a hybrid orthogonal multiple acInternet-of-things (IoT) systems, where a group of interested IoT devices is assigned to subchannels, some using NOMA and the others using OMA. Since multicasting is heavily constrained by the weakest channel gain of involved IoT devices, an advanced resource allocation strategy is required to enhance energy efficiency. This paper investigates a simultaneous optimization of power allocation and subchannel assignment to maximize energy efficiency in HMA, not resorting to alternating optimization (AO). Specifically, an optimization problem is formulated as a mixedinteger nonlinear programming (MINLP) problem, posing a significant challenge in finding a globally optimal solution. To address this, we propose two novel algorithms: the first is designed for small search spaces, using the Big-M method for linearization of a bilinear term followed by an iterative and thresholdbased rounding, and the second is tailored for large search spaces, utilizing a hybrid proximal policy optimization (HPPO) technique to train hybrid action space. The simulation results confirm that both the proposed algorithms surpass other AObased benchmarks by 7.1% and 5.6%, respectively, in terms of energy efficiency.
Coverage and energy consumption are highly nonconvex and challenging optimization problems in wireless sensor networks (WSN)s deployment. Traditionally, these problems are optimized separately. In this paper, we present a multi-objective optimization model that addresses both issues concurrently, enhancing its relevance for real-world applications such as seabed monitoring, tactical surveillance, and traffic direction systems in 3D WSNs, where balancing coverage and energy efficiency is critical. While conventional intelligent optimization algorithms (e.g., PSO, GWO) often excel in benchmark function tests, they struggle with premature convergence and computational inefficiency in practical engineering problems like WSN deployment. Therefore, we propose a novel heuristic optimization algorithm with low computational complexity, termed the imitator-inspired optimization algorithm (IOA). To validate IOA's single-objective optimization performance, it is applied to coverage optimization in 2D WSNs and compared with five other popular algorithms. For energy-efficient coverage enhancement in 3D WSNs, we introduce a multi-objective imitator-inspired optimization algorithm (MOIOA), which simultaneously optimizes the conflicting objectives of coverage and energy consumption. MOIOA's performance is benchmarked against three well-known multi-objective algorithms. Simulation results demonstrate that the proposed algorithms exhibit both reliability and superiority in single-objective and multi-objective optimization, effectively solving the energy-efficient coverage enhancement problem in 3D WSNs.
The development of wireless communication systems has reached even more ambitious goals through the years, one of this being the attempt to exchange information by using optical signals in the underwater domain. To do so, there is the need to overcome the intrinsic challenges of this environment, represented by the strong attenuations and temporal variations caused by the optical properties of the underwater medium. Hence, the feasibility of an underwater communication system necessarily passes through the ability to model the underwater channel and design a system that is resilient to this environment. In particular, the main task is to characterize the turbulence and the channel impulse response, having both a major impact on the power fading and waveform distortions experienced by the transmitted signal during the packet communication. In this work, we propose a comprehensive model for the attenuation of the underwater medium focusing on the effect of turbulence and scattering within the marine environment and their impact on the temporal response of the whole system. Then, we summarize these results in the turbulence random process model and the channel impulse response. We also analyze the performances of the system using the most important figures of merit, such as the and capacity, and analyze the temporal distortion of the received waveforms, using the scatter plots and the eye diagrams. The analysis is carried out using several uncoded modulation formats, without error correction and other impairments, by means of numerical simulations.
Satellite-assisted relay networks (SaRNs) can provide wide-area coverage and enhance communication capabilities in remote or complex environments. In this paper, we investigate the physical layer security of SaRNs in multiple-relay and multiple-eavesdropper scenarios, and the effects of hardware impairments (HIs), eavesdropper mobility, and imperfect channel state information (CSI) are considered. Specifically, a SaRN transmission framework with the mixed decode-and-forward satellite/terahertz (THz) link is proposed, where the satellite link transmits information from a geosynchronous earth orbit (GEO) satellite to relays, while the THz links support communication between relays and indoor users. Two wiretapping cases are analyzed: Case I, where the eavesdroppers overhear the satellite to-relay (S-R) transmissions; and Case II, where the eaves droppers intercept both the S-R and relay-to-destination (R-D) transmissions. For two wiretapping cases, expressions of secrecy outage probability (SOP) and average secrecy capacity (ASC) under HIs, eavesdropper mobility, and imperfect CSI conditions are derived separately. The results show that the secrecy performance is related to the number of relays and eavesdroppers, HIs level, eavesdropper mobility, channel conditions, and the antenna configuration. Moreover, for the ideal cases of ideal hardware, static eavesdropping, and perfect CSI, we obtain exact expressions for SOP and ASC in terms of the Fox's H-function and their corresponding asymptotic results. The asymptotic analysis shows that the diversity gain of the system under both cases depends on the number of relays and parameters of the THz links.
Accurate channel estimation with limited pilot overhead is a key bottleneck for 6G MU-mMIMO, particularly at mmWave (with implications for THz) where path loss and multiuser interference are severe. We propose a two-stage least squares convolutional neural network (LS-CNN) framework in which a robust LS estimator provides a stable coarse channel estimate, avoiding the sparsity assumptions of compressed sensing algorithms that often fail in rich-scattering outdoor environments. In the second stage, this estimate is refined using a CNN trained on a hybrid dataset expanded through controlled augmentation of a generative adversarial network (GAN), improving generalization while minimizing pilot overhead. Training data is refined using mean squared error (MSE) thresholds to ensure high-quality samples, and a robust standardization method is used to stabilize learning with complex-valued input. Simulations on the DeepMIMO ray-tracing dataset demonstrate that the proposed approach provides an up to 8 dB normalized MSE (NMSE) improvement at low signal-to-noise ratio (SNR) over classical LS and MMSE estimators. In our experiments, it achieves performance comparable to that of the ideal MMSE estimator while requiring 60% fewer pilot symbols. Complexity analysis confirms that the proposed hybrid architecture is significantly more efficient than state-of-the-art Vision Transformers, making it practical for real-time 6G deployments.
