Joint Resource Allocation and Transceiver Design for Sum-Rate Maximization under Latency Constraints in Multicell MU-MIMO Systems

TL;DR

This paper develops a joint resource allocation and transceiver design framework for multicell MU-MIMO OFDM systems that maximizes sum-rate under latency constraints, considering practical factors like signaling overhead.

Contribution

It introduces a novel optimization approach that jointly designs beamforming, scheduling, and resource allocation with centralized and decentralized solutions for latency-sensitive MU-MIMO OFDM systems.

Findings

Significant sum-rate gains over saturated buffer models.

Outperforms finite-buffer algorithms in realistic scenarios.

Effective joint design improves system performance under latency constraints.

Abstract

Due to the continuous advancements of orthogonal frequency division multiplexing (OFDM) and multiple antenna techniques, multiuser multiple input multiple output (MU-MIMO) OFDM is a key enabler of both fourth and fifth generation networks. In this paper, we consider the problem of weighted sum-rate maximization under latency constraints in finite buffer multicell MU-MIMO OFDM systems. Unlike previous works, the optimization variables include the transceiver beamforming vectors, the scheduled packet size and the resources in the frequency and power domains. This problem is motivated by the observation that multicell MU-MIMO OFDM systems serve multiple quality of service classes and the system performance depends critically on both the transceiver design and the scheduling algorithm. Since this problem is non-convex, we resort to the max-plus queuing method and successive convex approximation. We propose both centralized and decentralized solutions, in which practical design aspects, such as signaling overhead, are considered. Finally, we compare the proposed framework with state-of-the-art algorithms in relevant scenarios, assuming a realistic channel model with space, frequency and time correlations. Numerical results indicate that our design provides significant gains over designs based on the wide-spread saturated buffers assumption, while also outperforming algorithms that consider a finite-buffer model.