Sensing-Assisted Adaptive Channel Contention for Mobile Delay-Sensitive Communications

TL;DR

This paper introduces an adaptive, sensing-assisted channel contention mechanism for mmWave uplink systems, optimizing queuing performance through decentralized policies learned via stochastic gradient methods.

Contribution

It presents a novel decentralized multi-agent MDP framework with environment sensing for adaptive channel contention in mobile mmWave systems, optimized using efficient stochastic gradient techniques.

Findings

Significant improvement in queuing performance with the proposed method

Efficient stochastic gradient estimation outperforms SPSA

Effective environment and mobility sensing enhances channel contention decisions

Abstract

This paper proposes an adaptive channel contention mechanism to optimize the queuing performance of a distributed millimeter wave (mmWave) uplink system with the capability of environment and mobility sensing. The mobile agents determine their back-off timer parameters according to their local knowledge of the uplink queue lengths, channel quality, and future channel statistics, where the channel prediction relies on the environment and mobility sensing. The optimization of queuing performance with this adaptive channel contention mechanism is formulated as a decentralized multi-agent Markov decision process (MDP). Although the channel contention actions are determined locally at the mobile agents, the optimization of local channel contention policies of all mobile agents is conducted in a centralized manner according to the system statistics before the scheduling. In the solution, the local policies are approximated by analytical models, and the optimization of their parameters becomes a stochastic optimization problem along an adaptive Markov chain. An unbiased gradient estimation is proposed so that the local policies can be optimized efficiently via the stochastic gradient descent method. It is demonstrated by simulation that the proposed gradient estimation is significantly more efficient in optimization than the existing methods, e.g., simultaneous perturbation stochastic approximation (SPSA).