Cross-Layer Optimization: Joint User Scheduling and Beamforming Design With QoS Support in Joint Transmission Networks

TL;DR

This paper introduces a novel joint optimization framework for user scheduling and beamforming in multicell multiuser networks, enhancing system sum rate while satisfying QoS and power constraints.

Contribution

It formulates a cross-layer optimization problem, transforms it into a tractable form, and proposes an alternating optimization algorithm along with heuristic methods for efficient solutions.

Findings

The proposed method maximizes sum rate under QoS and power constraints.

Base stations allocate zero power to unscheduled users, as proven theoretically.

Numerical results show the effectiveness and balanced performance of the proposed algorithms.

Abstract

User scheduling and beamforming design are two crucial yet coupled topics for wireless communication systems. They are usually optimized separately with conventional optimization methods. In this paper, a novel cross-layer optimization problem is considered, namely, the user scheduling and beamforming are jointly discussed subjecting to the requirement of per-user quality of service (QoS) and the maximum allowable transmit power for multicell multiuser joint transmission networks. To achieve the goal, a mixed discrete-continue variables combinational optimization problem is investigated with aiming at maximizing the sum rate of the communication system. To circumvent the original non-convex problem with dynamic solution space, we first transform it into a 0-1 integer and continue variables optimization problem, and then obtain a tractable form with continuous variables by exploiting the characteristics of 0-1 constraint. Finally, the scheduled users and the optimized beamforming vectors are simultaneously calculated by an alternating optimization algorithm. We also theoretically prove that the base stations allocate zero power to the unscheduled users. Furthermore, two heuristic optimization algorithms are proposed respectively based on brute-force search and greedy search. Numerical results validate the effectiveness of our proposed methods, and the optimization approach gets relatively balanced results compared with the other two approaches.