Considering a multi-user Downlink scenario, this paper studies transmit antenna selection (AS) for uniform rectangular arrays (URA). A key contribution of this work is the investigation of the trade-off between AS and the array directivity (or gain), and its impact on spectral efficiency (SE) and energy efficiency (EE). In this context, to improve EE of the system, a directivity-aware transmit AS and power allocation problem is formulated and solved using a sequential approach. For AS, two schemes are proposed: Block selection (BS) and L´evy flight based binary particle swarm optimization (LFBPSO), while for power allocation, an iterative quadratic transform method is employed in combination with Dinkelbach algorithm for EE maximization. The performance of the AS schemes is evaluated with respect to SE, EE and computational complexity. The results show that the array directivity varies with the subset of selected antennas and has a direct impact on the performance of the AS schemes in terms of achievable SE and EE. Furthermore, AS without considering the directivity can incur a loss in the array gain of up to 2 dB in a λ/2 spaced array. The results also show that LFBPSO outperforms other AS schemes with respect to SE and EE. Finally, another important contribution of this work is the experimental validation of the impact of array directivity on the AS process. This is demonstrated by replacing the isotropic antenna array with a measured 4×8 uniform rectangular array of patch antennas in the simulation framework.

This paper investigates the block error rate (BLER) of three encoding schemes: joint encoding (JE), separate encoding (SE), and rate splitting (RS) in low-throughput massive multiple input and multiple output (mMIMO) networks with finite blocklength transmission. To reduce pilot overhead, we first partition users via k-means clustering based on their spatial coordinates, by which the pilot sequences can be reused among different groups. Then, we apply a minimum mean square error (MMSE) estimator to examine their instantaneous signal-to-interference-plus-noise ratios (SINRs). In particular, a local successful interference cancellation is proposed to mitigate co-channel interference for the RS scheme. Then, we formulate min-max problems for optimizing the error performance, with power control coefficients, combining vectors, and blocklength being their variables. Additionally, we employ the iterative MMSE combiner as the combining vector and then transform the problem for minimizing the BLER to a max-min problem maximizing the SINR at each iteration based on the monotonicity of the BLER expression with respect to the SINR. Power control coefficients are then updated by solving the reformulated geographic programming (GP) problem. Furthermore, the golden-section and bisection methods are applied to optimize the blocklength allocation strategy for the SE scheme and to search for the achievable BLER for the RS scheme. Simulation results, using network availability as the performance metric, demonstrate the comparable performance of the JE and RS schemes when combined with the proposed solving methods, whereas the SE scheme exhibits moderate performance.

This paper investigates point-to-point uplink rate-splitting (RS) transmission in the finite blocklength (FBL) regime. The system is configured with a dual-polarized (DP) multiple-input and multiple-output (MIMO) antenna array at the receiver and a pair of DP antennas at the transmitter. During transmission, vertically and horizontally polarized antennas at the transmitter are employed to transmit two independent data streams partitioned via RS. At the receiver, a successive interference cancellation (SIC)-free detection scheme is adopted to exploit the polarization-domain degree of freedom (DoF), thereby eliminating the need for SIC. To validate the efficacy of the proposed RS-DP scheme, we optimize the combining vectors for both vertical and horizontal data streams to maximize the sum spectral efficiency under FBL constraints. Simulation results demonstrate that the RS-DP scheme achieves performance comparable to the conventional RS-SIC approach, thereby corroborating its effectiveness in balancing system performance and decoding complexity.

In this paper, we focus on the energy efficiency (EE) optimization and analysis of reconfigurable intelligent surface (RIS)-assisted multiuser downlink near-field communications. Specifically, we conduct a comprehensive study on several key factors affecting EE performance, including the number of RIS elements, the types of reconfigurable elements, reconfiguration resolutions, and the maximum transmit power. To accurately capture the power characteristics of RISs, we adopt more practical power consumption models for three commonly used reconfigurable elements in RISs: PIN diodes, varactor diodes, and radio frequency (RF) switches. These different elements may result in RIS systems exhibiting significantly different energy efficiencies (EEs), even when their spectral efficiencies (SEs) are similar. Considering discrete phases implemented at most RISs in practice, which makes their optimization NP-hard, we develop a nested alternating optimization framework to maximize EE, consisting of an outer integer-based optimization for discrete RIS phase reconfigurations and a nested non-convex optimization for continuous transmit power allocation within each iteration. Extensive comparisons with multiple benchmark schemes validate the effectiveness and efficiency of the proposed framework. Furthermore, based on the proposed optimization method, we analyze the EE performance of RISs across different key factors and identify the optimal RIS architecture yielding the highest EE.

Self-supervised contrastive learning (SCL) has driven significant advancements in time-series representation learning. While recent studies built upon the information noise contrastive estimation (InfoNCE) loss framework focus on constructing appropriate positives and negatives, we theoretically analyze and identify two overlooked issues inherent in this approach: false negatives and class imbalance. To address these challenges, we propose a simple yet effective modification based on the SimCLR framework, integrating a multi-instance discrimination task to mitigate false negatives. Additionally, we introduce a graph-based interactive projection head and semantic consistency regularization, which enhances minority-class representations with minimal annotation cost. Extensive experiments on six real-world time-series datasets demonstrate that our approach consistently outperforms state-of-the-art methods, achieving up to 3.96% higher accuracy and 10.73% improvement in $F1$ -score, particularly benefiting imbalanced data scenarios.

This paper investigates the uplink power control scheme for massive multiple-input and multiple-output non-orthogonal multiple access (mMIMO-NOMA) in massive ultra-reliable and low-latency communications (mURLLC) for Industrial Internet of Things (IIoT) applications. By the proposed NOMA scheme, the connected sensors are divided into several groups, and only group-level successive interference cancellation (GL-SIC) is considered at the receiver to reduce the decoding complexity and processing delay. Two schemes, i.e., mMIMO-NOMA with and without pilot sharing, are developed to fully explore the superiority of mMIMO-NOMA in mURLLC with a finite blocklength. For both schemes, the closed-form expressions of the achievable rate are obtained for the minimum mean square error (MMSE) estimator and zero-forcing (ZF) detector. Next, to address the formulated sum rate maximization problem, we develop a successive condensation approach (SCA)-based algorithm to jointly optimize pilot and data power. Besides, the max-min fairness (MMF) rate is also analyzed by verifying the feasibility of the problem. Finally, the simulation results demonstrate the effectiveness of the proposed SCA power control scheme. In addition, the advantages of the proposed two NOMA schemes in both sum rate and sum MMF rate are verified compared to traditional multi-user MIMO systems in mURLLC scenarios.

Rate-splitting multiple access (RSMA) is promising to be developed as a key enabling technology for 6G. This paper investigates the resource allocation problem in a downlink multi-user and multiple-input single-output (MU-MISO) RSMA system with successive interference cancellation (SIC) errors for ultra-reliable and low-latency communications (URLLC). The single-carrier RSMA (SC-RSMA) resource allocation scheme with URLLC is first given, where the beamforming vector and the transmission rate are optimized to maximize the effective throughput (ET). To solve the non-convex optimization problem formulated in the SC-RSMA scheme, an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) is proposed to alternately optimize the beamforming factor and the transmission rate. Furthermore, the multicarrier RSMA (MC-RSMA) scheme is developed for the URLLC access of large-scale users. Based on the results of the SC-RSMA scheme, a low-complexity three-step optimization algorithm is proposed to solve the resource allocation problem formulated in the MC-RSMA scheme. Finally, the simulation results show that the RSMA scheme and MC-RSMA scheme can respectively achieve higher ET than the non-orthogonal multiple access (NOMA) scheme and multi-carrier NOMA (MC-NOMA) scheme in the URLLC scenario, and verify that RSMA can reduce the latency and improve the reliability of the system.