Distributed Relay Selection in Presence of Dynamic Obstacles in Millimeter Wave D2D Communication

TL;DR

This paper proposes a POMDP-based method for relay selection in mmWave D2D communication with dynamic obstacles, enabling UEs to make local decisions to minimize delays and improve reliability.

Contribution

It introduces a novel POMDP framework and a practical threshold policy for relay selection under obstacle-induced channel uncertainty in mmWave D2D networks.

Findings

The proposed approach outperforms recent algorithms in simulations.

A simplified threshold policy achieves near-optimal performance.

The method effectively adapts to dynamic obstacle conditions.

Abstract

Millimeter wave (mmWave) device to device (D2D) communication is highly susceptible to obstacles due to severe penetration losses and requires almost a line of sight (LOS) communication path. D2D channel condition is local to devices/user equipments (UEs) and hence is \textit{not} directly visible to the base station (BS). Thus quality of the D2D channel needs to be propagated to BS by UEs which may incur some delay. Hence the solution provided by BS to UEs using this gathered channel information might become less useful to establish communication due to moving obstacles. These types of obstacles might not be known in advance and hence may cause unpredictable fluctuations to the D2D channel quality. Hence we seek to learn the D2D channels using the finite horizon partially observable Markov decision process (POMDP) framework to model the uncertainty in such kind of network environments with dynamic obstacles. The objective is to minimize delay when channel quality deteriorates, by making UEs choose locally the best possible decision between i) to continue on the current relay link on which communication is taking place or ii) to switch to another good relay by exploring other possible UEs in its locality. We derive an optimal threshold policy which tells the UE to take appropriate decision locally. Later, we give a simplified and easy to implement stationary threshold policy which counts the number of successive acknowledgement failures, based on which UE make appropriate decision locally. Through extensive simulation, we demonstrate that our approach outperforms recent algorithms.