, the achievable rate can be further increased.

This paper investigates the linear precoder design for K-user interference channels of multiple-input multiple-output (MIMO) transceivers under finite alphabet inputs. We first obtain general explicit expressions of the achievable rate for users in the MIMO interference channel systems. We study optimal transmission strategies in both low and high signal-to-noise ratio (SNR) regions. Given finite alphabet inputs, we show that a simple power allocation design achieves optimal performance at high SNR whereas the well-known interference alignment technique for Gaussian inputs only utilizes a partial interference-free signal space for transmission and leads to a constant rate loss when applied naively to finite-alphabet inputs. Moreover, we establish necessary conditions for the linear precoder design to achieve weighted sum-rate maximization. We also present an efficient iterative algorithm for determining precoding matrices of all the users. Our numerical results demonstrate that the proposed iterative algorithm achieves considerably higher sum-rate under practical QAM inputs than other known methods.

The discrete Fourier transmission spread OFDM (DFTS-OFDM) based single-carrier frequency division multiple access (SC-FDMA) has been widely adopted due to its lower peak-to-average power ratio (PAPR) of transmit signals compared with OFDM. However, the offset modulation, which has lower PAPR than general modulation, cannot be directly applied into the existing SC-FDMA. When pulse-shaping filters are employed to further reduce the envelope fluctuation of transmit signals of SC-FDMA, the spectral efficiency degrades as well. In order to overcome such limitations of conventional SC-FDMA, this paper for the first time investigated cyclic prefixed OQAM-OFDM (CP-OQAM-OFDM) based SC-FDMA transmission with adjustable user bandwidth and space-time coding. Firstly, we propose CP-OQAM-OFDM transmission with unequally-spaced subbands. We then apply it to SC-FDMA transmission and propose a SC-FDMA scheme with the following features: a) the transmit signal of each user is offset modulated single-carrier with frequency-domain pulse-shaping; b) the bandwidth of each user is adjustable; c) the spectral efficiency does not decrease with increasing roll-off factors. To combat both inter-symbol-interference and multiple access interference in frequency-selective fading channels, a joint linear minimum mean square error frequency domain equalization using {a prior} information with low complexity is developed. Subsequently, we construct space-time codes for the proposed SC-FDMA. Simulation results confirm the powerfulness of the proposed CP-OQAM-OFDM scheme (i.e., effective yet with low complexity).

In this paper, we analyze the ergodic sum-rate of a multi-cell downlink system with base station (BS) cooperation using regularized zero-forcing (RZF) precoding. Our model assumes that the channels between BSs and users have independent spatial correlations and imperfect channel state information at the transmitter (CSIT) is available. Our derivations are based on large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the BS and users approach to infinity with some fixed ratios. In particular, a deterministic equivalent expression of the ergodic sum-rate is obtained and is instrumental in getting insight about the joint operations of BSs, which leads to an efficient method to find the asymptotic-optimal regularization parameter for the RZF. In another application, we use the deterministic channel rate to study the optimal feedback bit allocation among the BSs for maximizing the ergodic sum-rate, subject to a total number of feedback bits constraint. By inspecting the properties of the allocation, we further propose a scheme to greatly reduce the search space for optimization. Simulation results demonstrate that the ergodic sum-rates achievable by a subspace search provides comparable results to those by an exhaustive search under various typical settings.

In this paper, the outage performance for a decode-and-forward two-way relay network is investigated in the presence of multiple interferers at the source terminals. The exact expression for the outage probability is derived and the disparity is discussed between symmetrical and asymmetrical cases. Based on the closed-form expressions the optimal power allocation between source terminals is discussed and the diversity order is derived. The effect of interference power is also studied. Simulation results are provided to validate the analytical results.

In this paper, we investigate single-carrier frequency division multiple access (SC-FDMA) transmission with offset quadrature amplitude modulations (OQAM). Firstly, we propose cyclic prefixed OQAM orthogonal frequency division multiplexing (CP-OQAM-OFDM) transmission with unequally-spaced subbands. We then apply it to FDMA transmission and propose a SC-FDMA scheme with the following features: a) the transmit signal of each user is offset modulated single-carrier with frequency-domain pulse-shaping; b) the bandwidth of each user is adjustable; c) the spectral efficiency does not decrease with increasing the roll-off factors. To combat both inter-symbol-interference and multiple access interference in frequency-selective fading channels, a joint linear minimum mean square error frequency domain equalization using a prior information with low complexity is developed. Simulation results confirm the effectiveness of the proposed SC-FDMA transmission.

This paper proposes a statistical-eigenmode spacedivision multiple-access (SE-SDMA) transmission for a two-user downlink system where two transmit antennas are equipped at the base station and each mobile user has one receive antenna, assuming that only statistical channel state information (CSI) is available at the transmitter. By maximizing a lower bound of the ergodic signal-to-leakage-and-noise ratio, the proposed SE-SDMA approach selects two users with orthogonal principal statistical eigen-directions and transmits to each user along the corresponding eigenmode. We derive an exact expression of the ergodic achievable rate, and compare it with the zero-forcing beamforming (ZFBF) system exploiting instantaneous CSI. It is shown that SE-SDMA can achieve the maximum ergodic sum-rate of the two selected users, and provide significant user selection gain. Analytical and simulation results show that the rate gap between SE-SDMA and ZFBF with perfect CSI at the transmitter can tend to zero in highly correlated channels, which indicates that statistical precoding can be used instead of instantaneous precoding in certain environments.

We investigate the design of linear transmit precoding for multiple-input multiple-output (MIMO) broadcast channels (BC) with finite alphabet input signals. We derive an explicit expression for the achievable rate region of the MIMO BC with discrete constellation inputs, which is generally applicable to cases involving arbitrary user number and arbitrary antenna configurations. For the case where all the users employ the same modulation scheme, we further present a weighted sum rate upper-bound of the MIMO BC with identical transmit precoding matrices. The resulting bound demonstrates a serious performance loss due to multi-user interference for MIMO BC with finite alphabet inputs in high signal-to-noise ratio (SNR) region, which motivates the use of simple precoding to combat this multiuser interference. Based on a constrained optimization problem formulation, we apply the Karush-Kuhn-Tucker analysis to derive necessary conditions for MIMO BC precoders to maximize the weighted sum-rate. We then propose an iterative gradient descent algorithm with backtracking line search to optimize the linear precoders for each user. Numerical results illustrate that our proposed algorithm provides significant gains over other conventional precoding schemes including the traditional iterative water-filling (WF) design for the Gaussian input assumption.

In this paper, we investigate the secrecy rate of finite alphabet communications over multiple-input, multiple-output, multiple-antenna eavesdropper (MIMOME) systems. Traditional precoder designs at the transmitter for achieving secrecy capacity (maximum secrecy rate) for MIMOME systems are developed according to the assumption of Gaussian input signals. Such designs may risk substantial secrecy rate loss when Gaussian inputs are replaced by practical finite alphabet inputs. To address this issue, we propose a linear precoding design to directly maximize the secrecy rate for MIMOME systems under the constraint of finite alphabet input. Exploiting convex optimization and matrix calculus, we present necessary conditions required of the optimal precoding design and develop an iterative algorithm for finding an efficient precoder. With finite alphabet input signals, maximum transmission power no longer corresponds to maximum secrecy rate as in the case of Gaussian input. We further derive closed-form results on the optimal transmission design for maximizing secrecy rate in low signal-to-noise ratio (SNR) region and near-optimal transmission power in a high SNR region.

In this paper, we consider the multi-cell downlink system with multiple base stations (BSs) and multiple single antenna users employing the BS cooperation. The channels between BSs and users have independent spatial correlations. All BSs jointly implement the regularized zero-forcing based on imperfect channel estimation. By utilizing the large dimensional random matrix theory, we first obtain the limiting distribution of the eigenvalues for a class of random Hermitian matrix. Based on this result, we derive a deterministic equivalent of ergodic sum rate for the multi-cell downlink system and obtain the optimal regularization parameter in the special case employing maximize the deterministic equivalent of ergodic sum rate. Numerical results show that the deterministic equivalent are accurate even for finite number of antenna and the channel estimation parameter almost do not affect the accuracy of the approximation.

In this paper, we focus on a general coordinated multi-point (CoMP) multiple input multiple-output (MIMO) system consisting of multiple users and multiple base stations (BSs) equipped with multiple antennas, respectively. An asymptotic ergodic mutual information expression and the capacity-achieving input covariance matrices for the system are derived employing novel techniques from large dimensional random matrix theory (RMT). The asymptotic regime is based on the assumption that the numbers of antennas at the transmitter and receiver approach to infinity with a fixed ratio. Our contributions are to extend the previous results to the general channel model with two-sided spatial correlation and line-of-sight (LOS), in which the transmit and receive correlation matrices are both generally nonnegative definite and the channel entries are non-Gaussian distributed. Simulations show that the asymptotic capacity is accurate even for finite number of antenna and invariant to all types of fading distribution.

In order to achieve a better tradeoff between overhead and performance, this paper proposes a separately selected dual-codebook (SSDC) based best companion cluster (BCC) approach. In this scheme, each UE selects its best and worst longterm precoding matrix indicators (PMI1), and feeds them back as the cluster indicators (CIs) for UE pairing. The final precoder W is determined by two precoders indicated by the best PMI1 and the short-term feedback PMI2. Besides, channel quality indicators (CQIs) are calculated for UE scheduling. To compare with SSDC-based BCC, we also discuss and propose a jointly selected dual-codebook (JSDC) based BCC scheme. Simulation results show that SSDC-based BCC has a comparable throughput performance with JSDC-based BCC but with a substantially reduced complexity and feedback overhead for 8Tx.

In this paper, we study adaptive transmission mode switching between statistical and instantaneous channel state information (CSI) aided single-user (SU) and multiuser (MU) precoding for a two-user downlink system, where two transmit antennas are equipped at the base station and each mobile user has one receive antenna. In the case where only statistical CSI (SCSI) is available at the transmitter, a statistical-eigenmode space-division multiple-access (SE-SDMA) scheme is proposed by maximizing a lower bound of the ergodic signal-to-leakage-and-noise ratio. An exact analytical expression of the ergodic achievable rate is derived for the proposed SE-SDMA and compared with SU schemes such as SE transmission (SET) and instantaneous CSI (ICSI)-aided beamforming (BF), as well as the MU schemes such as ICSI-aided zero-forcing BF (ZFBF). Assuming the ICSI obtained at the transmitter is imperfect, the operating regions of these schemes are determined for different signal-to-noise ratio regions, channel correlation levels and ICSI inaccuracy levels.

Multi-user multiple antenna systems have attracted much attention because of large spectral efficiency. However, receiver and transmitter channel state information (CSI) is generally required, especially for the downlink channel. In this study, the authors investigate the ergodic sum rate and present low complexity adaptive transmission scheme for multiple-input single-output downlink multi-user systems with statistical CSI at the base station (BS). The authors derive an approximation of the ergodic sum rate, and the constraint of each user's statistical CSI under which the approximation of the ergodic sum rate is maximised. Then, statistical beamforming space division multiple access transmission is derived through maximising the approximation of the ergodic sum rate, and new adaptive transmission schemes are proposed. The proposed algorithms are shown to perform well and require the BS only to know the statistical CSI of each user.

In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail.

This paper investigates communication over multiple-input multiple-output Rayleigh-product channels in the presence of both co-channel interference and thermal noise. We first present exact expressions for the marginal ordered eigenvalue distributions of the channel Gram matrix when multichannel beamforming is employed, from which we obtain exact results on the outage probability on each eigenmode. Our results are applicable to an extensive class of multichannel systems with generic system configurations. For particular scenarios of single-input multiple-output, keyhole, and multiple-input single-output channels, simplified closed-form expressions for the asymptotic distributions of the non-zero eigenvalue of the channel Gram matrix are derived. These results indicate that the diversity order is only determined by the second largest value among the number of transmit antennas, scatterers, and receive antennas. For these scenarios, we also derive concise closed-form expressions for the moments of the output signal-to-interference-plus-noise ratio, from which an explicit relationship to the moments in Rayleigh fading channels is revealed. These results are also used to investigate the impact of the number of channel scatterers on the bandwidth requirements for a given transmission rate and power, at low signal-to-noise ratios.

We investigate the design of linear transmit precoder for multiple-input multiple-output (MIMO) broadcast channels (BC) with finite alphabet input signals. We first derive an explicit expression for the achievable rate region of the MIMO BC with discrete constellation inputs, which is generally applicable to cases involving arbitrary user number and arbitrary antenna number. We further present a weighted sum rate upper bound of the MIMO BC with identical transmit precoding matrices. The resulting bound exhibits a serious performance loss because of the non-uniquely decodable transmit signals for MIMO BC with finite alphabet inputs in high signal-to-noise ratio (SNR) region. This performance loss motivates the use of a simple precoding to combat the non-unique decodability. Based on a constrained optimization problem formulation, we apply the Karush-Kuhn-Tucker analysis to derive necessary conditions for MIMO BC precoders to maximize the weighted sum-rate. We then propose an iterative gradient descent algorithm with backtracking line search to optimize the linear precoders for each user. Our { simulation} results under the practical transmit symbols of discrete constellations demonstrate significant gains by the proposed algorithm over other precoding schemes including the traditional iterative water-filling (WF) design for the Gaussian input signals. For the low-density parity-check coded systems, our precoder provides considerably coded BER improvement through iterative decoding and detection.

In this paper, the outage performance for amplify-and-forward two-way relay channels is studied in the presence of co-channel interference. We derive the exact outage probability by integral-form expression, and approximate the outage probability with closed-form expression. It is shown by numerical results that the approximations fit well with the exact results in all signal-to-interference plus noise ratio regions, and the approximation perform more exactly when the sum power of interference being large much than the power of noise. Also, the outage performance for different interferers' power distributions are compared in simulations and it is shown the distribution of interferers' power has little effect to the outage probability of system.

This paper studies the eigenvalue distributions of multiple-input multiple-output (MIMO) Rayleigh-product channels in the presence of both co-channel interference and thermal noise. We first present exact expressions for the marginal ordered eigenvalue distributions of the channel Gram matrix when multichannel beamforming is employed, from which we obtain exact results of the outage probability on each eigenmode. Our results apply to a wide class of multichannel systems which transmit on the eigenmodes of the MIMO channel, allowing for transmission on any numbers of eigensubchannels, with any numbers of antennas, any numbers of scatterers in the environment, and any numbers of interferers with arbitrary powers. Also, for the particular case of keyhole channels, simplified closed-form expressions for the asymptotic distributions of the non-zero eigenvalue of the channel Gram matrix are derived, which indicate that the diversity order is only determined by the minimum of the number of transmit and receive antennas.

In this paper, we consider channel estimation for amplify-and-forward (AF) relay network with orthogonal frequency division multiplexing (OFDM) modulation. We propose a superimposed training strategy at relay that allows the destination node to obtain the separate channel information of the source-->;relay link and the relay-->;destination link. The proposed training strategy only requires two transmission phases and is thus compatible with the two-phase data transmission scheme, i.e., the training can be embedded into data transmission. Since the optimal minimum mean square error (MMSE) estimator and the maximum a posteriori (MAP) estimator cannot be expressed in close-forms, we propose to obtain the initial channel estimates from the low complexity suboptimal linear estimators, e.g., linear minimum mean-square error (LMMSE) or least square (LS), and then resort to iterative approaches to improve the estimation accuracy.

We investigate the multiple-input multiple-output broadcast channel with channel distribution information available at the transmitter. The so-called fading-paper model is considered with Nttransmit antennas, and each user having Nrreceive antennas. Near-optimal designs are proposed for the inflation factor matrix under general fading conditions, based on maximizing the approximation of linear assignment capacity. It is proved that for Nt≤ Nr, this matrix has a similar structure and achieves a similar interference elimination effect as dirty-paper coding. Also, low complexity iterative algorithms are proposed for Nt>; Nrcase, which yield a good choice for the inflation factor matrix numerically. Based on the obtained inflation factor matrix, we provide efficient approaches to evaluate the linear assignment achievable rate region for some popular statistical channel models.

In this paper, we focus on the low complexity algorithm of turbo receivers over double selective channels for the Lone Term Evolution (LTE) single user uplink. Based on the analysis of the physical meaning of the frequency channel matrix, we propose a low complexity algorithm which is significant for the existent frequency LDL turbo receivers. To further reduce the receiver complexity, based on the analysis of the equivalent noise of the conventional turbo equalizer over fast fading channels, a new low complexity sub-block orthogonal segmental turbo receiver is also presented. Through Monte Carlo simulation, we confirm that with lower complexity the performance of the proposed turbo receiver is better than the existent frequency LDL turbo receivers.

This paper proposes a novel cascaded precoding scheme at the base station to adapt to both long-term and short-term channel variations in higher-order MIMO systems which are supported in 3GPP LTE-Advanced. The proposed precoding scheme includes a long-term precoding followed by a short-term precoding, where the former bases on the channel statistical eigenmodes, adapting to the long-term channel variations and the latter bases on the LTE Rel. 8 codebook, adapting to the short-term channel variations. Simulation results show that the proposed scheme can achieve a high spectral efficiency while guaranteeing a given codeword error ratio target. And when compared with the precoding scheme using the codebook for LTE-Advanced which is finally fixed recently in one of the 3GPP's proposals, the proposed scheme has a comparable performance but a reduced complexity due to the codebook size and long-term feedback interval.

This paper proposes limited feedback spatial division multiple access (SDMA) schemes for transmit correlated channels by using statistical channel state information (SCSI) and instantaneous channel state information (ICSI). Different from conventional codebook-based MU-MIMO scheme, the proposed multi-beam selection with single channel quality indicator (CQI) feedback (MBS-SCF) scheme determines the preferred beam vector by exploiting the SCSI and only feeds back CQI at each timeslot. The performance of the MBS-SCF scheme is nearly the same as the conventional scheme. In order to further improve the sum rate, we propose multi-beam selection with dual CQIs feedback (MBS-DCF) scheme, which determines dual statistical eigen-directions and feeds back dual CQIs at each timeslot. It will increase the opportunity to exploit multiuser diversity and multiplexing gain. Simulation results demonstrate that the MBS-DCF scheme exhibits a higher sum rate than the conventional scheme does.

In this paper, we consider the channel estimation for the classical three-node relay networks that employ the amplify-and-forward (AF) transmission scheme and the orthogonal frequency division multiplexing (OFDM) modulation. We propose a superimposed training strategy that allows the destination node to separately obtain the channel information of the source→relay link and the relay→destination link. Specifically, the relay superimposes its own training signal over the received one before forwarding it to the destination. The proposed training strategy can be implemented within two transmission phases and is thus compatible with the two-phase data transmission scheme, i.e., the training can be embedded into data transmission. We also derive the Cramér-Rao bound for the random channel parameters, from which we compute the optimal training sequence as well as the optimal power allocation. Since the optimal minimum mean square error (MMSE) estimator and the maximum a posteriori (MAP) estimator cannot be expressed in closed-form, we propose to first obtain the initial channel estimates from the low complexity linear estimators, e.g., linear minimum mean-square error (LMMSE) and least square (LS) estimators, and then resort to the iterative method to improve the estimation accuracy. Simulation results are provided to corroborate the proposed studies.

Single-carrier frequency division multiple access (SC-FDMA) has appeared to be a promising technique for high data rate uplink communications. Aimed at SC-FDMA applications, a cyclic prefixed version of the offset quadrature amplitude modulation based OFDM (OQAM-OFDM) is first proposed in this paper. We show that cyclic prefixed OQAM-OFDM (CP-OQAM-OFDM) can be realized within the framework of the standard OFDM system, and perfect recovery condition in the ideal channel is derived. We then apply CP-OQAM-OFDM to SC-FDMA transmission in frequency selective fading channels. Signal model and joint widely linear minimum mean square error (WLMMSE) equalization using a prior information with low complexity are developed. Compared with the existing DFTS-OFDM based SC-FDMA, the proposed SC-FDMA can significantly reduce envelope fluctuation (EF) of the transmitted signal while maintaining the bandwidth efficiency. The inherent structure of CP-OQAM-OFDM enables low-complexity joint equalization in the frequency domain to combat both the multiple access interference and the intersymbol interference. The joint WLMMSE equalization using a prior information guarantees optimal MMSE performance and supports Turbo receiver for improved bit error rate (BER) perform BER) performance. Simulation results confirm the effectiveness of the proposed SC-FDMA in terms of EF (including peak-to-average power ratio, instantaneous-to-average power ratio and cubic metric) and BER performances.

This paper investigates the performance of Rayleigh-product multiple-input multiple-output (MIMO) channels in the presence of both co-channel interference (CCI) and thermal noise. We obtain closed form expressions for the cumulative distribution function and probability density function of the output signal-to interference-plus-noise ratio (SINR) when optimum combining is employed. In contrast to prior results, our expressions apply for arbitrary numbers of interferers with arbitrary powers. Furthermore, the impact of noise is firstly addressed in our expressions. These are made possible based on the recent random matrix theory tools from which the new statistical properties of maximum eigenvalue of the resultant channel matrix can be derived. The new statistical results permit a general analysis for outage probability of the optimum combining system in Rayleigh-product MIMO channels. Simulation results are also provided to validate the analysis and to examine the effect of CCI and thermal noise on performance.

This paper compares several feedback schemes of a limited feedback system in a coordinated cellular networks. In a coordinated system one user can be served by different base stations (BSs) simultaneously. Conventional codebook is intended for single BS, its aim is to maximize the amplitude of the receive signal. When multiple BSs are equipped, phase discrimination among different BSs at the receive side may lead to performance degradation. Brute-force search through the entire BSs is a heavy burden especially when the BS number is large. In order to mitigate the negative effects, we present one solution dedicated to the coordinated system in this paper. When the BS number is small, we put forward a codebook design scheme based on the Lloyd codebook. When the BS number is large, we propose phase criterion instead of amplitude criterion in choosing the beamforming vector. Both schemes work well under their operating region. With our solution we can effectively reduce performance degradation in a coordinated cellular networks while still maintain a low complexity burden. Simulation results verify the effectiveness of our proposed schemes.

A novel 3D cluster-based double-directional MIMO channel model with consideration of polarization is proposed and the modeling process is described in detail. The spatial channel model (SCM) proposed by 3GPP (3rdGeneration Partnership Project) is retrieved and compared with the new model. Based on the new model, capacity and spatial correlation of the co-located dual- and triple-polarized MIMO systems are simulated through Monte-Carlo method and the results are analyzed. Simulation results reveal that triple-polarized antennas can be deployed in MIMO systems to improve the channel capacity remarkably in rich scattering environments. The impact of the azimuth and elevation angle spread (AS) on the channel correlation is also analyzed by simulation at the end of the paper.

In this paper, we consider detection for DFT-spread OFDM based 3GPP long term evolution (LTE) uplink transmission, where multiuser multiple-input multiple-output (MUMIMO) technique is employed. We propose an iterative detection method, in which soft interference cancellation based on sorted QR decomposition of frequency domain channel matrix. We show that the new iterative detection has lower computational complexity than conventional linear minimum mean square error (LMMSE) based iterative detections. Simulation results demonstrate that the proposed low-complexity detector offers significant performance gain over the conventional LMMSE-based iterative detection.

In this paper, we consider optimal power allocation (OPA) for distributed space-time coded two-way relay networks. Each relay transmits a scaled version of the linear combinations of the received symbols and their conjugates, and the scaling factor is based on automatic gain control (AGC) at the relays. We show that solving the OPA across relays to minimize the average conditional PEP of the destination terminals is a generalized linear fractional programming problem, which can be resolved by the Dinkelbach-type procedure. We also prove that at most two relays are active while the others keep silent if the sum power constraint is made across the relays. This motivates us to propose a new low-complexity relaying scheme that uses distributed Alamouti codes on the selected two-best relay nodes. Simulation results show that the distributed space-time codes (DSTC) with the OPA and the proposed scheme have significant performance gains over the DSTC with the equal power allocation.

With mean or covariance channel feedback, the input covariance matrix can be designed to achieve the ergodic capacity of a MIMO fading channel. It is known that the eigenvectors of the optimal input covariance matrix are the same as the eigen-vectors of the channel mean or covariance matrix. However, the optimal power allocation across the eigen-vectors is much less understood. In this paper, two scenarios are investigated: (1) Rician MIMO channels with mean channel feedback, and (2) Rayleigh MIMO channels with covariance channel feedback. We first derive a suboptimal power allocation algorithms in the spatial domain for expected mutual information maximization for two transmit antennas systems, based on an upper bound for the ergodic capacity of a MIMO channel with either channel mean or covariance information at the transmitter. Then, we extend heuristically the results to systems with multiple antennas at both the transmitter and receiver side. The proposed power allocation solution permits a closed-form expression and has a water-filling interpretation. Simulation results reveal that the proposed method performs nearly the same as the optimal solution (which requires highly complex optimization routines over random processes) with inappreciable difference.

In this paper, we propose a new channel estimation prototype for the amplify-and-forward (AF) two-way relay network (TWRN). By allowing the relay to first estimate the channel parameters and then allocate the powers for these parameters, the final data detection at the source terminals could be optimized. Specifically, we consider the classical three-node TWRN where two source terminals exchange their information via a single relay node in between and adopt the maximum likelihood (ML) channel estimation at the relay node. Two different power allocation schemes to the training signals are then proposed to maximize the average effective signal-to-noise ratio (AESNR) of the data detection and minimize the mean-square-error (MSE) of the channel estimation, respectively. The optimal/sub-optimal training designs for both schemes are found as well. Simulation results corroborate the advantages of the proposed technique over the existing ones.

We study the sum capacity of the uplink multiuser multiple-input multiple-output (MIMO) multiple-access channel (MAC) in the low signal-to-noise ratio (SNR) regime under double-scattering and Rician fading scenarios. The receiver is assumed to have perfect channel state information (CSI), while each transmitter knows only its statistical CSI. We first derive analytical expressions for the minimum ¿b/N0and the wideband slope of MIMO systems employing statistical beamforming, based on which we then present new analytical approximations for the sum capacity of the MIMO-MAC at low SNRs, and investigate the effects of transmit, receive and scatterer correlation, as well as Rician K-factor.

In this paper, we address the preamble-based channel estimation problem for orthogonal frequency-division multiplexing (OFDM) systems using multiple amplify-and-forward (AF) relays. We first establish a linear minimum mean-square-error (LMMSE) channel estimator which directly estimates the overall channels from the source to the destination. Then, based on the assumption that the channel correlations between the relays are unknown at the source terminal, a suboptimal training sequence and precoding matrices in closed form are designed to approach the optimal estimation performance at low-complexity.

We investigate the sum capacity of the uplink multiuser multiple-input multiple-output (MIMO) multiple-access channel (MAC) in the low signal-to-noise ratio (SNR) regime. For each user, the MIMO channel is modeled according to a general class of stochastic channel matrices, known as double-scattering. Assuming that each user knows only their own spatial correlation matrices and employs optimal statistical beamforming transmission, we present new analytical approximations for the sum capacity of the MIMO-MAC for low SNR values. Our approximations are accurate, and lead to key insights into the effect of correlation.

In this work, we consider channel estimation for an amplify-and-forward (AF) two way relay network (TWRN), where two terminal nodes exchange information via a single relay node in between. A new concept of channel estimation by performing the power allocation at the relay node is introduced. As an example, we consider the maximum likelihood (ML) channel estimation at relay node and derive the power allocation factor such that the average effective signal-to-noise ratio (AESNR) at the terminal nodes is maximized. Nonetheless, the idea of using power allocation at relay node can be straightforwardly extended to more general scenarios. The simulation results show the advantages of the proposed method over the existing techniques.

In this paper, new dynamic resource allocation algorithms are presented for the downlink of multi-user MIMO-OFDMA/SDMA systems. Since it is difficult to obtain the optimal solution to the joint optimization problem, the whole procedure is divided into two steps, namely, the subcarrier-user scheduling and the resource allocation. In the first step, a new metric is proposed to measure the spatial compatibility of multiple users each with multiple receive antennas, based on which a new subcarrier-user scheduling algorithm is designed. In the second step, two dynamic resource allocation algorithms are developed to assign radio resources to the scheduled users accordingly. Simulation results demonstrate the superiority of the proposed algorithms in terms of the system throughput.

Single-carrier frequency division multiple access (SC-FDMA) has appeared to be a promising technique for high data rate communication in Long Term Evolution (LTE) uplink. In this paper, we propose an improved turbo equalizer for multiuser multiple-input multiple-output (MU-MIMO) LTE uplink to enhance the performance as the channel statement information is imperfect at the receiver. we derive the optimal soft-input soft-output (SISO) detector in turbo equalizer based on minimum mean square error (MMSE) criterion under imperfect channel estimation by concerning the statistical characteristic of the channel estimation error. It can be implemented in frequency-domain and almostly cause no increase in computational complexity compared to the conventional turbo equalizer for SC-FDMA. Simulation results for LTE scenario demonstrate that the proposed turbo equalizer yields 0.8-1.0dB gain to the turbo receiver which does not consider the imperfect CSI when channel estimation errors exist.

We investigate channel estimation for long term evolution (LTE) uplink over frequency-selective fading multiple-input multiple-output (MIMO) channels. As the conventional DFT-based channel estimation algorithm used for obtaining the channel impulse response (CIR) in wide band may not be applicable in narrow band of LTE uplink, which results an irreducible error floor, we propose an novel channel estimation based on discrete cosine transform (DCT), assuming only two users transmit simultaneously in the same frequency band (or SDMA). By exploiting the properties of reference signals of each user, type I DCT can separate channel response of each user in DCT-domain completely. Then, optimal channel estimation in the linear minimal mean square error (MMSE) sense in DCT-domain is derived. Finally, the performance of the proposed channel estimation is verified via simulation.

In this paper, we present a polynomial-based noise variance estimator for multiple-input multiple-output single-carrier block transmission (MIMO-SCBT) systems. It is shown that the optimal pilots for noise variance estimation satisfy the same condition as that for channel estimation. Theoretical analysis indicates that the proposed estimator is statistically more efficient than the conventional sum of squared residuals (SSR) based estimator. Furthermore, we obtain an efficient implementation of the estimator by exploiting its special structure. Numerical results confirm our theoretical analysis.

We investigate multiple-input multiple-output (MIMO) eigenmode transmission using statistical channel state information at the transmitter. We consider a general jointly correlated MIMO channel model, which does not require separable spatial correlations at the transmitter and receiver. For this model, we first derive a closed-form tight upper bound for the ergodic capacity, which reveals a simple and interesting relationship in terms of the matrix permanent of the eigenmode channel coupling matrix and embraces many existing results in the literature as special cases. Based on this closed-form and tractable upper bound expression, we then employ convex optimization techniques to develop low-complexity power allocation solutions involving only the channel statistics. Necessary and sufficient optimality conditions are derived, from which we develop an iterative water-filling algorithm with guaranteed convergence. Simulations demonstrate the tightness of the capacity upper bound and the near-optimal performance of the proposed low-complexity transmitter optimization approach.

This paper investigates the performance of multiple- input multiple-output (MIMO) multichannel beamforming systems in interference-limited Ricean fading channels. We derive new expressions for the exact symbol error rate, as well as the diversity order and array gain. Our results are based on new exact closed-form expressions which we derive for the marginal ordered eigenvalue distributions of finite-dimensional complex noncentral F-distributed random matrices. Numerical results are presented to validate the theoretical analysis.

In practical orthogonal frequency division multiplexing (OFDM) systems, some virtual subcarriers are reserved for easing the requirements on the filter. In this case, the conventional discrete Fourier transform (DFT)-based filtering in frequency-domain suffers from a spectral leakage, which results in an irreducible error floor. On the other hand, the DFT-based filtering or interpolation in time-domain also has performance degradation for system with high Doppler frequency. In order to solve these two problems, we propose a two dimensional discrete cosine transform (2D DCT)-based channel estimator for OFDM systems with virtual subcarriers and high Doppler frequency. Simulation results show that the performance of the proposed method can well approach the 2D minimum mean square error (MMSE) channel estimation.

This paper presents an analytical performance investigation of a Rayleigh-product multiple-input multiple-output (MIMO) channel using optimum transmit-receive beamforming. By deriving the closed-form expressions for the cumulative distribution function (C.D.F.), and the probability density function (P.D.F.), we provide a complete statistical characterization of the received signal-to-noise ratio (SNR) of the Rayleigh-product MIMO channel. These new statistical results further permit the analysis for the outage probability and the symbol-error-rate (SER), which are important performance indications of MIMO systems. In addition, we examine, in detail, an important special case corresponding to the degenerate keyhole scenario, for which we present insightful closed-form expressions for the diversity order and array gain.

This paper studies the power allocation problems for a multiple-input multiple-output (MIMO) antenna system with various partial channel state information (CSI) feedback. Through the derivation of new upper bounds of the ergodic capacity for both the cases of channel mean and covariance information feedback, we devise simple closed-form power allocation solutions for the MIMO systems with two transmit antennas. These results are then extended to systems with any number of antennas at both sides. Unlike previous approaches which require computationally complex numerical optimizations over random processes, our solutions in closed-form offer a water-filling interpretation and perform nearly the same as the global optimum.

The performance of linear receivers for detection of the single carrier frequency division multiple access transmission over frequency-selective fading channels is investigated in this work. Firstly, the cumulative distribution functions (CDF) of output signal to interference plus noise ratios (SINR) with zero forcing frequency domain equalization (ZF-FDE) and minimum mean squared error frequency domain equalization (MMSE-FDE) are derived by employing the numerical inversion of Laplace transforms. Next, based on the CDFs of the output SINRs, the approximate average bit error rate expressions as functions of the average receive signal to noise ratio for Gray-coded quaternary phase shift keying constellations are obtained. Finally, analytical and Monte Carlo simulated results are compared, and they perfectly agree for both ZF-FDE and MMSE-FDE.

We investigate the sum capacity of a fading multiple-input multiple-output (MIMO) multiple access channel (MAC) under a general class of fading, known as double-scattering. We assume the receiver has perfect channel state information (CSI), while the transmitters only have access to statistical CSI. We show that the optimum transmit directions for each user coincide with the eigenvectors of the user’s own transmit spatial correlation matrix. We also derive new closed-form upper bounds on the sum capacity of the MIMO-MAC under double-scattering, which we employ to obtain suboptimal power allocation policies. These policies are easy to compute, and require each user to know only their own channel statistics.

In this paper, we investigate training aided frequency offset estimation for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems via polynomial rooting over frequency selective fading channels. By designing the training sequences properly, both integer and fractional carrier frequency offsets (CFO) can be estimated from the polynomials corresponding to the cost function. Moreover, we show through analysis that rooting the cost function is equivalent to rooting the first-order derivative of the cost function in the considered MIMO case. Simulation results verify the good performance of our new CFO estimator and the corresponding analytical results.

This paper investigates low-complexity soft-input soft-output (SISO) detectors for multiple-input multiple-output (MIMO) transmission over frequency-selective channels. The detectors are developed with a unified detection procedure, that is space-time pre-filtering followed by a reduced dimension MAP detection. The condition of perfect pre-filtering is discussed and several suboptimal pre-filtering methods are proposed. Simulation results show that the proposed SISO detectors can make a good trade-off between performance and computational complexity in frequency-selective fading MIMO channels with various spatial correlating factor and Ricean components.

In this study, a user selection algorithm aiming to optimize the signal-to-noise ratio (SNR) is proposed for MIMO BC systems. After setting a threshold value, only users whose channel gains are above the set threshold are considered. While achieving the full multiplex gain, this method can get a desirable sum capacity and BER performance. Compared with the exhaustive search scheme, the system overheads, such as the amount of feedback and the number of selection in the base station, decrease evidently.

This paper analyzes MIMO systems with multichannel beamforming in Ricean fading. Our results apply to a wide class of multichannel systems which transmit on the eigenmodes of the MIMO channel. We first present new closed-form expressions for the marginal ordered eigenvalue distributions of complex noncentral Wishart matrices. These are used to characterize the statistics of the signal to noise ratio (SNR) on each eigenmode. Based on this, we present exact symbol error rate (SER) expressions. We also derive closed-form expressions for the diversity order, array gain, and outage probability. We show that the global SER performance is dominated by the subchannel corresponding to the minimum channel singular value. We also show that, at low outage levels, the outage probability varies inversely with the Ricean A*-factor for cases where transmission is only on the most dominant subchannel (i.e. a singlechannel beamforming system). Numerical results are presented to validate the theoretical analysis.

This paper analyzes bit error rate (BER) and outage probability of singular value decomposition-based multiple-input multiple-output systems with channel estimation error and feedback delay over uncorrelated Ricean fading channels. By utilizing marginal unordered and ordered eigenvalue distributions of complex noncentral Wishart matrices, we derive exact closed-form expressions on the average system performance and high signal- to-interference-plus-noise ratio (SINR) approximations on the individual eigen-subchannels, respectively, under the assumption of equal power allocation. Our expressions apply for various modulation formats and arbitrary numbers of transmit and receive antennas. Our results show that in low-to-moderate SINR regimes, both the BER and the outage probability increase with channel estimation error, feedback delay and the Ricean K-factor at a polynomial rate that is inversely proportional to the difference between the numbers of transmit and receive antennas. We also show that, with channel estimation error and feedback delay, the diversity orders of the BER and outage probability are zero and an irreducible error floor exists at high SINR.

This paper addresses carrier frequency offset (CFO) estimation and training sequence design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over frequency selective fading channels. By exploiting the orthogonality of the training sequences in the frequency domain, integer. CFO (ICFO) is estimated. With the uniformly spaced non-zero pilots in the training sequences and the corresponding geometric mapping, fractional CFO (FCFO) is estimated through the roots of a real polynomial. Furthermore, the condition for the training sequences to guarantee estimation identifiability is developed. Through the analysis of the correlation property of the training sequences, two types of sub-optimal training sequences generated from the Chu sequence are constructed. Simulation results verify the good performance of the CFO estimator assisted by the proposed training sequences.

We investigate the capacity of a general class of MIMO channels, known as double-scattering channels. Our results assume perfect channel state information (CSI) at the receiver and statistical CSI at the transmitter. We first derive the optimal capacity-achieving signaling directions, and show that they correspond to the eigenvectors of the transmit spatial correlation matrix. We then derive new simple closed-form power allocation policies which are shown to yield negligible capacity loss compared with the optimal power allocation policy, without requiring complicated numerical optimization methods to compute. Finally we derive a necessary and sufficient condition for which a beamforming approach (i.e. rank-1 transmission) achieves the ergodic capacity.

The IEEE 802.16 mesh mode coordinated distributed scheduling has been expected to be collision free so that only one node can transmit at any transfer opportunity (TO) in the two-hop neighborhood. We will show in this paper that this collision free property is not unconditional. Assuming the common interference modeling, necessary and sufficient conditions under which the IEEE 802.16 mesh mode coordinated distributed scheduling can be collision free are identified. As the IEEE 802.16 standard defines only a framework for the mesh mode coordinated distributed scheduling, our study could provide a suggestion on how to guarantee the correct functionality.

In this paper, we consider a general family of MIMO channels known asdoublescatteringchannels, which encompass a variety of propagation environments from independent and identically distributed Rayleigh to degenerate Keyhole or pinhole cases by embracing both rank-deficiency and spatial correlation effects. We investigate the optimal transmit strategy of a single-user MIMO systems with channel statistical information feedback in the presence of double scattering. We show that transmitting in the direction of the eigenvectors of the transmit correlation matrix is the optimal transmission strategy under the criteria of maximizing the ergodic capacity. In addition to this, the power allocation problem is studied and a necessary and sufficient condition for optimality of beamforming is derived.

This paper analyzes the average bit error rate (BER) performance of singular value decomposition-based multiple-input multiple-output systems with channel estimation error and feedback delay over uncorrelated Ricean fading channels. By utilizing marginal unordered eigenvalue distributions of complex noncentral Wishart matrices, we derive exact closed- form BER expression under the assumption of equal power allocation. Our results apply for various modulation formats and arbitrary numbers of transmit and receive antennas. Our results show the average BER increases with channel estimation error, feedback delay and Ricean A'-factor at a polynomial rate that is inversely proportional to the difference between the numbers of transmit and receive antennas. We also show that the achievable BER performance is limited by the presence of an irreducible error floor as the signal-to-interference-plus-noise ratio increases.

This paper addresses a low complexity frequency offset estimator for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over frequency selective fading channels. By exploiting the good correlation property of the training sequences, which are constructed from the Chu sequence, carrier frequency offset (CFO) estimation is obtained with great complexity reduction through factor decomposition for the derivative of the cost function. The CFO estimate's variance and Cramer-Rao bound (CRB) are developed to optimize the parameter of the simplified estimator and also to evaluate the estimation performance. Simulation results verify the good performance of the training-assisted CFO estimator.

In this paper, we consider the power allocation problem for single-user multiple-input multiple-output (MIMO) systems. We propose new sub-optimal power allocation algorithms for MIMO systems with channel mean or covariance information at the transmitter, through the maximization of a lower bound of the ergodic channel capacity. Assume that the receiver has perfect channel knowledge. The algorithms proposed here need only the channel statistical information, and are very useful for systems in which only the channel statistical information is accessible to the transmitter. Simulation results confirm the performance.

Low complexity channel estimation for single-carrier block transmission systems over multiple-input multiple-output time varying frequency-selective channels is investigated. A time slot structure that uses Golay complementary sequences with perfect periodic autocorrelations as two-sided pilot blocks is presented. Employing this strucutre, optimal least square estimate in the minimum mean square error (MMSE) sense is achieved. Furthermore, a computationally efficient algorithm which is named as fast periodic Golay correlation is proposed based on the specific generator and the properties related to circulant matrices. Finally, the simulation results show the MMSE performance of the proposed scheme and algorithm.

This letter derives the asymptotic symbol error rate (SER) and outage probability of multiple-input multiple-output (MIMO) maximum ratio-combining (MRC) systems. We consider Rayleigh fading channels with both transmit and receive spatial correlation. Our results are based on new asymptotic expressions that we derive for the p.d.f. and c.d.f. of the maximum eigenvalue of positive-definite quadratic forms in complex Gaussian matrices. We prove that spatial correlation does not affect the diversity order but that it reduces the array gain and hence increases the SER in the high SNR regime

We investigate channel estimation for timeslot-structured single-carrier block transmission (SCBT) over space-, time-, and frequency-selective fading multiple-input multiple-output (MIMO) channels. A MIMO-SCBT with a dual cyclic timeslot structure is presented first. Then, an optimal channel estimation in the minimal mean square error (MMSE) sense on the timeslot basis is investigated. It is shown that the optimal pilots for the timeslot-based MMSE channel estimation are related to the statistical channel state information in eigenmode. Under the assumption that the transmit correlation is unknown at the transmitter, the optimal pilots satisfy the same condition as reported for the block-based least-square (LS) channel estimation in literature, and the channel estimation can be simplified to initial block-based LS channel estimation followed by space-time postprocessing. Particularly, for spatially uncorrelated channels, the space-time postprocessing can be reduced to pathwise processing. A new design of the pilot sequences is given, which leads to an efficient implementation of the channel estimation. Later on, a more efficient implementation for the initial channel estimation is obtained by using the structure of the pilot sequences, and discrete cosine transform (DCT)-based implementation is developed for the space-time postprocessing to approximate the optimal solution with low implementation complexity. Finally, the performance of the proposed channel estimation is verified via simulations.

This paper investigates the ergodic capacity of Ricean-fading multiple-input-multiple-output (MIMO) channels with rank-1 mean matrices under the assumption that the channel is unknown at the transmitter and perfectly known at the receiver. After introducing the system model and the concept of ergodic capacity of MIMO channels, we derive the explicit expressions for the expected values of the determinant and log-determinant of complex noncentral Wishart matrices. Subsequently, we obtain new upper and lower bounds on the ergodic capacity of rank-1 Ricean-fading MIMO channels at any signal-to-noise ratio (SNR). We show that our bounds are tighter than previously reported analytical bounds, and discuss the impact of spatial fading correlation and Ricean K-factor with the help of these bounds. Furthermore, we extend the analysis of ergodic capacity to frequency selective spatially correlated Ricean-fading MIMO channels. We demonstrate that the calculation of ergodic capacity of frequency selective fading MIMO channels can be converted to the calculation of the one of equivalent frequency flat-fading MIMO channels. Finally, we present numerical results that confirm the theoretical analysis

Considering frequency-selective Nakagami-m fading multiple-input multiple-output (MIMO) channels in the presence of spatial correlation, we study the error probability of orthogonal space-time block-coded (OSTBC) orthogonal frequency-division multiplexing (OFDM) systems. Exploiting the equivalent single-input single-output (SISO) OFDM model, we derive an exact and closed-form expression for the moment generating function (MGF) of effective instantaneous signal-to- noise ratio (SNR). By using a well-known MGF-based approach and the Gauss-Legendre quadrature rule, a simple and accurate numerical solution for the average symbol error rate (SER) of coherent MPSK signals is provided. In addition, the impact of complex component correlation among the MIMO channel links on the SER performance is assessed by fading figure and numerical examples.

This paper investigates the tradeoff between diversity and multiplexing of orthogonal space-time block coding (OSTBC) for correlated Nakagami-m fading. For a given transmission rate, the outage probability as function of the multiplexing gain and signal-to-noise ratio (SNR) is derived. Following the negative slope of the outage probability versus SNR curve, we obtain the diversity gain at finite SNR and its maximum attainable value to predict the system performance at operating SNR's. In addition, the asymptotic diversity gain at high SNR is given via two approaches, which provides a theoretical limit for practical SNR's. The impact of the complex component correlation among the channel links on the outage performance is also presented.

The problem of joint eigenstructure estimation for the non-defective matrices is addressed. A procedure revealing the joint eigenstructure by simultaneous diagonalization with unitary and non-unitary similarity transformations alternately is proposed to overcome the convergence difficulties of previous methods based on simultaneous Schur form and unitary transformations. It can be proved that its asymptotic convergence rate is ultimately quadratic. Numerical experiments are conducted in a multi-dimensional harmonic retrieval application and suggest that the method presented here converges considerably faster than the methods based on only unitary transformation for matrices which are not near to normality

New frequency domain training sequences are proposed sedfor carrier frequency offset (CFO) estimation in multipleinput multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels. By exploiting frequency domain orthogonality of the training sequences, integer CFO (ICFO) can be estimated without matrix inversion operation. With the non-zero pilots in the training sequences uniformly spaced, fractional CFO (FCFO) can be estimated through the roots of a complex polynomial. Moreover, a simplified CFO estimator is also proposed which exploits a geometric mapping to transform the complex polynomial to a real one. Simulation results illustrate the good performances of the CFO estimators assisted by the proposed training sequences.

Carrier frequency offset (CFO) estimation in an OFDM system is usually divided into fractional frequency offset (FFO) estimation and integer frequency offset (IFO) estimation. The former is generally done in the time domain while the latter in the frequency domain. We propose a maximum likelihood estimation algorithm for IFO estimation which works in the time domain using two FFOs with different estimation ranges. After introducing the concepts of frequency offset diagram, solution subset and minimum distance, we reveal the close relationship between the minimum distance and the error rate of the ML estimation result. Our further analysis exhibits a performance-range tradeoff which is tunable by the choice of the greatest common divisor (GCD) of two integers.

We derive new closed-form expressions for the marginal cumulative distribution function (c.d.f.) and marginal probability density function (p.d.f.) of the ordered eigenvalues of complex noncentral Wishart matrices. These results are used to analyze the performance of singular value decomposition (SVD) based MIMO systems in Ricean fading channels. New expressions are derived for the exact symbol error rate (SER), as well as the diversity order and array gain. Our results show that the global SER performance is dominated by the subchannel SER corresponding to the minimum channel singular value. Numerical results are presented to validate the theoretical analysis

The problem of transmit antenna subset selection is considered for Rayleigh fading MIMO (multiple-input multi-output) channels with transmit correlation. We provide an active antennas control algorithm for minimum mean square error (MMSE) receivers, assuming the transmitter correlation matrices are available. Using Bartlett's decomposition and Cauchy-Binet theorem, we derive a tight closed-form lower bound on the capacity of correlated Rayleigh MIMO channels. Based on this lower bound, we propose a transmit antenna selection criterion in terms of channel capacity maximization. Monte Carlo simulation results also validate our theoretical analysis.

Antenna selection based on statistical channel knowledge (SCK) is considered for double-sided correlated Rayleigh fading multiple-input multi-output (MIMO) channels. We address the problem of the antenna subset selection at both the transmitter and receiver in spatial multiplexing systems. The selection goal is to maximize channel capacity or maximize the minimum receive SNR. Using Minkowski's inequality, We derive two lower bounds on the resulted system capacity. Subsequently, two antenna selection algorithms in terms of capacity maximization are derived by employing these lower bounds. This paper also develops a selection algorithm for zero forcing (ZF) receiver by using Weyl's theorem. Monte Carlo simulation results validate our theoretical analysis

In this paper, an efficient digital implementation of multicarrier transmission scheme based on generalized discrete Fourier transform (DFT) filter banks is presented for multicarrier code-division multiple-access (MC-CDMA) systems. Generalized DFT filter banks has been traditionally discussed for very high-speed digital subscriber lines (VDSL) wireline systems, received interest also for wireless applications. The design of the generalized DFT modulated filter banks is investigated and its fast implementation is derived in the time-domain for arbitrary integer sampling rate. Through the utilization of the generalized DFT modulated filter banks, one or more subcarriers, even noncontiguous subcarriers can be easily supported by system in both uplink and downlink, which facilitates the users to access the network in a frequency-division multiple-access manner. Simulation results show that the overall bit-error-rate performance of the proposed multicarrier transmission scheme well approaches that of a single-carrier system due to the negligible intercarrier interference introduced by an appropriate design of the generalized DFT modulated filter banks.

A ratio-adjustable time-frequency training sequence and the corresponding carrier frequency offset (CFO) estimator are proposed for orthogonal frequency division multiplexing (OFDM) systems over frequency-selective fading channels. The proposed method estimates the coarse and fine CFO through the training sequence in frequency domain and time domain separately. Computation complexity can be greatly decreased by employing predefined lookup table. Simulation results show that the proposed estimator has better performance with almost the same overhead compared with Lei's method in (2004).

The ergodic capacity of multiple antenna system in Ricean fading is considered under the assumption that channel state information (CSI) is available only at the receiver and the transmitter has no knowledge of the CSI. Using complex noncentral Wishart distribution and Bartlett's decomposition, we derive tight closed-form lower bounds on the ergodic capacity of such channels. We have also derived analytical approximations to the lower bound. Moreover, we show that our lower bounds tighter than previously reported analytical lower bounds. Numerical results also show that our results closely match the actual capacity of Ricean fading MIMO channels.

This paper addresses the problem of the soft decision equalization. We show the relation between the iterative soft decision interference cancellation (ISDIC) and probabilistic data association (PDA) multiuser detector (PDA-MUD). We prove that ISDIC is equivalent to PDA-MUD, and give the block-wise implementation of them. We also present a soft interference cancellation algorithm for the polynomial expansion linear detector. With its low complexity, simple implementations, and impressive performance offered by iterative soft-decision processing, it is an attractive candidate to deliver efficient reception solutions to practical wireless transmission systems. Simulation comparisons of the new method with ISDIC are presented under the frequency selective channels.

In this paper, we present a generalized multi-carrier (GMC) transmission technique in multi-input and multi-output (MIMO) channel environments for beyond 3G mobile communications. Several key techniques including the design and implementation of the GMC synthesis/analysis system, adaptive double cyclic timeslot structure, pilot design and channel estimation, and iterative detection/decoding are briefly discussed. Link-level simulation results in Rayleigh fading MIMO channels are provided

The ergodic capacity of a multiple antenna system in Rician fading is considered under the assumption that channel state information (CSI) is available only at the receiver and the transmitter has no knowledge of the CSI. Using complex non-central Wishart distribution and Bartlett's decomposition, we derive a tight closed-form upper bound on the ergodic capacity of such channels. We show that our bound is tighter than previously reported analytical bound. Moreover, we provide an approximation of ergodic capacity in high-SNR regime. Numerical results also show that our results closely match the actual ergodic capacity of Rician fading MIMO channels.

In this paper, a low complexity space-frequency iterative detection and decoding scheme is developed for multiuser space-time block-coded (STBC) block transmission systems, such as the cyclic prefix based single-carrier block transmission (CP-SCBT) system and OFDM system. Using the algebraic properties of such systems, the MMSE-based turbo detection algorithm can be implemented in the frequency domain and then the complexity of the matrix inversion can be reduced greatly. The performance of the iterative receiver for multiuser STBC block transmission systems with bit-interleaved coded modulation (BICM) is evaluated in mobile multipath frequency selective channels through computer simulations. It has been shown that the proposed receiver significantly outperforms the conventional noniterative receiver. Moreover, at high signal-to-noise ratio, the detrimental effects of multiple-access interference (MAI) and intersymbol interference (ISI) in the channel can almost be completely overcome by the turbo processing, and the performance is very close to that of BICM in a Gaussian channel.

Joint channel estimation and symbol detection for space-frequency block coded OFDM (SFBC-OFDM) systems is considered. The expectation-maximization (EM) algorithm is employed to devise both soft-in-hard-out (SIHO) maximum likelihood (ML) iterative receivers and soft-in-soft-out (SISO) maximum a posteriori (MAP) iterative receivers for SFBC-OFDM systems. Besides the low computational complexity and fast convergence, the EM-based iterative receivers also have the ability of tracing the variation of fast fading channels, and thus provide quite satisfying performance and bandwidth efficiency even in high-vehicular-speed environment. Numerical results are provided to corroborate the theoretical designs.

We study the low complexity space-time turbo detection and decoding schemes for MIMO block transmission systems such as cyclic prefix based single-carrier block transmission (CP-SCBT) system and OFDM system. Because of the circulant property of the channel matrices, the detectors can he implemented by FFT/IFFT. Simulation results show that the proposed receivers significantly outperform the traditional, non-iterative receivers.

We study the low complexity turbo detector for cyclic prefix based single-carrier block transmission (CP-SCBT) systems with multiple receive antennas. It has the following characteristics: firstly, it can be implemented by FFT/IFFT with low complexity; secondly, along with SISO decoder, turbo detection can be applied. Simulation results show that the iterative receiver provides much better performance than the conventional CP-SCBT system. It also outperforms its coded OFDM counterpart. The most attractive advantage is that it can be implemented by FFT/IFFT without significantly increasing the complexity of the system.

In this paper we propose low complexity turbo detection and decoding schemes for space-time block-coded block transmission (STBC-BT) systems. Because of the circulant property of the channel matrices, the detectors can be implemented in the frequency domain. Simulation results show that the performance of the proposed receivers is better than that of the traditional, noniterative receivers.

For orthogonal frequency-division multiplexing (OFDM) systems with pilot-symbol-aided channel estimation, a straightforward way to overcome system throughput deterioration is by decreasing the power and reducing the number of pilots, which unfortunately, leads directly to the degradation of BER performance. For the purpose of improving the BER performance, an iterative joint channel estimation and symbol detection algorithm with low complexity and fast convergence is proposed. Both theoretical analysis and computer simulation show that this algorithm gives better BER performance and saves the overhead of OFDM systems.

In the broadband communication, channel impulse response usually exhibits sparse behavior (i.e.. many nearly zero taps). This paper considers the channel estimation of the sparse channel for broadband multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. Three algorithms for MIMO-OFDM sparse channel estimation are presented and compared: least square channel estimation (LSCE), constraint least square channel estimation (CLSCE) with ideal delay estimation, matching pursuit based channel estimation (MPCE). Mean square error (MSE) performances an also analyzed. Simulation results show that MPCK has better performance than LSCE and is very close to CLSCE under the high SNR, and also MPCE does not require the a priori of channel. Using the MP-based channel estimator, the detection performance is nearly optimal.

The sparsity of multipath fading channel is introduced, and then the improved MMSE channel estimation for orthogonal frequency-division multiplexing (OFDM) system is presented. With computationally simple algorithm, the proposed channel estimator extracts the channel information from its noisy version accurately by utilizing the correlation of the taps of finite impulse response (FIR) filter channel model. Simulation results show that the proposed channel estimation gives much better performance than LS and DFT-based MMSE channel estimator.

Two-channel orthogonal and symmetric complex-valued FIR filter banks and their corresponding wavelets are investigated. First, the conditions for the filter bank to be orthogonal, symmetric and regular are presented. Then, a complete and minimal lattice structure is developed, which enables a general design approach for filter banks and wavelets with arbitrary length and arbitrary order of regularity. Finally, two integer implementation methods that preserve the perfect reconstruction property are proposed. Their performances are evaluated via experimental results.

An efficient algorithm is proposed to reduce the computation cost of block matching algorithms for motion estimation in video coding. Based on a new insight in block matching algorithms, we extend the successive elimination algorithm to a multilevel case. By using the sum norms of the blocks and the subblocks, tighter and tighter decision boundaries can be obtained for eliminating the search positions. The efficiency of the proposed algorithm combined with the full search algorithm and several fast search algorithms is verified by simulation results.

In this paper, the fast implementation of cosine-modulated filter bank (CMFB) is revisited. A class of paraunitary CMFBs with arbitrary length is considered. By further reorganizing of the polyphase component matrix and using the linear-phase property of the prototype filter, we obtain a more efficient implementation structure for the CMFB, in which we use 2/spl times/2 lossless matrices instead of 2/spl times/1 ones. In the new implementation, the number of two-channel lossless lattices is reduced by a factor of two.

This paper presents a decision feedback neural network (NN) coherent receiver scheme for continuous phase modulation based on frequency domain. Through decision feedback pre-processing, the effect of the previous transmitted symbols can be removed for the present symbol decision. By employing a Karhunen-Loeve transform (KLT) or discrete cosine transform (DCT), the input data number of neural networks can be reduced significantly. To obtain more sufficient convergence of neural networks a modified "delta-bar-delta" BP learning algorithm is proposed. Despite the low complexity in NN training and implementation, computer simulation results show that our NN receivers can achieve near optimal demodulation performance.

A modified fractal transform (MFT) is presented. In the function part of the MFT, the conventional greyscale function of an image block is replaced by the greyscale function of an error image block whose mean is removed. This fractal transform is used to approximate an image which is to be encoded. The simulation results show that with the MFT the image decoding process is very fast, typically only 1 to 3 iterations are required to reconstruct the image while the quality of reconstructed image remains high.

This paper presents a subband filter bank model of N subbands for orthogonally multiplexed QAM system with 2N subchannels. Then, a fast implementation algorithm using polyphase decomposition and discrete cosine transform (DCT) is derived from this model. Compared with the implementation method through discrete Fourier transform, only half the computation cost is needed. Finally, we give the adaptive equalization scheme based on subband adaptive filter theory, and its efficiency is verified by experiments.