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

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

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

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

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