The asymmetrical uplink and downlink transceiver architecture has emerged as a promising solution to reduce hardware cost and complexity in massive multiple-input multiple-output (MIMO) systems, especially under scenarios with dense antenna deployments, such as uniform planar arrays (UPAs). However, accurate full-dimensional channel state information (CSI) recovery becomes more challenging than uniform linear array (ULA) scenarios due to the significantly reduced number of radio frequency (RF) chains. Directly extending the ULA channel recovery method into UPAs will not only result in high computational complexity but also introduce angle estimation ambiguity owing to the extra vertical array dimension. To address these challenges, we propose a channel recovery framework for UPA-assisted massive MIMO systems with asymmetrical transceiver architectures. First, we introduce the concept of the mixed angle to deal with the low elevation angular resolution originating from the compact array form, and a virtual array is then constructed based on the mixed angle via the spatial correlation matrix. After that, an antenna selection algorithm is designed to maximize the virtual array aperture with a minimal number of RF chains, and a low-complexity UPA-based modified newtonized orthogonal matching pursuit (UPA-based mNOMP) channel recovery algorithm is developed to enable accurate full-dimensional CSI reconstruction. Finally, the imperfect spatial correlation matrix is considered and a orthogonal rank-one matrix pursuit-based spatial correlation matrix recovery algorithm is proposed to recover the spatial correlation matrix from its spatial sparse measurements by exploiting the low-rank property of massive MIMO channels. Simulation results validate the superiority of the proposed algorithms in achieving excellent full-dimensional channel recovery performance for asymmetrical transceiver-based massive MIMO systems with UPAs.

Multi-user millimeter-wave communication relies on narrow beams and dense cell deployments to ensure reliable connectivity. However, tracking optimal beams for multiple mobile users across multiple base stations (BSs) results in significant signaling overhead. Recent works have explored the capability of out-of-band (OOB) modalities in obtaining spatial characteristics of wireless channels and reducing pilot overhead in single-BS single-user/multi-user systems. However, applying OOB modalities for multi-BS selection towards dense cell deployments leads to high coordination overhead, i.e, excessive computing overhead and high latency in data exchange. How to leverage OOB modalities to eliminate pilot overhead and achieve efficient multi-BS coordination in multi-BS systems remains largely unexplored. In this paper, we propose a novel OOB modality synergy (OMS) based mobility management scheme to realize multi-user beam prediction and proactive BS selection by synergizing two OOB modalities, i.e., vision and location. Specifically, mobile users are initially identified via spatial alignment of visual sensing and location feedback, and then tracked according to the temporal correlation in image sequence. Subsequently, a binary encoding map based gain and beam prediction network (BEM-GBPN) is designed to predict beamforming gains and optimal beams for mobile users at each BS, such that a central unit can control the BSs to perform user handoff and beam switching. Simulation results indicate that the proposed OMS-based mobility management scheme enhances beam prediction and BS selection accuracy and enables users to achieve 91% transmission rates of the optimal with zero pilot overhead and significantly improve multi-BS coordination efficiency compared to existing methods.

Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has recently been introduced to enable advanced control over electromagnetic waves to further increase the benefits of traditional RIS in enhancing signal quality and improving spectral and energy efficiency for next-generation wireless networks. A significant issue in designing and deploying BD-RIS is the tradeoff between its performance and circuit complexity. While existing studies have explored optimal architectures to minimize circuit complexity in ideal BD-RIS, architecture discovery for non-ideal BD-RIS remains uninvestigated. Consequently, how non-idealities and circuit complexity jointly affect the performance of BD-RIS remains unclear, making it difficult to achieve the performance-circuit complexity tradeoff in the presence of non-idealities. Essentially, architecture discovery for non-ideal BD-RIS faces challenges from both the computational complexity of global architecture search and the difficulty in achieving global optima. To tackle these challenges, we propose a learning-based two-tier architecture discovery framework (LTTADF) consisting of an architecture generator and a performance optimizer to jointly discover optimal architectures for non-ideal BD-RIS given specific circuit complexities, which can effectively explore over a large architecture space while avoiding getting trapped in poor local optima and thus achieving near-optimal solutions for the performance optimization. Numerical results provide valuable insights for deploying non-ideal BD-RIS considering the performance-circuit complexity tradeoff.

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.

Ultra-reliable low-latency communication is the key technology for smart factories and autonomous vehicles. However, traditional beam training approaches in millimeter-wave communications generally cause significant latency and communication overhead, especially in the case of multi-user communications. To tackle this problem, we propose a novel Vision-aided Multi-user Beam Tracking (VA-MUBT) framework for mmWave massive MIMO system, which leverages deep learning based visual object detection and multiple objects tracking algorithm to enable fast beam tracking of multi-user. In addition, a prototype is constructed to evaluate the proposed VA-MUBT framework and the experimental results based on this prototype show that the accuracy of 3-time beam search can reach near 90% with only 8% overhead of the exhaustive beam search method. Hence, the proposed VA-MUBT demonstrates the superiority in achieving fast multi-user beam tracking and significantly reducing the communication overhead.

This article examines the application of hybrid automatic repeat request with incremental redundancy (HARQ-IR) to reliable mission-critical Internet of Things (IoT) communications, which frequently use short packets to meet low latency of mission. We first analyze the average block error rate (BLER) of HARQ-IR-aided short packet communications. The finite-blocklength information theory and the correlated decoding events preclude the analysis of BLER. To overcome the issue, the recursive formulation of the average BLER motivates us to calculate its value through trapezoidal approximation and Gauss-Laguerre quadrature. Besides, dynamic programming is applied to implement Gauss-Laguerre quadrature to avoid redundant calculations. Moreover, the asymptotic analysis is performed to derive a simple expression for the asymptotic average BLER at high-signal-to-noise ratio (SNR). Then, we study the maximization of long-term average throughput (LTAT) via power allocation meanwhile ensuring power and BLER constraints. To tackle the fractional and nonconvex problem, the asymptotic BLER is employed to convert the original problem into a convex one through geometric programming (GP). Unfortunately, since there is a large approximation error at low SNR, the GP-based solution underestimates the LTAT performance in the circumstance. Alternatively, we develop a deep reinforcement learning (DRL)-based framework to learn the optimal power allocation policy. In particular, the optimization problem is transformed into a constrained Markov decision process problem, which is solved by integrating deep deterministic policy gradient(DDPG) and subgradient method. The numerical results demonstrate that the DRL-based method outperforms the GP-based one at low SNR, albeit at the cost of increasing computational burden.

To reap the promising benefits of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) is required through channel estimation. However, due to the complicated wireless propagation environment and large-scale antenna arrays, precise channel estimation for massive MIMO systems is significantly challenging and costs an enormous training overhead. Considerable time-frequency resources are consumed to acquire sufficient accuracy of CSI, which thus severely degrades systems’ spectral and energy efficiencies. In this paper, we propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods under various pilot-density settings and signal-to-noise ratios. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method.

Accurate channel state information (CSI) is essential for downlink precoding in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems with orthogonal frequency-division multiplexing (OFDM). However, obtaining CSI through feedback from the user equipment (UE) becomes challenging with the increasing scale of antennas and subcarriers and leads to extremely high CSI feedback overhead. Deep learning-based methods have emerged for compressing CSI but these methods generally require substantial collected samples and thus pose practical challenges. Moreover, existing deep learning methods also suffer from dramatically growing feedback overhead owing to their focus on full-dimensional CSI feedback. To address these issues, we propose a low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems. An incorporation-extrapolation scheme for eigenvector-based CSI feedback is proposed to reduce the feedback overhead. Then, to alleviate the necessity of extensive collected samples and enable few-shot CSI feedback, we further propose a knowledge-driven data augmentation (KDDA) method and an artificial intelligence-generated content (AIGC) -based data augmentation method by exploiting the domain knowledge of wireless channels and by exploiting a novel generative model, respectively. Experimental results based on the DeepMIMO dataset demonstrate that the proposed IEFSF significantly reduces CSI feedback overhead by 64 times compared with existing methods while maintaining higher feedback accuracy using only several hundred collected samples.

In this article, we present a hybrid channel estimation algorithm for uniform planar array (UPA)-assisted millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems by exploiting the benefits from both the compressed sensing (CS) and the sparse Bayesian learning (SBL). Compared with existing studies, the distribution characteristics and correlations between propagation paths in the elevation (e)- and azimuth (a)-angle domains are considered to enhance the estimation performance. Specifically, we first redefine the e-angles and the a-angles to simplify the system model. Then, a novel autoregressive (AR)-Gaussian channel prior is proposed to capture both the sparsity and the clustering properties of mmWave massive MIMO IoT channels. After that, we provide a channel approximation method to overcome the channel uncertainty by exploiting the structure of the AR-Gaussian channel prior. The hybrid beamforming (HBF) architecture with limited radio-frequency (RF) chains in mmWave IoT systems is also considered. Finally, we propose a hybrid channel estimation algorithm, which consists of two stages. Based on the different distribution characteristics in different angle domains, the CS-based channel estimation is performed for e-angles on stage one, while the SBL-based channel estimation is applied for a-angles on stage two. Numerical results reveal that compared with the existing CS- and SBL-only methods, the proposed hybrid channel estimation algorithm exhibits better performance in terms of computational complexity, sparsity robustness, and estimation accuracy.