Ahmed K. Ali¹, Jungpil Shin^2,*, Yujin Lim^3,*, Da-Hun Seong³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4245-4278, 2025, DOI:10.32604/cmes.2025.073871 - 23 December 2025

Abstract Single-signal detection in orthogonal frequency-division multiplexing (OFDM) systems presents a challenge due to the time-varying nature of wireless channels. Although conventional methods have limitations, particularly in multi-input multioutput orthogonal frequency division multiplexing (MIMO-OFDM) systems, this paper addresses this problem by exploring advanced deep learning approaches for combined channel estimation and signal detection. Specifically, we propose two hybrid architectures that integrate a convolutional neural network (CNN) with a recurrent neural network (RNN), namely, CNN-long short-term memory (CNN-LSTM) and CNN-bidirectional-LSTM (CNN-Bi-LSTM), designed to enhance signal detection performance in MIMO-OFDM systems. The proposed CNN-LSTM and CNN-Bi-LSTM architectures are… More >

Kinzah Noor¹, Agbotiname Lucky Imoize^2,*, Michael Adedosu Adelabu³, Cheng-Chi Lee^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1575-1664, 2025, DOI:10.32604/cmes.2025.073200 - 26 November 2025

Abstract The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern… More > Graphic Abstract

Chao Li^1,3,#, Chen Wang^1,3,#, Caichang Ding^2,*, Yonghao Liao^1,3, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3129-3149, 2025, DOI:10.32604/cmc.2025.067440 - 23 September 2025

Abstract Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers. However, CNNs exhibit constrained performance in high-resolution image transmission, while Transformers incur high computational cost due to quadratic complexity. Recently, VMamba, a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities, has shown great potential in computer vision tasks. Inspired by this, we propose MNTSCC, an efficient VMamba-based nonlinear joint source-channel coding (JSCC) model for wireless image transmission. Specifically, MNTSCC comprises a VMamba-based nonlinear transform module, an MCAM entropy model, and a JSCC module. In the encoding stage, the… More >

Mehrdad Shoeibi¹, Mohammad Mehdi Sharifi Nevisi², Sarvenaz Sadat Khatami³, Diego Martín^2,*, Sepehr Soltani⁴, Sina Aghakhani⁵

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2819-2843, 2024, DOI:10.32604/cmc.2024.056823 - 18 November 2024

Abstract In the evolving landscape of the smart grid (SG), the integration of non-organic multiple access (NOMA) technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy management. However, the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages, especially when broadcasted from a neighborhood gateway (NG) to smart meters (SMs). This paper introduces a novel approach based on reinforcement learning (RL) to fortify the performance of secrecy. Motivated by the need for efficient and effective training of the fully connected layers in the RL… More >

Mohammed A. Alhartomi^1,*, Mohammad F. L. Abdullah², Wafi A. B. Mabrouk², Mohammed S. M. Gismalla³, Ahmed Alzahmi¹, Saeed Alzahrani¹, Mohammad R. Altimania¹, Mohammed S. Alsawat⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2087-2105, 2024, DOI:10.32604/cmes.2024.055679 - 31 October 2024

Abstract The demand for broadband data services on high-speed trains is rapidly growing as more people commute between their homes and workplaces. However, current radio frequency (RF) technology cannot adequately meet this demand. In order to address the bandwidth constraint, a technique known as free space optics (FSO) has been proposed. This paper presents a mathematical derivation and formulation of curve track G2T-FSO (Ground-to-train Free Space Optical) model, where the track radius characteristics is 2667 m, divergence angle track is 1.5° for train velocity at V = 250 km/h. Multiple transmitter configurations are proposed to maximize More >

Mehdi Houichi^1,*, Faouzi Jaidi^1,2, Adel Bouhoula³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 393-441, 2024, DOI:10.32604/cmc.2024.054007 - 15 October 2024

Abstract The expansion of smart cities, facilitated by digital communications, has resulted in an enhancement of the quality of life and satisfaction among residents. The Internet of Things (IoT) continually generates vast amounts of data, which is subsequently analyzed to offer services to residents. The growth and development of IoT have given rise to a new paradigm. A smart city possesses the ability to consistently monitor and utilize the physical environment, providing intelligent services such as energy, transportation, healthcare, and entertainment for both residents and visitors. Research on the security and privacy of smart cities is… More >

Sagar Kavaiya¹, Hiren Mewada^2,*, Sagarkumar Patel³, Dharmendra Chauhan³, Faris A. Almalki⁴, Hana Mohammed Mujlid⁴

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3685-3711, 2024, DOI:10.32604/cmc.2024.053587 - 12 September 2024

Abstract The mobility and connective capabilities of unmanned aerial vehicles (UAVs) are becoming more and more important in defense, commercial, and research domains. However, their open communication makes UAVs susceptible to undesirable passive attacks such as eavesdropping or jamming. Recently, the inefficiency of traditional cryptography-based techniques has led to the addition of Physical Layer Security (PLS). This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments, proposing a solution to complement the conventional cryptography approach. Initially, we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems, namely hybrid… More >

Mohammad Mehdi Sharifi Nevisi¹, Elnaz Bashir², Diego Martín^3,*, Seyedkian Rezvanjou⁴, Farzaneh Shoushtari⁵, Ehsan Ghafourian²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3971-3991, 2024, DOI:10.32604/cmc.2024.047875 - 26 March 2024

Abstract This paper focuses on wireless-powered communication systems, which are increasingly relevant in the Internet of Things (IoT) due to their ability to extend the operational lifetime of devices with limited energy. The main contribution of the paper is a novel approach to minimize the secrecy outage probability (SOP) in these systems. Minimizing SOP is crucial for maintaining the confidentiality and integrity of data, especially in situations where the transmission of sensitive data is critical. Our proposed method harnesses the power of an improved biogeography-based optimization (IBBO) to effectively train a recurrent neural network (RNN). The… More >

Shuting Ge^1,2, Jin Ren^2,3,*, Yihua Shi⁴, Yujun Zhang¹, Shunzhi Yang², Jinfeng Yang²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3215-3245, 2024, DOI:10.32604/cmc.2023.046746 - 26 March 2024

Abstract In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues,… More >

Sepehr Soltani¹, Ehsan Ghafourian², Reza Salehi³, Diego Martín^3,*, Milad Vahidi⁴

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 93-108, 2024, DOI:10.32604/iasc.2024.042693 - 29 March 2024

Abstract For many years, researchers have explored power allocation (PA) algorithms driven by models in wireless networks where multiple-user communications with interference are present. Nowadays, data-driven machine learning methods have become quite popular in analyzing wireless communication systems, which among them deep reinforcement learning (DRL) has a significant role in solving optimization issues under certain constraints. To this purpose, in this paper, we investigate the PA problem in a -user multiple access channels (MAC), where transmitters (e.g., mobile users) aim to send an independent message to a common receiver (e.g., base station) through wireless channels. To… More >