The effective aggregate rate (AR) performance of large-scale multiple-input multiple-output aided orthogonal frequency division multiplexing systems based on time division duplexing protocol critically depends on uplink (UL) pilot-data power allocations for all users. We derive an asymptotic closed-form expression of the system's achievable AR for typical channel estimation schemes, which is a function of individual users' UL power allocation factors. We prove that the effective AR is a convex function of each UL power allocation factor, when all the other factors are given. The globally optimal UL pilot-data power allocation that maximizes the system's effective AR can easily be obtained using an iterative procedure, by solving an univariate convex optimization with the golden section method for individual UL power allocation factors one by one in each iteration. Owing to the piecewise convexity of the effective AR, this iterative algorithm guarantees to find the unique globally optimal solution with only one iteration. The simulation results confirm that the achievable effective AR is significantly enhanced through the proposed optimization of UL pilot-data power allocation.

Millimeter-wave (mmWave) MIMO systems have emerged as a key enabling technology for next-generation wireless networks, addressing the growing demand for ultra-high data rates through the utilization of wide bandwidths and large-scale antenna configurations. Beyond communication capabilities, these systems offer inherent advantages for integrated sensing applications, particularly in scenarios requiring precise object detection and localization. The sparse mmWave channel in the beamspace domain allows fewer radio-frequency (RF) chains by selecting dominant beams, boosting both communication efficiency and sensing resolution. However, existing channel estimation methods, such as learned approximate message passing (LAMP) networks, rely on computationally intensive iterations. This becomes particularly problematic in large-scale system deployments, where estimation inaccuracies can severely degrade sensing performance. To address these limitations, we propose a low-complexity channel estimator using a non-iterative reconstruction network (NIRNet) with a learning-based selection matrix (LSM). NIRNet employs a convolutional layer for efficient, non-iterative beamspace channel reconstruction, significantly reducing computational overhead compared to LAMP-based methods, which is vital for real-time sensing. The LSM generates a signal-aware Gaussian measurement matrix, outperforming traditional Bernoulli matrices, while a denoising network enhances accuracy under low SNR conditions, improving sensing resolution. Simulations show the NIRNet-based algorithm achieves a superior normalized mean squared error (NMSE) and an achievable sum rate (ASR) with lower complexity and reduced training overhead.

As one of the key technologies in the fifth generation of mobile communications, massive multi‐input multioutput (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radiofrequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users and analyze their impact on EE in the uplink and downlink of a single‐cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero‐forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single‐cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single‐cell perfect CSI scenario. In the single‐cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm.

When a fixed-wing Unmanned Aerial Vehicle (UAV) conducts All-Weather Post-Disaster Coverage Path Planning (PDCPP), the commonly used Sequential Path Coverage (SPC) method tends to generate redundant flight distance during turning transitions between adjacent coverage paths, which in turn increases the UAV’s flight energy consumption and thereby compromises the timeliness of rescue information acquisition. To address these challenges, this paper proposes a Multi-Selector Genetic Algorithm with Reinforcement Learning (MSGA-RL). It enhances population diversity through a distance-priority heuristic greedy initialization strategy, employs a multi-selector crossover operator to improve both solution diversity and convergence speed, and integrates a reinforcement learning-based individual retention mechanism with an elite pool protection strategy to prevent premature convergence. To simulate post-disaster scenarios, the disaster-affected area is modeled as a convex polygonal region with obstacles, while the flight energy consumption and stability of MSGA-RL are evaluated under different numbers of coverage paths. Simulation results indicate that, across all coverage path settings, MSGA-RL consistently achieves lower flight energy consumption than SPC, the Genetic Algorithm (GA), and the Dubins-based Enhanced Genetic Algorithm (DEGA), while exhibiting superior stability. In particular, in the convex quadrilateral scenario with 50 coverage paths, the flight energy consumption of MSGA-RL is reduced by 52.80%, 32.06%, and 15.96% compared with SPC, GA, and DEGA, respectively.

As the ever‐increasing attention to green communication, energy efficiency has become an imperative metric in the emerging massive multiple‐input multiple‐output (MIMO) systems. In order to maximize the energy efficiency, transmit antenna selection has been widely concerned by researchers. In this paper, we investigate the coded cooperation transmission for dynamic transmit antennas aided two‐hop massive MIMO systems. Explicitly, we propose a rate‐less codes aided cooperation scheme for reducing the implementation complexity in the broadcast phase, compared to the fixed‐rate coded cooperation scheme. Furthermore, we develop a partial cooperation scheme in the cooperative phase in order to avoid the low achievable rate caused by the full cooperation, especially when the source‐to‐relay (S‐R) channels are poor. Finally, the number of transmit antennas at the base station (BS) is optimized through theoretical analysis in a metric of maximizing the energy efficiency. Moreover, we also analyze the achievable error performance for different modulations. Our simulation results demonstrate the effectiveness of the proposed scheme.