The mid-band frequency range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is emerging as a key enabler for future communication systems. By exploiting the advent of new spectrum resources and degrees of freedom brought by the near-field propagation, the mid-band XL-MIMO system is expected to significantly enhance throughput and inherently support advanced functionalities such as integrated sensing and communication. Although theoretical studies have highlighted the benefits of mid-band XL-MIMO systems, the promised performance gains have yet to be validated in practical systems, posing a major challenge to the standardization. In this paper, preliminaries including frame structure, channel modeling, and signal models are first discussed, followed by an analysis of key challenges in constructing a real-time prototype system. Subsequently, the design and implementation of a real-time mid-band XL-MIMO prototype system are presented. Underpinned by a novel architecture, the proposed prototype system supports specifications aligned with standardization, including a bandwidth of 200 MHz, up to 1024 antenna elements, and up to 256 transceiver chains. Operating in time-division duplexing mode, the prototype enables multiuser communication for up to 12 users, while retaining standard communication procedures. Built on hybrid software-defined radio and field programmable gate array platforms, the prototype is programmable and allows for flexible deployment of advanced algorithms. Moreover, the modular architecture ensures high scalability, making the prototype adaptable to various configurations, including distributed deployments and decentralized signal processing. Experimental results demonstrate that the prototype handles real-time digital sample processing at 1453.33 Gbps and achieves a peak data throughput of 15.81 Gbps for 12 users.

This paper investigates the design of distributed precoding for multi-satellite massive MIMO transmissions. We first conduct a detailed analysis of the transceiver model, in which delay and Doppler precompensation is introduced to ensure coherent transmission. In this analysis, we examine the impact of precompensation errors on the transmission model, emphasize the near-independence of inter-satellite interference, and ultimately derive the received signal model. Based on such signal model, we formulate an approximate expected rate maximization problem that considers both statistical channel state information (sCSI) and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation fails to maintain equivalence in the considered scenario. To address this, we introduce an equivalent covariance decomposition-based WMMSE (CDWMMSE) formulation derived based on channel covariance matrix decomposition. By exploiting the channel characteristics, we develop a low-complexity decomposition method and propose an optimization algorithm. To further reduce computational complexity, we introduce a model-driven scalable deep learning (DL) approach that leverages the equivariance of the mapping from sCSI to the unknown variables in the optimal closed-form solution, enhancing performance through novel dense Transformer network and scaling-invariant loss function design. Simulation results validate the effectiveness and robustness of the proposed method in some practical scenarios. We also demonstrate that the DL approach can adapt to dynamic settings with varying numbers of users and satellites.

The upper mid-band balances coverage and capacity for the future cellular systems and also embraces extremely large-scale multiple-input multiple-output (XL-MIMO) systems, offering enhanced spectral and energy efficiency. However, these benefits are significantly degraded under mobility due to channel aging, and further exacerbated by the unique near-field (NF) and spatial non-stationarity (SnS) propagation in such systems. To address this challenge, we propose a novel channel prediction approach that incorporates dedicated channel modeling, probabilistic representations, and Bayesian inference algorithms for this emerging scenario. Specifically, we develop tensor-structured channel models in both the spatial-frequency-temporal (SFT) and beam-delay-Doppler (BDD) domains, which capture the NF and SnS propagation effects and leverage temporal correlations among multiple snapshots for channel prediction. In this model, the factor matrices of multi-linear transformations are parameterized by BDD domain grids and SnS factors, where beam domain grids are jointly determined by angles and slopes under spatial-chirp based NF representations. To enable tractable inference, we replace these environment-dependent BDD domain grids with uniformly sampled ones, and introduce perturbation parameters in each domain to mitigate grid mismatch.We further propose a hybrid beam domain strategy that integrates angle-only sampling with slope hyperparameterization to avoid the computational burden of explicit slope sampling. On this basis, we develop tensor-structured bi-layer inference (TS-BLI) algorithm under the expectation-maximization (EM) framework, which reduces the computational complexity by leveraging the inherent separation across different domains. In the E-step, we develop the bi-layer factor graph representation to isolate the bilinear mixing in the spatial domain induced by SnS propagation, thus facilitating bi-layer iterations using approximate inference techniques. In the M-step, we leverage an alternating strategy for hyperparameter learning, with closed-form rules derived by the quadratic approximation of objective functions. Numerical simulations based on a near-practical channel simulator developed upon QuaDRiGa with SnS extensions demonstrate the superior channel prediction performance of the proposed algorithm.

Integrated sensing and communication (ISAC) has emerged as a key enabler for future wireless networks by unifying communication and sensing functionalities within a shared framework. However, achieving the coordination gains of these two functionalities critically depends on accurate estimation of the sensing targets and communication channels, while their joint estimation remains challenging. To address this, this paper proposes a Bayesian tensor decomposition (BTD) approach for joint channel estimation and target sensing in multiple-input multiple-output (MIMO)-ISAC systems, where parts of sensing targets also act as communication scatterers. Specifically, we develop space-frequency domain received signal models for target sensing and channel estimation and formulate them as canonical polyadic decomposition (CPD) problems under the tensor decomposition framework. This formulation reveals the common multilinear structure and the partially shared physical parameters between sensing and communication, which underpins the ensuing joint estimation task. To solve these problems, we propose a dual-module Vandermonde structure-assisted BTD (V-BTD) algorithm that incorporates propagation-induced Vandermonde structure constraints within a Bayesian framework to enable effective sensing-communication collaboration while maintaining problem feasibility. In this algorithm, Module A estimates the factor matrices via unstructured BTD with Gaussian priors, whereas Module B exploits the Vandermonde structure to recover the underlying physical parameters using generalized von Mises priors. The dual-module design alternates between an unstructured tensor decomposition step and a structure-aware parameter recovery step, yielding a favorable trade-off between inference exactness and computational tractability. With the flexible prior models in the BTD framework, the proposed algorithm supports both uninformative and informative settings, thereby allowing sensing-derived information to be incorporated for communication channel estimation to further improve estimation accuracy. Simulation results demonstrate that the proposed method significantly outperforms the benchmarks, highlighting its superiority for advanced ISAC systems.

Mid-band extra large-scale multiple-input multiple-output (XL-MIMO) systems are considered a key enabler for future wireless communications, offering enhanced throughput and extended coverage. Combined with subarray-based architecture and distributed signal processing, the computational complexity and implementation overhead are reduced. However, uncertain channel characteristics associated with the novel frequency band present significant bottlenecks, hindering the development of hardware architecture and algorithm design. Meanwhile, channel characteristics across distributed processing units remain insufficiently explored. In response, a mid-band channel sounder is constructed, and extensive measurement campaigns are carried out across various typical scenarios. Initially, mid-band channel characteristics are unveiled and compared across different scenarios. Subsequently, the mid-band XL-MIMO channel characteristics are analyzed using a virtual array comprising 256 array antennas and 64 transceiver chains. Moreover, motivated by the potential of distributed processing, mid-band XL-MIMO channel characteristics are particularly investigated from the perspectives of subarrays and sub-bands, encompassing subarray-wise non-stationarities, consistencies, far-field approximations, and sub-band characteristics. Through the combination of analysis and measurement validation, several insights and benefits are revealed, particularly relevant to distributed architecture and processing, which provides practical guidance for the real-world deployment of mid-band XL-MIMO systems.

With the growing number of users in multi-user multiple-input multiple-output (MU-MIMO) systems, demodulation reference signals (DMRS) are efficiently multiplexed in the code domain via orthogonal cover codes (OCC) to ensure orthogonality and minimize pilot interference. In this paper, we investigate uplink DMRS-based channel estimation for MU-MIMO systems with Type II OCC pattern standardized in third generation partnership project (3GPP) Release 18, leveraging location-specific statistical channel state information (SCSI) to enhance performance. Specifically, we propose a SCSI-assisted Bayesian channel estimator (SA-BCE) based on the minimum mean square error criterion to suppress the pilot interference and noise, albeit at the cost of cubic computational complexity due to matrix inversions. To reduce this complexity while maintaining performance, we extend the scheme to a windowed version (SA-WBCE), which incorporates antenna-frequency domain windowing and beam-delay domain processing to exploit asymptotic sparsity and mitigate energy leakage in practical systems. To avoid the frequent real-time SCSI acquisition, we construct a grid-based location-specific SCSI database based on the principle of spatial consistency, and subsequently leverage the uplink received signals within each grid to extract the SCSI. Facilitated by the multilinear structure of wireless channels, we formulate the SCSI acquisition problem within each grid as a tensor decomposition problem, where the factor matrices are parameterized by the multi-path powers, delays, and angles. The computational complexity of SCSI acquisition can be significantly reduced by exploiting the Vandermonde structure of the factor matrices. Simulation results demonstrate that the proposed location-specific SCSI database construction method achieves high accuracy, while the SA-BCE and SA-WBCE significantly outperform state-of-the-art benchmarks in MU-MIMO systems.

This paper investigates robust transmit (TX) beamforming from the satellite to user terminals (UTs), based on statistical channel state information (CSI). The proposed design specifically targets the mitigation of satellite-to-terrestrial interference in spectrum-sharing integrated terrestrial and satellite networks. By leveraging the distribution information of terrestrial UTs, we first establish an interference model from the satellite to terrestrial systems without shared CSI. Based on this, robust TX beamforming schemes are developed under both the interference threshold and the power budget. Two optimization criteria are considered: satellite weighted sum rate maximization and mean square error minimization. The former achieves a superior achievable rate performance through an iterative optimization framework, whereas the latter enables a low-complexity closed-form solution at the expense of reduced rate, with interference constraints satisfied via a bisection method. To avoid complex integral calculations and the dependence on user distribution information in inter-system interference evaluations, we propose a terrestrial base station position-aided approximation method, and the approximation errors are subsequently analyzed. Numerical simulations validate the effectiveness of our proposed schemes.

In moderate- to high-mobility scenarios, channel state information (CSI) varies rapidly and becomes temporally non-stationary, leading to severe performance degradation in the massive multiple-input multiple-output (MIMO) transmissions. To address this issue, we propose a tensor-structured approach to dynamic channel prediction (TS-DCP) for massive MIMO systems with temporal non-stationarity, exploiting both dual-timescale and cross-domain correlations. Specifically, due to inherent spatial consistency, non-stationary channels over long-timescales can be approximated as stationary on short-timescales, decoupling complicated temporal correlations into more tractable dual-timescale ones. To exploit such property, we propose the sliding frame structure composed of multiple pilot orthogonal frequency-division multiplexing (OFDM) symbols, which capture short-timescale correlations within frames by Doppler domain modeling and long-timescale correlations across frames by Markov/autoregressive processes. Building on this, we develop the Tucker-based spatial-frequency-temporal domain channel model, incorporating angle-delay-Doppler (ADD) domain channels and factor matrices parameterized by ADD domain grids. Furthermore, we model cross-domain correlations of ADD domain channels within each frame, induced by clustered scattering, through the Markov random field and tensor-coupled Gaussian distribution that incorporates high-order neighborhood structures. Following these probabilistic models, we formulate the TS-DCP problem as variational free energy (VFE) minimization, and unify different inference rules through the structure design of trial beliefs. This formulation results in the dual-layer VFE optimization process and yields the online TS-DCP algorithm, where the computational complexity is reduced by exploiting tensor-structured operations. Numerical simulations demonstrate the significant superiority of the proposed algorithm over benchmarks in terms of channel prediction performance.

The fast time-varying channels in high-speed scenarios impact signal transmission between transceivers and pose challenges to both the accuracy and bandwidth utilization of communication systems. Semantic communication, known for its ability to significantly reduce transmission bandwidth and enhance communication reliability, is especially effective in extreme environments. However, current semantic communication systems lack a comprehensive physical layer design, which limits their ability to achieve optimal performance in rapidly changing conditions. In this paper, we propose an adaptive semantic speech recognition and cloning transmission system with a superimposed pilot (SwitchAC-SIP) tailored for high-speed scenarios to ensure high-quality speech transmission. The system converts speech signals into textual content and speaker timbre features at the transmitter, while a speech cloning model reconstructs the speech at the receiver with a timbre closely resembling the original speaker based on these features, thereby eliminating the need to retrain the speech generation model for different users, ensuring both transmission quality and efficiency. To address the impact of high-speed environments on channel estimation performance, we introduce a superimposed pilot (SIP) in the physical layer. This method superimposes pilots and data across the entire time-frequency grid with a specific power ratio, significantly mitigating the detrimental effects of high-speed conditions on semantic communication systems. Furthermore, to enhance system flexibility in dynamic scenarios, we design a channel-adaptive network that dynamically allocates bandwidth ratios for text and audio semantics based on real-time channel conditions. This adaptive approach prioritizes the protection of critical semantic features according to user requirements. Simulation results demonstrate the substantial improvements in transmission efficiency and accuracy achieved by the proposed system.

Large AI models (LAMs) offer new opportunities for wireless intelligence, but their deployment in latency- and resource-constrained systems remains challenging. To explore this in the context of channel state information (CSI) feedback for frequency-division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems, we propose a novel framework, OLAMCF, that integrates LAMs via offline codebook optimization, thereby avoiding the need for real-time inference. Specifically, the large vision model (LVM) at the core of this framework is built upon a vision-based backbone, pre-trained on large-scale image datasets and fine-tuned with site-specific CSI. This strategy allows this framework to capture the structural similarity between CSI and image to refine codewords from the conventional codebook and generate customized codebooks tailored to the specific environments. Simulation results show that our approach significantly outperforms existing schemes in both reconstruction accuracy and system throughput, without introducing additional inference latency or computational overhead. This design philosophy—extracting the best offline and discarding the rest online—offers a practical perspective on integrating LAMs into communication systems.

This paper investigates robust transmit beamforming based on statistical channel state information (CSI), against satellite-to-terrestrial user terminal (UT) interference arising from spectrum sharing in the integrated terrestrial and satellite network. First, we develop an integral-form interference model free of shared CSI to characterize the interference from satellite to terrestrial UTs. Then, we propose a robust interference-avoidance transmit beamforming scheme under the interference threshold and power budget. We derive a closed-form solution based on the minimum mean square error criterion and apply a bisection method to satisfy interference thresholds. Furthermore, we introduce a base station position-aided approximation scheme to eliminate the complex integral calculations. Numerical simulations validate the proposed schemes.

With the burgeoning advancement of mega low earth orbit (LEO) satellite constellations, multi-antenna gateway station (MAGS) has emerged as a key enabler to support extremely high system capacity via massive feeder links. However, the densification of both space and ground segment will lead to reduced spatial separation between links, posing unprecedented challenges of interference exacerbation. This paper investigates graph coloring-based frequency allocation methods for interference mitigation (IM) of mega LEO satellite communication (SatCom) systems. We reveal the characteristics of MAGS interference pattern and formulate the IM problem of mega LEO systems into a K-coloring problem using an adaptive threshold method. Then, we propose a tailored graph coloring algorithm called Clique-Based Tabu Search (CTS), which leverages the unique clique structure brought by MAGSs to achieve outstanding IM performance. Simulation results demonstrate the superiority and effectiveness of the proposed methodology.

This paper presents a dual transformer-based robust precoding scheme for massive multiple-input multiple-output systems with low computational complexity. By utilizing the a posteriori channel model, the imperfect channel state information (CSI) is modeled as the statistical CSI that incorporates channel mean and channel variance information with spatial correlation. Based on this, we formulate a robust precoding problem aimed at maximizing the expected sum rate and subsequently transform it into a robust weighted minimum mean square error problem. We prove the permutation equivariance and invariance satisfied by the mapping from the available CSI to the low-dimensional variables in the optimal closed-form solution. To fully exploit such properties, we design a dual transformer block with residual connection and introduce an invariant transformer module to construct a neural network, which is trained to approximate the mapping for precoding computation. Simulation results demonstrate that this method exhibits strong robustness, lower computational complexity, and high scalability in dynamic user/antenna scenarios compared to other approaches.

This paper studies the distributed precoding design for multi-satellite massive MIMO transmission. We first conduct a detailed analysis of the transceiver process, examining the effects of delay and Doppler compensation errors and emphasizing the nearly independent nature of inter-satellite interference. Based on the derived signal model, an approximate expected sum rate maximization problem is formulated, incorporating statistical channel state information and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation cannot hold equivalence in the considered scenario. To address this, we propose a modified WMMSE formulation leveraging channel covariance matrix decomposition. By exploiting channel characteristics, a low-complexity decomposition method is then developed, accompanied by an efficient algorithm. Simulation results validate the effectiveness and robustness of the proposed method in some practical simulated scenarios.

Integrating narrowband Internet of things (NB-IoT) into low earth orbit (LEO) satellite communications plays a promising role in serving massive devices and extensive coverage. In the receiver, the active user equipments (UEs) detection and timing synchronization in the random access procedure is essential but greatly challenged by the large frequency offset and long delay characterized by the LEO satellite. This paper presents the preamble allocation and receiver design of beam domain random access for NB-IoT integrated LEO satellite communications equipped with large-scale array antenna. Leveraging the inherent sparsity of the LEO satellite beam domain channel, we first design a UE grouping-based preamble allocation scheme to alleviate the collision by channel orthogonality for UEs simultaneously accessing. Building on the designed scheme, we propose a joint Doppler-beam domain active UE detection method that resorts to large frequency offset, which significantly improves the detection rate and obtains the coarse estimation of frequency offset. To achieve accurate timing synchronization, we formulate the timing offset estimation problem based on the maximum likelihood criterion. Following this, a modified and refined phase difference timing offset estimation algorithm is proposed, which employs the complete frequency hopping pattern of the preamble. To reduce the computational complexity, we propose an estimation algorithm possessing high performance with the prior information of estimated frequency offset. In comparison with the conventional method, the simulation demonstrates the superior performance of the proposed active UE detection and timing synchronization algorithms.

The unsourced random access (URA) has emerged as a viable scheme for supporting the massive machine-type communications (mMTC) in the sixth generation (6G) wireless networks. Notably, the tensor-based URA (TURA), with its inherent tensor structure, stands out by simultaneously enhancing performance and reducing computational complexity for the multi-user separation, especially in mMTC networks with a large number of active devices. However, current TURA scheme lacks the soft decoder, thus precluding the incorporation of existing advanced coding techniques. In order to fully explore the potential of the TURA, this paper investigates the Polar-coded TURA (PTURA) scheme and develops the corresponding iterative Bayesian receiver with feedback (IBR-FB). Specifically, in the IBR-FB, we propose the Grassmannian modulation-aided Bayesian tensor decomposition (GM-BTD) algorithm under the variational Bayesian learning (VBL) framework, which leverages the property of the Grassmannian modulation to facilitate the convergence of the VBL process, and has the ability to generate the required soft information without the knowledge of the number of active devices. Furthermore, based on the soft information produced by the GM-BTD, we design the soft Grassmannian demodulator in the IBR-FB. Extensive simulation results demonstrate that the proposed PTURA in conjunction with the IBR-FB surpasses the existing state-of-the-art unsourced random access scheme in terms of accuracy and computational complexity.

Deep learning has been widely applied to channel estimation (CE), yielding significant performance improvements. However, existing research primarily focuses on static channel scenarios, leading to substantial performance degradation in dynamic environments. Furthermore, the use of fixed pilot patterns fails to adequately capture channel dynamics, resulting in unnecessary pilot overhead. In this study, we propose a Vision Transformer-based joint pilot design (PD) and CE network (PD-CEViT) for orthogonal frequency division multiplexing (OFDM) systems. The PD module leverages maximum Doppler shift and delay spread information to determine pilot positions, effectively capturing channel variations in dynamic scenarios. To further improve CE accuracy and robustness across diverse environments, the coarse CE from the PD module is passed to a CE module that utilizes a Vision Transformer (ViT), forming the joint PD-CEViT structure. Additionally, we introduce a pilot number switch network, named SwitchPD-CEViT, which dynamically adjusts between different PD-CEViT configurations based on the current channel conditions. This strategy balances network performance and pilot overhead, accommodating varying pilot requirements across different scenarios. Simulation results demonstrate that our proposed structure more effectively tracks channel variations compared to fixed pilot patterns. Even under challenging conditions with large Doppler shifts and delay spreads, our method significantly outperforms traditional and deep learning approaches in terms of mean square error (MSE) performance. Moreover, the integration of channel information further enhances estimation performance and robustness. Meanwhile, SwitchPD-CEViT achieves superior CE performance with reduced pilot overhead by efficiently managing pilot utilization.

The increasing demands for spectral resources in emerging massive machine-type communication applications necessitate the implementation of massive grant-free transmission in the millimeter-wave (mmWave) band. This paper proposes two efficient receive analog beamforming design algorithms for mmWave massive grant-free transmission under hybrid beamforming architectures, intending to optimize spectral efficiency and access probability, respectively. Specifically, we first express the spectral efficiency of mmWave massive grant-free transmission systems and then derive an analytically tractable approximation using the random matrix theory. Following this, an alternating optimization method is employed to design the receive beamforming matrix efficiently. Additionally, we provide the formulation of access probability for mmWave massive grant-free transmission, whose explicit expression is approximately derived through the Gaussian approximation. Building upon this, we utilize a convex hull relaxation-based optimization method to optimize the beamforming matrix. The effectiveness of our proposed beamforming design algorithms in improving spectral efficiency and access probability is validated through extensive simulation experiments.

In massive multiple-input multiple-output (MIMO) systems, the downlink transmission performance heavily relies on accurate channel state information (CSI). Constrained by the transmitted power, user equipment always transmits sounding reference signals (SRSs) to the base station through frequency hopping, which will be leveraged to estimate uplink CSI and subsequently predict downlink CSI. This paper aims to investigate joint channel estimation and prediction (JCEP) for massive MIMO with frequency hopping sounding (FHS). Specifically, we present a multiple-subband (MS) delay-angle-Doppler (DAD) domain channel model with off-grid basis to tackle the energy leakage problem. Furthermore, we formulate the JCEP problem with FHS as a multiple measurement vector (MMV) problem, facilitating the sharing of common CSI across different subbands. To solve this problem, we propose an efficient Off-Grid-MS hybrid message passing (HMP) algorithm under the constrained Bethe free energy (BFE) framework. Aiming to address the lack of prior CSI in practical scenarios, the proposed algorithm can adaptively learn the hyper-parameters of the channel by minimizing the corresponding terms in the BFE expression. To alleviate the complexity of channel hyper-parameter learning, we leverage the approximations of the off-grid matrices to simplify the off-grid hyper-parameter estimation. Numerical results illustrate that the proposed algorithm can effectively mitigate the energy leakage issue and exploit the common CSI across different subbands, acquiring more accurate CSI compared to state-of-the-art counterparts.

In this paper, we propose a unified framework based on equivariance for the design of artificial intelligence (AI)-assisted technologies in multi-user multiple-input-multiple-output (MU-MIMO) systems. We first provide definitions of multidimensional equivariance, high-order equivariance, and multidimensional invariance (referred to collectively as tensor equivariance). On this basis, by investigating the design of precoding and user scheduling, which are key techniques in MU-MIMO systems, we delve deeper into revealing tensor equivariance of the mappings from channel information to optimal precoding tensors, precoding auxiliary tensors, and scheduling indicators, respectively. To model mappings with tensor equivariance, we propose a series of plug-and-play tensor equivariant neural network (TENN) modules, where the computation involving intricate parameter sharing patterns is transformed into concise tensor operations. Building upon TENN modules, we propose the unified tensor equivariance framework that can be applicable to various communication tasks, based on which we easily accomplish the design of corresponding AI-assisted precoding and user scheduling schemes. Simulation results show that the proposed methods achieve near-optimal performance with significantly lower complexity and strong generalization across multiple dimensions. For instance, the NN trained for precoding with 8 users provides satisfactory performance in a 10-user scenario. This validates the superiority of TENN modules and the unified framework.

This paper investigates the joint beam alignment and Doppler estimation (BADE) for fast time-varying wideband millimeter-wave channels, which is essential for subsequent data transmission. In such scenarios, the non-negligible Doppler frequencies significantly impact the beam alignment performance and reference signal overhead, calling for accurate time variation modeling and efficient transceiver design. Toward this end, we leverage the angle, Doppler frequency, and delay sparsity, thus formulating the joint BADE problem as a sparse signal recovery problem in the angle-Doppler-delay domain. The feasibility of the formulated problem strongly depends on the transmitter codebook and the receiver algorithm, which motivates our design. For the transmitter codebook, we characterize the design criterion aiming at maximal identifiable paths, which facilitates precise path parameter estimations and is not satisfied by existing deterministic codebooks. Following this, we provide a new deterministic codebook generation algorithm to meet the necessary conditions of the proposed criterion. For the receiver algorithm, we propose the greedy-based multi-path parameter extraction algorithm. In the proposed algorithm, the hierarchical refinement dictionaries with extended refinement range are employed, balancing the BADE performance and computational complexity. The numerical simulations demonstrate the superiority of the proposed transceiver over benchmarks on the joint BADE problem.

In this paper, we consider symbol-level precoding (SLP) in channel-coded multiuser multi-input single-output (MISO) systems. It is observed that the received SLP signals do not always follow Gaussian distribution, rendering the conventional soft demodulation with the Gaussian assumption unsuitable for the coded SLP systems. It, therefore, calls for novel soft demodulator designs for non-Gaussian distributed SLP signals with accurate log-likelihood ratio (LLR) calculation. To this end, we first investigate the non-Gaussian characteristics of both phase-shift keying (PSK) and quadrature amplitude modulation (QAM) received signals with existing SLP schemes and categorize the signals into two distinct types. The first type exhibits an approximate-Gaussian distribution with the outliers extending along the constructive interference region (CIR). In contrast, the second type follows some distribution that significantly deviates from the Gaussian distribution. To obtain accurate LLR, we propose the modified Gaussian soft demodulator and Gaussian mixture model (GMM)-expectation-maximization (EM) soft demodulators to deal with two types of signals respectively. Subsequently, to further reduce the computational complexity and pilot overhead, we put forward a novel neural network named pilot feature extraction network (PFEN) to replace the EM algorithm, leveraging the transformer mechanism in deep learning. Simulation results show that the proposed soft demodulators dramatically improve the throughput of existing SLPs for both PSK and QAM transmission in coded systems.

This paper investigates the robust design of symbol-level precoding (SLP) for multiuser multiple-input multiple-output (MIMO) downlink transmission with imperfect channel state information (CSI) caused by channel aging. By utilizing thea posteriorichannel model based on the widely adopted jointly correlated channel model, the imperfect CSI is modeled as the statistical CSI incorporating the channel mean and channel variance information with spatial correlation. With the signal model in the presence of channel aging, we formulate the signal-to-noise-plus-interference ratio (SINR) balancing and minimum mean square error (MMSE) problems for robust SLP design. The former targets to maximize the minimum SINR across users, while the latter minimizes the mean square error between the received signal and the target constellation point. When it comes to massive MIMO scenarios, the increment in the number of antennas poses a computational complexity challenge, limiting the deployment of SLP schemes. To address such a challenge, we simplify the objective function of the SINR balancing problem and further derive a closed-form SLP scheme. Besides, by approximating the matrix involved in the computation, we modify the proposed algorithm and develop an MMSE-based SLP scheme with lower computation complexity. Simulation results confirm the superiority of the proposed schemes over the state-of-the-art SLP schemes.

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.

This paper investigates the robust design of symbollevel precoding (SLP) for multiuser multiple-input multipleoutput (MIMO) downlink transmission with imperfect channel state information (CSI) caused by channel aging. By utilizing the a posteriori channel model based on the widely adopted jointly correlated channel model, the imperfect CSI is modeled as the statistical CSI incorporating the channel mean and channel variance information with spatial correlation. With the signal model in the presence of channel aging, we formulate the signal-to-noise-plus-interference ratio (SINR) balancing problem for robust SLP design, which targets to maximize the minimum SINR and can be transformed into a typical max-min fractional programming (MMFP). In the scenario of massive MIMO, we simplify the objective function of the SINR balancing problem and further derive a low-complexity SLP scheme. Simulation results confirm the superiority of the proposed schemes over the state-of-the-art SLP schemes.

This paper focuses on energy efficiency (EE) pre-coding design and computational-efficient precoding updating strategy for low earth orbit (LEO) satellite communications. Firstly, we formulate the EE precoding problem, which aims to maximize the EE metric under the quality of service (QoS) constraint and per-antenna power constraint (PAPC). By intro-ducing semidefinite relaxation, first-order Taylor approximation, and quadratic transformation, the problem is transferred into a convex one that can be efficiently solved. Moreover, due to the continuous movement of LEO satellites, precoding is performed frequently to maintain the high EE performance, leading to high computational complexity. Consequently, we consider prolonging precoding intervals to reduce complexity while alleviating severe performance degradation during the intervals. To this end, a computational-efficient beam direction change (BDC) algorithm is proposed to update pre coding vectors, which makes the main lobes of beams always point toward users. Furthermore, an adaptive method is proposed to adjust the precoding interval flexibly. Simulation results have indicated the effectiveness of the EE precoding algorithm and the BDC algorithm.

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.

Abstract With the increasing number of user terminals (UTs), the interference among UTs might significantly decrease the throughput of the low earth orbit satellite communication system. In this paper, the user scheduling method is investigated to suppress user interference. Specifically, leveraging the strong spatial directivity of satellite channels, a low‐complexity angle‐based orthogonal user selection (AOUS) algorithm is proposed, which selects UTs with nearly orthogonal channels via angle information of UTs. A rate‐based proportionally fair (PF)‐AOUS algorithm is further proposed to ensure fairness among UTs, which combines the AOUS with the PF criterion. To reduce complexity, an improved angle‐based PF‐AOUS algorithm that schedules UTs according to their pitch angles rather than their rates is proposed. In addition, efficient precoding schemes for orthogonal UTs are designed by combining the steering vector and power allocation matrix, and it is shown that precoding can be converted into power allocation problems that further balance fairness and throughput. The numerical results indicate that the AOUS achieves a near‐optimal sum rate performance, and the angle‐based PF‐AOUS has the similar performance to the rate‐based PF‐AOUS, which achieves a high fairness index with the proposed precoding scheme.

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.

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.

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.

To meet the escalating demand on spectral resource in massive machine-type communication (mMTC) applications, a critical solution is applying massive grant-free transmission to the millimeter-wave (mmWave) band. In this paper, to maximize the ergodic rate, we propose an efficient hybrid analog/digital beamforming (HBF) design algorithm for the massive grant-free transmission in uplink mmWave systems. Specifically, to make the HBF design problem tractable, we first leverage the deterministic equivalent method to derive an approximate expression of the ergodic rate for the mMTC in the mmWave system. Since the ergodic rate maximization-based HBF design problem is nonconvex, we leverage the alternating optimization strategy and propose a semidefinite relaxation-based HBF algorithm to improve the ergodic rate. Simulation results verify the superior performance of the proposed HBF design algorithm in improving the ergodic rate.

In massive grant-free transmission, joint user activity detection (UAD) and channel estimation (CE) is essential for data recovery at the receiver, which has been extensively researched in frequency-flat fading scenarios. However, in practical orthogonal frequency division multiplexing (OFDM)-based systems, frequency-selective fading leads to a significant increase in the number of channel coefficients to be estimated, imposing new challenges for the design of joint UAD and CE algorithms. Therefore, this paper investigates joint UAD and CE for OFDM-based massive grant-free transmission over frequency-selective fading channels. Firstly, the discrete cosine transform (DCT) is employed to reformulate the compressed sensing (CS) problem with the reduced dimension of the DCT domain channel response vector. Then, we develop a hybrid message passing (HMP) algorithm under the framework of the constrained Bethe free energy (BFE) minimization to achieve efficient joint UAD and CE. Numerical results confirm the superior joint estimation performance of the proposed algorithm over frequency-selective fading channels.

This paper studies low-complexity robust beamforming in mobile massive multiple-input multiple-output (MIMO) wireless communication systems, which introduces a robustness factor and deep learning (DL)-based framework to mitigate the impact of imperfect channel state information (CSI). By incorporating the estimation uncertainty, we aim to design transceivers to minimize outage probability subject to a total transmit power constraint. However, due to the probabilistic constraints, the optimization problem is difficult to solve. Therefore, we introduce a robustness factor to the quality of service (QoS) constraints by maximizing a zero-outage region, based on an extension of the offset maximization method. Then, we convert the problem into a convex problem and get a quasi-closed-form solution. Besides, iterating the fixed point equation of the Lagrange multipliers in the inequality optimization introduces enormous computational complexity. To this end, we present a novel DL-based algorithm to predict the multipliers directly from imperfect CSI, which includes a column convolutional layer elaborately designed for channel input. Comprehensive experimental comparisons demonstrate the proposed robust transceiver can improve the outage probability significantly over conventional baselines, and the DL-based algorithm can further reduce the running time.

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.

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.

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.

In massive grant-free transmission, joint user activity detection (UAD) and channel estimation (CE) is essential for data recovery at the receiver, which has been extensively researched in frequency-flat fading scenarios. However, in practical orthogonal frequency division multiplexing (OFDM)-based systems, frequency-selective fading (FSF) leads to a significant increase in the number of channel coefficients to be estimated, imposing new challenges for the design of joint UAD and CE algorithms. Therefore, this paper investigates joint UAD and CE for OFDM-based massive grant-free transmission over FSF channels. Firstly, by employing the discrete cosine transform (DCT), the joint estimation problem is formulated as the compressed sensing (CS) problem with the reduced dimension of the DCT-domain channel response vector. Then, based on the low-dimension sparse channel model, we develop a hybrid message passing (HMP) algorithm under the constrained Bethe free energy (BFE) minimization framework to achieve efficient joint UAD and CE. To deal with the lack of the DCT-domain prior information in practical scenarios, we parameterize it as the Cauchy distribution or the Laplacian distribution and learn their parameters by the proposed HMP algorithm. Numerical results confirm the superior joint UAD and CE performance of the proposed algorithm over FSF channels.

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.

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.

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.

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 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.

Non-orthogonal multiple access (NOMA) on shared resources has been identified as a promising technology in 5G to improve resource efficiency and support massive access in all kinds of transmission modes. Power domain and code domain NOMA have been extensively studied and evaluated in both literatures and 3GPP standardization, especially for the uplink where large number of users would like to send their messages to the base station. Though different in the transmitter side design, power domain NOMA and code domain NOMA share the same need of the advanced multi-user detection (MUD) design at the receiver side. Various multi-user detection algorithms have been proposed, balancing performance and complexity in different ways, which is important for the implementation of NOMA in practical networks. In this paper, we introduce a unified variational inference (VI) perspective on various universal NOMA MUD algorithms such as belief propagation (BP), expectation propagation (EP), vector EP (VEP), approximate message passing (AMP) and vector AMP (VAMP), demonstrating how they could be derived from and adapted to each other within the VI framework. Moreover, we unveil and prove that conventional elementary signal estimator (ESE) and linear minimum mean square error (LMMSE) receivers are special cases of EP and VEP, respectively, thus bridging the gap between classic linear receivers and message passing based nonlinear receivers. Such a unified perspective would not only help the design and adaptation of NOMA receivers, but also open a door for the systematic design of joint active user detection and multi-user decoding for sporadic grant-free transmission.

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 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 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, 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.

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.

We consider a single-cell massive multi-input multi-output (MIMO) network with uniform planar array (UPA) antennas equipped at the base station that serves a number of single-antenna users. In the overloaded multi-user setting, it is likely that users' channels are highly spatial-correlated with overlapping spectrum in the angular domain, which imposes challenges on uplink channel estimation and data transmission due to potential pilot contamination during uplink training and multiuser interference during uplink data transmission. To mitigate the effect of multiuser channel spatial correlation, we adopt a recently proposed active channel sparsification strategy, and propose a novel method for joint user and beam selection in the angular domain. In particular, we represent all users' channels in the angular/beam domain, taking advantage of the doubly block Toeplitz structure of the channel covariance matrix for UPA. Accordingly, we construct a weighted bipartite graph to represent the beam and user association for ease of user/beam selection. By doing so, we reformulate the problems of mean square error minimization for uplink channel estimation and sum rate maximization for uplink data detection as two mixed integer linear programs (MILPs), by which the challenging joint user and beam selection problem can be efficiently solved via off-the-shelf MILP solvers. The simulation results demonstrate the effectiveness of our active channel sparsification strategy for the joint user and beam selection.

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 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.

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.

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.

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 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.

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.

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.

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.

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 recent years, research on closed-loop STCs show that with the help of feedback, the achievable rate and/or the diversity order can further be improved in space-time coded systems. In this paper, we study on the optimal closed-loop STCs with low-complexity receivers and any amount of feedback. A unified framework for designing closed-loop STCs is firstly proposed by extending the conventional linear dispersion codes to the cases with feedback. Then a new design method is proposed, where the stochastic gradient descent algorithm is employed to obtain optimal set of LDC candidates particularly for spacetime coded systems with linear minimum mean square error (MMSE) receiver. The proposed method allows for flexible system parameters such as the number of transmit/receive antennas, modulated symbols and the length of codewords thus include most of the existing closed-loop STCs. Simulation results confirm the advantages of the newly-proposed design method of closed-loop STCs.

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 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 presents a new design method for delay-tolerant linear dispersion codes (DT-LDCs) for asynchronous cooperative communication networks. We consider a system that is equipped with linear minimum mean square error (MMSE) receiver. Based on the DT-LDC framework, we propose a new design method to yield DT-LDCs that approach near-optimal capacity as well as minimum average MSE. The proposed design employs stochastic gradient algorithm to guarantee the local optimum. Moreover, it is improved by using simulated annealing type optimization to approach the global optimum. Simulation results confirm the performance of the newly-proposed delay-tolerant LDCs.

In cooperative communication networks, owing to the nodes' arbitrary geographical locations and individual oscillators, the system is fundamentally asynchronous. This will damage some of the key properties of the space-time codes and can lead to substantial performance degradation. In this paper, we study the design of linear dispersion codes (LDCs) for such asynchronous cooperative communication networks. Firstly, the concept of conventional LDCs is extended to the delay-tolerant version and new design criteria are discussed. Then we propose a new design method to yield delay-tolerant LDCs that reach the optimal Jensen's upper bound on ergodic capacity as well as minimum average pairwise error probability. The proposed design employs stochastic gradient algorithm to approach a local optimum. Moreover, it is improved by using simulated annealing type optimization to increase the likelihood of the global optimum. The proposed method allows for flexible number of nodes, receive antennas, modulated symbols and flexible length of codewords. Simulation results confirm the performance of the newly-proposed delay-tolerant LDCs.

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.

In this paper, we study the design of linear dispersion codes (LDCs) for asynchronous cooperative communication networks. Firstly, the concept of conventional LDCs is extended to the delay-tolerant version and new design criteria are discussed. Then we propose a new design method to yield delay-tolerant LDCs that approach near-optimal capacity as well as minimum average pairwise error probability. The proposed design employs stochastic gradient algorithm to guarantee the local optimum. Moreover, it is improved by using simulated annealing type optimization to approach the global optimum. The proposed method allows flexible number of nodes, receive antennas, the length of codewords and the number of modulated symbols. Simulation results confirm the performance of the newly-proposed delay-tolerant LDCs.

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.

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.

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.

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.

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 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 paper, we propose maximum a posteriori probability (MAP) based equalizers for orthogonal frequency division multiplexing (OFDM) systems over time-varying multipath channels. Under the approximation of linear variation in an OFDM symbol, it is easy to obtain the equivalent channel matrix. Based on the Tailbiting trellis structure and the fact that intercarrier interference (ICI) energy is concentrated in adjacent subcarriers, a new Tailbiting BCJR equalizer, operating in the frequency domain, is developed. Furthermore, a Tailbiting DF- BCJR algorithm is proposed to achieve better performance, and an MBCJR algorithm is also introduced to reduce the implementation complexity. Simulation results indicate that, by successfully taking advantage of the diversity gain caused by time-selective channels, our equalizers demonstrate good performance in fast fading environments.