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.
Abstract
The rapid advancement of sixth-generation (6G) networks is accelerating the convergence of media intelligence and communication intelligence, driving media communication beyond conventional bit-level delivery toward intelligent, semantic-aware, and generative paradigms. Emerging media services require not only high data rates and low latency, but also semantic awareness, perceptual quality assurance, adaptive resource orchestration, trustworthy content processing, and personalized media generation. Meanwhile, media technologies are evolving from handcrafted signal processing and conventional coding toward artificial intelligence (AI)-driven representation learning, content understanding, and generative reconstruction. Motivated by these trends, this paper presents a systematic survey of media communication technologies for 6G vision communication by revisiting the evolution of communication and media technologies and clarifying the intrinsic relationship between media content processing and wireless transmission. We introduce a unified framework consisting of four key dimensions: AI-driven media technologies, media-aware wireless transmission, large model-enabled media communication, and intelligent network infrastructures. Specifically, AI-driven media technologies encompass media coding, content understanding, quality assessment, security and compliance detection, and AIGC-enabled media generation, while media-aware wireless transmission is examined from three complementary perspectives: semantic joint source-channel optimization, which jointly encodes task-relevant semantic information; source-aware transmission optimization, which leverages media characteristics for channel adaptation, prediction, and compensation; and channel-aware source optimization, which adapts media coding and reconstruction based on real-time channel conditions.
This survey formally categorizes state-of-the-art DTN architectures into passive monitoring twins and active control twins, and provides an in-depth evaluation of their underlying enabling technologies, specifically ray-tracing, reconfigurable intelligent surfaces, artificial intelligence, and mobile edge computing.
Charalampos Oikonomidis, E. T. Michailidis, N. Miridakis· 0 citations
A comparative analysis of key technologies in intelligent signal processing for 6G based on a literature survey indicates that AI-based methods offer superior performance and lower computational latency in complex scenarios, yet they continue to face challenges related to interpretability, generalization, and standardization.
Chen-Song Zhang· International journal of eng...· 0 citations
The study concludes that although 5G provides the technological foundation for next-generation digital transformation, continued research is required to improve network security, intelligent resource allocation, sustainable deployment, and integration with future sixth-generation (6G) communication systems.
E. M. Tanuja, Basavaraj S. Pol· International Journal of Res...· 0 citations
The sixth-generation (6G) wireless networks are expected to enable the deep integration of communication, sensing, computing, control, and intelligence in highly dynamic environments. This evolution drives a fundamental transition from conventional passive channel adaptation to proactive channel cognition and reconfiguration, wherein wireless channels are no longer regarded as uncontrollable propagation media but as network resources that can be learned, predicted, and actively reconfigured. This paper presents a comprehensive overview of this emerging paradigm. We first review channel cognition through the channel knowledge map (CKM) as a systematic framework for learning and exploiting channel characteristics across space, time, and frequency domain. The definitions, construction methods, and applications in wireless networks of CKMs are comprehensively reviewed. Building upon channel cognition, we then review channel reconfiguration technologies from two complementary perspectives: transceiver-side reconfiguration enabled by movable antennas (MAs) and environment-side reconfiguration enabled by intelligent reflecting surfaces (IRSs). For both MA- and IRS-enabled wireless systems, we review their architectures, performance advantages, and key design challenges. Finally, we discuss several promising research directions to inspire further innovations in this burgeoning field.
Wenyan Ma, Zixiang Ren, Weitong Zhai et al.· 0 citations
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.
A. Jamil· International Journal of Mul...· 0 citations
This paper conducts a comprehensive literature survey to examine existing optimization techniques and proposes an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing that demonstrates the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability.
Kenji Sato· International Journal of Mod...· 0 citations
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