Joint Design of Embedded Index Coding and Beamforming for MIMO-based Distributed Computing via Multi-Agent Reinforcement Learning

TL;DR

This paper introduces a multi-agent reinforcement learning framework to jointly optimize embedded index coding and beamforming in MIMO distributed computing, reducing communication time effectively in wireless environments.

Contribution

It presents a novel MARL-based method for joint EIC and beamforming design, addressing complexity issues and adapting to dynamic wireless conditions.

Findings

MARL approach achieves near-optimal performance

Significantly reduces transmission time compared to traditional methods

Effective in dynamic wireless environments

Abstract

In distributed computing systems, reducing the communication load during the data shuffling phase is a critical challenge, as excessive inter-node transmissions are a major performance bottleneck. One promising approach to alleviate this burden is Embedded Index Coding (EIC), which exploits cached data at user nodes to encode transmissions more efficiently. However, most prior work on EIC has focused on minimizing code length in wired, error-free environments-an objective often suboptimal for wireless multiple-input multiple-output (MIMO) systems, where channel conditions and spatial multiplexing gains must be considered. This paper investigates the joint design of EIC and transmit beamforming in MIMO systems to minimize total transmission time, an NP-hard problem. We first present a conventional optimization method that determines the optimal EIC via exhaustive search. To address its prohibitive complexity and adapt to dynamic wireless environments, we propose a novel, low-complexity multi-agent reinforcement learning (MARL) framework. The proposed framework enables decentralized agents to act on local observations while effectively managing the hybrid action space of discrete EIC selection and continuous beamforming design. Simulation results demonstrate that the proposed MARL-based approach achieves near-optimal performance with significantly reduced complexity, underscoring its effectiveness and practicality for real-world wireless systems.