Beam division multiple access for millimeter wave massive MIMO: Hybrid zero-forcing beamforming with user selection

Massive multiple-input multiple-output (MIMO) systems are considered a promising solution to minimize multiuser interference (MUI) based on simple precoding techniques with a massive antenna array at a base station (BS). This paper presents a novel approach of beam division multiple access (BDMA) which BS transmit signals to multiusers at the same time via different beams based on hybrid beamforming and user-beam schedule. With the selection of users whose steering vectors are orthogonal to each other, interference between users is significantly improved. While, the efficiency spectrum of proposed scheme reaches to the performance of fully digital solutions, the multiuser interference is considerably reduced

A Fair User Selection Algorithm for Multi-User Massive MIMO System

Massive Multiple-input Multiple-output (Massive MIMO) system is one of the most potential candidates for the fifth-generation wireless communication. Massive-MIMO system employs a very large number of antennas which could easily reach more than a thousand antennas in the future. Instead of using an omni directional antenna which is a very popular base station antenna nowadays, massive-MIMO uses its large number of antennas to create multiple smaller beams which are transmitted directly into the intended receivers. In this paper, we develop a user-scheduling technique for Multi-user Massive-MIMO system called Fair-CDUS which is developed from charcoal distance-based user selection (CDUS) technique. Fair-CDUS aims to give more fairness to users in term of selection frequency and at the same time could maintain the total throughput performance. Some experimental scenarios with a different number of beams and a different number of receiving antenna are presented in this paper. We believe this proposed method could be a potential method to be used in Multi-user Massive-MIMO system

A Tutorial on Nonorthogonal Multiple Access for 5G and Beyond

Today's wireless networks allocate radio resources to users based on the orthogonal multiple access (OMA) principle. However, as the number of users increases, OMA based approaches may not meet the stringent emerging requirements including very high spectral efficiency, very low latency, and massive device connectivity. Nonorthogonal multiple access (NOMA) principle emerges as a solution to improve the spectral efficiency while allowing some degree of multiple access interference at receivers. In this tutorial style paper, we target providing a unified model for NOMA, including uplink and downlink transmissions, along with the extensions tomultiple inputmultiple output and cooperative communication scenarios. Through numerical examples, we compare the performances of OMA and NOMA networks. Implementation aspects and open issues are also detailed.Comment: 25 pages, 10 figure

A Unified Framework for Precoding and Pilot Design for FDD Symbol-Level Precoding

Large-scale antenna array techniques are key enablers for modern wireless communication systems. Channel state information (CSI) is indispensable for large-scale multi-antenna systems, but is challenging to obtain. To tackle this issue, in this paper we propose a unified precoding and pilot design frame-work, that allows minimal and precoding-sensitive modified CSI (mCSI) to be collected. This results in a significant reduction in the CSI overheads and complexity compared to classical physical CSI (pCSI) estimation. Based on this unified framework, we further propose an intelligent pilot (IP) approach that senses and selects the mCSI to be collected. The IP approach utilizes a compressive sensing formulation to attach sensing and selection of significant mCSI to precoding optimization. We apply the above techniques to the multi-user frequency division duplexing (FDD) downlink as an example. Our study shows that the advantages of the IP approach are three-fold. First, in contrast to the pCSI, precoding-sensitive information is only captured, which reduces the training and feedback overheads. Second, the precoders are optimized directly based on the mCSI, which avoids recovering the pCSI of high-dimension. Third, since the mCSI of reduced dimension is utilized, the scale of the problem to optimize the precoder is also reduced and thus it is much easier to solve