This paper investigates a multi-user pinching-antenna (PA) system, where a single PA is activated on each waveguide. With the maximum ratio transmission (MRT) beamforming, the system spectral efficiency (SE) is studied, where the inter-user interference term complicates the analysis of the SE. To overcome this obstacle, the stationary phase point method (SPPM) is applied to obtain an analytically tractable form of the SE. The analysis reveals that the average inter-user interference can be negligible with a large waveguide spacing even using the MRT. This insight makes the simple MRT appealing for PA-based multi-user communications. Finally, the theoretical analysis is verified through simulations. Our numerical results confirm that 1) with the aid of SPPM, the approximation of the system SE is accurate; 2) and while increasing the waveguide spacing helps reduce the average inter-user interference, it might degrade the SE due to the increased signal propagation path loss.

As a novel antenna array architecture, continuous aperture arrays (CAPAs) have garnered wide attention in recent years. While existing CAPA-based research has predominantly addressed unicast scenarios, the critical area of multicast beamforming remains unexplored. In this paper, a CAPA-based multi-group multicast communication system is investigated. An integral-based CAPA multi-group multicast beamforming design is formulated for the maximization of the system energy efficiency (EE), subject to a minimum multicast SE constraint of each user group and a total transmit power constraint. To address this non-convex fractional programming problem, we employ Dinkelbach’s method, such that the non-convex group-wise multicast spectral efficiency (SE) constraint is first equivalently transformed into a tractable form using auxiliary variables. Then, an efficient block coordinate descent (BCD)-based algorithm is developed to solve the reformulated problem. The CAPA beamforming design subproblem can be optimally solved via the Lagrangian dual method and the calculus of variations (CoV) theory. It reveals that the optimized CAPA beamformer should be a combination of all the groups’ user channels. To further reduce the computational complexity, a low-complexity zero-forcing (ZF)-based approach is proposed. The closed-form ZF CAPA beamformer is derived using each group’s most representative user channel to mitigate the inter-group interference while ensuring the intra-group multicast performance. Then, the beamforming design subproblem in the BCD-based algorithm becomes a convex power allocation subproblem, which can be efficiently solved. Numerical results demonstrate that 1) the CAPA can significantly improve the EE compared to conventional spatially discrete arrays (SPDAs); 2) due to the enhanced spatial resolutions, increasing the aperture size of CAPA is not always beneficial for EE enhancement in multicast scenarios; and 3) wider user distributions of each group cause a significant EE degradation of CAPA compared to SPDA.

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

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

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

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

Device-edge collaborative inference, which deploys well-trained artificial intelligence (AI) models at the network edge via the cooperation of edge devices and edge servers, emerges as a promising technique to provide ubiquitous intelligent services. In this paper, a multiple-input multiple-output (MIMO) over-the-air computation (AirComp) scheme is proposed for the efficient implementation of device-edge collaborative inference. In the considered system, the technique of MIMO AirComp is utilized to aggregate local feature vectors, extracted from noise-corrupted sensory data on devices, at the server to efficiently derive a denoised global one for completing the downstream inference task. Device-edge collaborative inference features a task-oriented property, that concerns the effectiveness and efficiency of the task execution. In this case, the traditional AirComp criterion, i.e., minimum mean square error (MMSE), is not effective, since the same distortion level on different feature elements may have different influences on the inference performance. To this end, this paper directly adopts inference accuracy as the design objective. As the instantaneous inference accuracy is unknown during the design stage, an approximated but tractable metric, called discriminant gain, which measures the discernibility of different classes, is adopted. To maximize the inference accuracy measured by discriminant gain, a MIMO AirComp technique is proposed to jointly optimize all feature elements. The problem is nonconvex because of the complicated form of the objective function and the constraints. The solution based on semidefinite relaxation (SDR) and successive convex approximation (SCA) is employed to design a joint transmit precoding and receive beamforming scheme. Besides, to enhance the robustness of practical AI models in the inference stage, a post-processing design of feature magnitude normalization is proposed. Extensive experiments are conducted based on a practical human motion recognition task, which verifies our theoretical analysis and the superiority of our proposed scheme.

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

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

A continuous aperture array (CAPA)-based multicast communication system is investigated in this paper, where a base station (BS) employs a CAPA to serve a set of multicast users. Under a transmit power constraint, the problem of maximizing the system multicast spectral efficiency (SE) by designing the CAPA beamformer is formulated, where the involved beamformer is a continuous current density function across the CAPA surface. By introducing auxiliary variables, the non-convex multicast SE objective function is first transformed into a tractable form. Then, to address the reformulated problem, an efficient block coordinate descent (BCD)-based algorithm is developed. The CAPA beamforming design subproblem can be optimally solved via the Lagrangian dual method and the calculus of variations (CoV) theory. Numerical results demonstrate that the considered CAPA can significantly improve the multicast SE compared to a conventional spatially discrete array (SPDA).

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

This paper investigates near-field multi-user downlink communications over Ricean fading channels underpinned by the holographic multiple-input multiple-output (HMIMO) technology. We first establish the mutual coupling and radiation efficiency models to characterize the effect of mutual coupling and then formulate the practical input-output relationship. Based on this, the achievable spectral efficiency (SE) is derived for maximum ratio transmission (MRT). By further investigating the special cases of pure line-of-sight (LoS) and Rayleigh fading, our analysis reveals that for a moderate number of antenna elements, the system's SE with mutual coupling might outperform that without mutual coupling, especially in the low transmit power regime. Moreover, the additional distance degrees-of-freedom (DoF) introduced by the near-filed channel can enable the inter-user interference mitigation, even for the worst case when the users have similar angular directions. Finally, the obtained theoretical analysis is validated through simulations.

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

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

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

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

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.

The core idea of holographic MIMO (HMIMO) is to densely deploy numerous antenna elements within a given aperture size. However, with the denser distribution of antenna elements, stronger mutual coupling effects would kick in among antenna elements, which would eventually affect the communication performance. Meanwhile, as the holographic array usually has large physical size, the possibility of near-field communication increases. This paper investigates a near-field multi-user downlink HMIMO system and characterizes the spectral efficiency (SE) under the mutual coupling effect over Ricean fading channels. Both perfect and imperfect channel state information (CSI) scenarios are considered. (i) For the perfect CSI case, the mutual coupling and radiation efficiency model are first established. Then, a closed-form SE expression is derived under maximum ratio transmission (MRT). By comparing the SE between the cases with and without mutual coupling, it is unveiled that the system SE with mutual coupling might outperform that without mutual coupling in the low transmit power regime for a given aperture size. Moreover, it is also unveiled that the inter-user interference cannot be eliminated unless the physical size of the array increases to infinity. Fortunately, the additional distance term in the near-field channel can be exploited for the inter-user interference mitigation, especially for the worst case, where the users’ angular positions overlap to a great extent. (ii) For the imperfect CSI case, the channel estimation error is considered for the derivation of the closed-form SE under MRT. It shows that in the low transmit power regime, the system SE can be enhanced by increasing the pilot power and the antenna element density, the latter of which will lead to severe mutual coupling. In the high transmit power regime, increasing the pilot power has a limited effect on improving the system SE. However, increasing the antenna element density remains highly beneficial for enhancing the system SE. Finally, both analytical and simulation results confirm that reducing the antenna spacing will be accompanied by significant mutual coupling effects, which may potentially enhance the system SE. However, this enhancement is ultimately limited by the radiation efficiency of the antennas and the physical size of the array.

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

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

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

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.

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

This paper investigates the capacity performance of an uplink fluid antenna system (FAS), in which the base station (BS) is equipped with multiple fluid antennas and each user has a single fluid antenna. We aim to maximize the capacity of the system by optimizing the transmit power, and the user and BS antenna positions. Beginning with simple cases where the number of paths or the number of BS antennas is small, we reveal that the capacity is independent of the antenna positions. Then we give an upper bound on the capacity for the case where the BS has a single fluid antenna. After that, we show that in the optimal case, all users should transmit at the maximum power. Moreover, we propose an alternative algorithm to iteratively optimize the antenna positions at the BS and user sides. When keeping the user antenna positions fixed, the BS antenna positions are updated alternatively using a discrete exhaustive search in the single-user case. By transforming the capacity maximization problem into a difference-of-convex (DC) form, the majorization-minimization (MM) algorithm can also be applied to jointly optimize the BS antenna positions when there is a single user in the system. For the multiuser scenario, the antenna positions at the BS side are optimized utilizing the gradient descent method. We show that the user antenna positions can also be optimized using the discrete exhaustive search or the MM algorithm. Simulation results show that FAS can greatly improve the system capacity compared to traditional fixed-position antenna systems.

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

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

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

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

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

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

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

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.

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

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 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.

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

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.

This article proposes a framework of cooperative multistation secure transmission in high-frequency skywave communications for wide-area Internet of Things applications by simultaneously exploiting the benefits of massive multiple-input multiple-output and coordinated multiple points communications. In this framework, the original message sent to the user is divided into several submessages by the core network, each of which will be transmitted to the user by the base stations (BSs). A joint optimization problem is established to maximize the user rate by optimally utilizing multiple BSs with multiple antennas for cooperative precoding. Semidefinite programming is introduced for the precoding vector design for the partial channel state information scenario. To solve the mixed-integer optimization problem of selecting BSs, a low complexity algorithm is proposed. Simulation results show that the performance of the proposed algorithm approaches that of exhaustive search, which demonstrates the effectiveness of the proposed algorithm.

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.

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.

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

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

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

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.

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.

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

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.

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.

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 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.

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.

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.

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

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

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

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

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

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

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

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

We investigate 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.

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.

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

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

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

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

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

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