Yuanwei Liu

TL;DR: In this chapter, the sustainability of NOMA will be discussed by talking about cooperative N OMA and wireless powered NOMa networks.

TL;DR: In this article , a secure transmission framework is proposed for simultaneously transmitting and re-ecting surface (STARS) net-works in presence of an untrusted user, where the active and passive secure beamforming optimization problem is addressed.

TL;DR: In this article , a novel simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) aided downlink NOMA communication frame-work is proposed, where a partitioning approach is proposed to divide the STAR-RIDS into several tiles.

TL;DR: The orbital angular momentum (OAM) beam generated by a holographic multiple-input multiple-output (MIMO) is employed to utilize the OAM mode orthogonality and improve the capacity performance of the line-of-sight base station-RIS channel.

Abstract: Grant-free non-orthogonal multiple access (NOMA) is considered as one of the supporting technology for massive connectivity for future networks. In the grant-free NOMA systems with a massive number of users, user activity detection is of great importance. Existing multi-user detection (MUD) techniques rely on complicated update steps which may cause latency in signal detection. In this paper, we propose a generative neural network-based MUD (GenMUD) framework to utilize low-complexity neural networks, which are trained to reconstruct signals in a small fixed number of steps. By exploiting the uncorrelated user behaviours, we design a network architecture to achieve higher recovery accuracy with a low computational cost. Experimental results show significant performance gains in detection accuracy compared to conventional solutions under different channel conditions and user sparsity levels. We also provide a sparsity estimator through extensive experiments. Simulation results of the sparsity estimator showed high estimation accuracy, strong robustness to channel variations and neglectable impact on support detection accuracy.