Abstract
Opportunistic beamforming (OBF) is a potential technique in the fifth generation (5G) and beyond 5G (B5G) that can boost the performance of communication systems and encourage high user quality of service (QoS) through multi-user selection gain. However, the achievable rate tends to be saturated with the increased number of users, when the number of users is large. To further improve the achievable rate, we proposed a multi-antenna opportunistic beamforming-based relay (MOBR) system, which can achieve both multi-user and multi-relay selection gains. Then, an optimization problem is formulated to maximize the achievable rate. Nevertheless, the optimization problem is a non-deterministic polynomial (NP)-hard problem, and it is difficult to obtain an optimal solution. In order to solve the proposed optimization problem, we divide it into two suboptimal issues and apply a joint iterative algorithm to consider both the suboptimal issues. Our simulation results indicate that the proposed system achieved a higher achievable rate than the conventional OBF systems and outperformed other beamforming schemes with low feedback information.
Highlights
Multiple-input-multiple-output (MIMO) is one of the key techniques for the fifth generation (5G) and beyond 5G (B5G) [1,2,3] that can improve the system performance through multi-antenna technique without other extra wireless resource
Backhaul traffic caused by heavy signalling in millimeter wave (mmWave)-based 5G heterogeneous networks (HetNets) was reduced through a cluster-based central architecture
We found that the proposed optimization problem (20) is a non-deterministic polynomial (NP)-hard problem and is difficult to solve due to the following reasons: (1) Both Pb and Pk are continuous variables; both k and u are discrete integer variables
Summary
Paper,let letx xand andx xpresent present a variable a vector, respectively. XT indiDuring this a variable andand a vector, respectively. | · | represents the the cates transpose.∥k· ·∥kdenotes denotesthe the Frobenius norm a vector,. Represents the the absolute value of a variable. C is applied to denote the complex space. The meanings of the notations are listed in Appendix A
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