A Novel Scheme for Separate Training of Deep Learning-Based CSI Feedback Autoencoders

In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. .

Building mm Wave on the Evolving C-V2X:mm Wave NR-V2X

Since the evolving standardization of cellular V2X(C-V2X)technologies is continuously developed by 3GPP,research on new radio(NR-V2X)has been on schedule by academic and industries.Though millimeter wave(mm Wave)frequency band is supposed to provide large transmission bandwidth,yet the development of mm Wave band in NR-V2X is still in preliminary stage.In this article,a comprehensive discussion of mm Wave NR-V2X is given covering trends,standardization landscape,and enabling technologies,aiming at tackling the challenge of channel modeling,directional channel access,beamforming and blockage management.The vision of mm Wave NR-V2X is to fully support the development of automated driving,holographic control display and improved in-car infotainment for the future.

Blockchain for Network Slicing in 5G and Beyond:Survey and Challenges

Network slicing has gained popularity as a result of the advances in the fifth generation(5G)mobile network.Network slicing facilitates the support of different service types with varying requirements,which brings into light the slicing-aware next generation mobile network architecture.While allowing resource sharing among multiple stakeholders,there is a long list of administrative negotiations among parties that have not established mutual trust.Distributed ledger technology may be a solution to mitigate the above issues by taking its decentralized yet immutable and auditable ledger,which may help to ease administrative negotiations and build mutual trust among multi-stakeholders.There have been many research interests in this direction which focus on handling various problems in network slicing.This paper aims at constructing this area of knowledge by introducing network slice from a standardization point of view to start with,and presenting security,privacy,and trust challenges of network slicing in 5G and beyond networks.Furthermore,this paper covers distributed ledger technologies basics and related approaches that tackle security,privacy,and trust threats in network slicing for 5G and beyond networks.The various proposals proposed in the literature are compared and presented.Lastly,limitations of current work and open challenges are illustrated as well.