Hongtao Zhang

TL;DR: This paper provides the proof to demonstrate that the MIMO-NOMA is strictly better than MIMo-OMA in terms of sum channel capacity (except for the case where only one user is being communicated to), i.e., for any rate pair achieved by MIMD, there is a power split for which MIMM-NomA can achieve rate pairs that are strictly larger.

TL;DR: The necessity of mobility prediction, together with its intrinsic characteristics in terms of movement predictability, prediction outputs, and performance metrics is discussed and an overview of the state-of-the-art approaches is provided.

TL;DR: A deep residual learning framework is proposed, UcnBeamNet, to enhance the ability of approximating the iterative algorithm for sum rate maximization, where multi-branch subnets are connected in parallel to extract extra information.

TL;DR: This is the first work that attempts to integrate decode-forward (DF), amplify-forward, and NOMA into one strategy design to improve system performance and results show that compared with the traditional schemes, the proposed HDAF-NOMA scheme can achieve larger sum channel capacity for the transmission of x1 and x2, and it can also achieve larger average system throughput at high SNR region.

TL;DR: Considering the complexity of ultradense network and practical implementation, a backhaul-aware $\eta$ -optimal biasing adjustment model is proposed for flexible coverage and aims to optimize the coverage for throughput improvement while matching the backhaul capacity.