A Comparative Analysis of Key Technologies in Intelligent Signal Processing for 6G

Reconfigurable Intelligent Surface-Assisted 6G Communication: A Beamforming and Channel Modeling Perspective

A Review of Using Artificial Intelligence to Improve Signal Reception in 5G and Future Mobile Networks

Wireless communication systems are constantly evolving from 5th generation (5G) networks to beyond-5G (B5G) and 6th generation (6G) networks, presenting unprecedented requirements for highly reliable and efficient reception of signals. Advanced applications like autonomous vehicles, industrial automation, remote healthcare, extended reality and integrated sensing demand ultra-reliable communications with low latency (URLLC), massively connected devices and quality of service. The multipath fading and co-channel interference, dynamic user mobility, blockage and penetration losses of millimeter-wave and terahertz frequencies, and rapidly changing propagation environments, however, severely affect the reception of signals in modern wireless systems. These impairments affect signal to noise ratio (SNR), signal to interference-plus-noise ratio (SINR), bit error rate (BER), throughput, and overall communication reliability. Artificial Intelligence (AI) has become a game-changer that can tackle such challenges with data-driven and adaptive optimization. In this review, the authors comprehensively examine the roles that Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable Artificial Intelligence (XAI) play in improving the reception of signals in 5G and future mobile networks. It is used in key applications such as channel estimation, equalization, beamforming, blockage prediction, interference mitigation, predictive handover, power control, and intelligent resource allocation. The literature provides ample evidence of the significant improvements of AI-based techniques over traditional methods in terms of SINR, BER, throughput and reliability in highly dynamic environments.

6G communications: empowering the future with advanced AI and ML techniques

A unified view of how 6G communications, empowered by advanced AI and ML technologies and distributed intelligence, are expected to drive evolution and expedite the journey toward a smart and intelligent connected world is offered.

A survey of intelligent OFDM receivers for channel estimation in model driven 5G and AI enabled 6G systems

Orthogonal Frequency Division Multiplexing (OFDM) is the core physical-layer modulation technique used in modern wireless communication systems due to its high spectral efficiency and robustness against frequency-selective fading. In fifth-generation (5G) systems, channel estimation for OFDM is mainly performed using classical pilot-based methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) estimation. Although these techniques are computationally efficient and well standardized, their performance degrades in practical fading environments, particularly under high mobility, sparse pilot configurations, and higher-order modulation schemes. This survey reviews the OFDM physical layer and traditional channel estimation techniques in 5G systems and analyzes their limitations using performance results over AWGN and Rayleigh fading channels. The paper further examines recent artificial intelligence and machine learning (AI/ML)-based channel estimation approaches proposed for sixth-generation (6G) systems, highlighting their improved robustness and adaptability in dynamic channel conditions. A comparative analysis between classical 5G and AI/ML-based 6G channel estimation methods is presented in terms of bit error rate performance, adaptability, and complexity, followed by a discussion on the advantages and limitations of intelligent channel estimation for future wireless networks.

A survey on AI-empowered RIS-assisted integrated sensing and communication: From terrestrial to non-terrestrial networks

The integration of artificial intelligence (AI), reconfigurable intelligent surfaces (RIS), and integrated sensing and communication (ISAC) is emerging as a key enabler for intelligent, adaptive, and efficient wireless networks in the 6G era. This survey provides a comprehensive and well-structured overview of how AI is revolutionizing RIS-assisted ISAC by enabling dynamic, data-driven control over the radio environment. Through intelligent configuration of RIS, AI facilitates real-time adaptation of signal propagation paths, enhances sensing resolution, and ensures robust communication performance across diverse and dynamic network conditions. The discussion is organized across two major domains: terrestrial networks (TNs) and non-terrestrial networks (NTNs). In TNs, we examine four critical directions: (i) improving EE, (ii) strengthening security, (iii) achieving high sensing accuracy and low communication latency via joint optimization, and (iv) maximizing throughput. In NTN domain, especially within UAV-based platforms, we explore AI-RIS frameworks that support coordinated sensing and communication, reinforce link security in mobile and unpredictable environments, and enhance aerial network capacity through real-time RIS tuning. By bridging both terrestrial and aerial applications, this survey highlights how AI-driven RIS control enables environment-aware, low-latency, and secure ISAC operations. Finally, we identify and analyze key challenges such as managing trade-offs between sensing and communication, generalizing AI models across varied network scenarios, and ensuring scalable, low-complexity real-time deployment. The work concludes with forward-looking insights into future research directions essential for realizing robust and intelligent RIS-assisted ISAC architectures in 6G and beyond.

Media Meets Communication in 6G: Fundamentals, Key Technologies, and Applications

A systematic survey of media communication technologies for 6G vision communication is presented by revisiting the evolution of communication and media technologies and clarifying the intrinsic relationship between media content processing and wireless transmission.