This paper introduces the novel IRS-based Optimal Relay Selection (ORS-IRS) method, aimed at analyzing the performance of wireless communication systems with an emphasis on symmetry. The ORS-IRS approach presents an innovative communication algorithm that seamlessly integrates Intelligent Reflecting Surfaces (IRS) with relay selection techniques. Through adaptive adjustments of reflection coefficients, IRS elements efficiently manipulate incoming signals, fostering symmetry in signal strength enhancement and latency reduction for improved signal delivery to the intended destination. This symmetrical optimization in channel capacity and transmission power ensures reliable data transmission with low latency, achieved through the seamless integration of IRS and relay selection techniques. In contrast, the Cell-Free Massive MIMO (CF-M-MIMO), with its decentralized architecture, excels in serving a larger user base and attaining remarkable capacity gains, showcasing a different dimension of symmetry. The Decode-and-Forward (DF) relaying approach demonstrates its potential in enhancing signal reliability across extended distances, contributing to the overall symmetry of the comparative analysis. This comprehensive evaluation provides valuable insights into selecting appropriate transmission strategies, particularly for applications that demand high capacity and reliability in the design of modern wireless communication systems with a symmetrical focus.

This study investigates power control algorithms for cell-free massive MIMO (CF-M-MIMO) systems, with the aim of enhancing spectral efficiency (SE) and overall system capacity. A novel Heuristic Iterative Power Control (HIPC) algorithm is proposed and compared against the Equal Power Control (EPC) and Max-Min Fairness Power Control (MMFPC) algorithms within both small-cell and CF-M-MIMO systems under Minimum Mean Squared Error (MMSE) and Maximum Ratio (MR) conditions. The simulation results demonstrate that the HIPC algorithm significantly outperforms both EPC and MMFPC by achieving higher SE and improving user fairness, particularly in CF-M-MIMO systems. Moreover, the robustness and effectiveness of the HIPC algorithm under MR conditions underscore its practical utility. These contributions provide important guidance on the design and implementation of CF-M-MIMO systems, establishing the HIPC algorithm as a valuable tool for optimizing power control and enhancing system performance.

Wireless Sensor Networks (WSNs) based on Internet of Things (IoT) applications are increasing day by day. These applications include healthcare, infrastructure monitoring, smart homes, wearable devices and smart cars. However, considering the fact that many different application areas will emerge in next generation wireless communication systems, efficient use of frequency spectrum is important. Because the whole frequency spectrum is now very crowded, it is important to ensure maximum spectrum efficiency for effective WSNs based on IoT. This study sought to determine which mode more effectively achieves spectrum efficiency for the performance of effective IoT systems under given conditions with respect to the length of the pilot sequence, Time Division Duplexing (TDD) or Frequency Division Duplexing (FDD). The results were obtained by Monte Carlo simulations. To the best of our knowledge, a study of effective mode selection analysis for spectrum efficiency in IoT based systems has not been available in the literature yet. The results of this study are useful for determining the appropriate design conditions for WSNs based on IoT.

Automatic modulation classification (AMC) plays a central role in adaptive and cognitive wireless communication systems by enabling reliable signal interpretation under varying channel conditions. This study presents a comprehensive evaluation of deep learning based AMC using constellation diagram representations under additive white Gaussian noise (AWGN) across six signal to noise ratio levels ranging from $$-5$$ - 5 , 0, 5, 10, 15, 20 dB. A balanced dataset consisting of 24,000 samples from eight modulation formats, namely BPSK, QPSK, 8PSK, 16PSK, 32PSK, 16QAM, 64QAM, and 256QAM, is employed to benchmark four convolutional architectures. These include the classical AlexNet, two lightweight modern backbones, MobileNetV2 and EfficientNet-B0, and the proposed Squeeze–Excite Convolutional Attention Lightweight Network (SCALNet). SCALNet integrates depthwise separable convolutions, squeeze–excite channel reweighting, and residual connections to enhance feature discrimination with minimal computational cost. Experimental results demonstrate that SCALNet consistently achieves the best robustness at low and mid SNRs, obtaining the highest F1-score at $$-5$$ - 5 dB (0.6004) and stable accuracy up to 20 dB, while requiring only 0.37M parameters and 1.36 MB of memory. Compared with AlexNet, SCALNet reduces parameter count and FLOPs by more than %99, while MobileNetV2 and EfficientNet-B0 provide intermediate performance–efficiency trade-offs. Efficiency analysis shows that SCALNet delivers the lowest inference latency (4.49 ms at 20 dB) and the smallest computational footprint across all SNRs, confirming its suitability for embedded or resource-constrained AMC deployments. These results position SCALNet as a lightweight yet highly reliable architecture, with future work planned to extend the evaluation toward fading, phase noise, and nonlinear RF impairments for emerging 6G communication scenarios.