Proposes network architecture for extended reality applications in 6G, including semantic communication, joint source-channel coding, and holographic displays.
Rigorous comparison of RIS-assisted links versus conventional decode-and-forward relaying — demonstrates conditions under which each approach dominates.
Defines scalable cell-free massive MIMO architecture with distributed processing, local combining, and large-scale fading decoding — a key 6G topology candidate.
Surveys federated learning as a privacy-preserving distributed training method for 6G intelligent networks, including communication-efficient FL algorithms.
Introduces holographic MIMO using continuous aperture antennas, exploring near-field propagation, electromagnetic information theory, and hardware prototyping.
Reviews physical layer security techniques for 6G including RIS-aided secure beamforming, covert communication, and anti-eavesdropping in THz channels.
Analyzes energy consumption at THz frequencies and cell-free deployments, proposing AI-driven resource management to meet 6G energy efficiency targets.
IEEE Transactions on Cognitive Communications and Networking
Comprehensive survey of semantic communication systems — joint source-channel coding, knowledge-base design, and performance metrics beyond Shannon capacity.
Characterizes near-field propagation regimes relevant to large aperture arrays at mmWave/THz, with channel models, beamforming design, and localization.
Framework for wirelessly powered IoT devices using simultaneous wireless information and power transfer (SWIPT) and ambient backscatter at ultra-low power.
Identifies 6G-specific threats including AI poisoning, THz jamming, and satellite spoofing, with countermeasure frameworks based on zero-trust architecture.
Surveys generative AI (diffusion models, LLMs, GANs) for 6G applications — channel estimation, network optimization, semantic communication, and digital twins.
Derives closed-form outage probability for RIS-assisted links accounting for realistic hardware distortions — phase noise, ADC quantization, and amplifier nonlinearity.
Nature perspective on 6G as an AI-native paradigm shift — embedding ML across PHY, MAC, and network layers with human-centric experience as the design metric.
Proposes polar-domain channel model and compressed sensing estimator for near-field XL-RIS — handles spatial non-stationarities missed by far-field models.