Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications

TL;DR

This paper introduces MACOL, a multi-agent learning strategy that uses contextual bandits to manage interference in mmWave vehicular networks, maintaining low interference levels despite high mobility and traffic loads.

Contribution

The paper proposes a novel multi-agent context learning approach for interference-aware beam allocation in mmWave vehicular communications, reducing complexity and improving performance.

Findings

MACOL maintains around 10% interference under heavy traffic.

Leveraging neighboring beam information improves interference management.

The approach reduces the need for frequent beam switching.

Abstract

Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.