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

TL;DR

This paper proposes a POMDP-based relay selection strategy in mmWave D2D communication to mitigate blockage effects from dynamic obstacles, balancing exploration delay and end-to-end latency.

Contribution

It introduces a novel threshold policy for relay selection under uncertainty, reducing relay switching frequency and delay in dynamic obstacle environments.

Findings

The proposed policy effectively balances exploration time and delay.

Simulation results validate the approach's robustness against dynamic obstacles.

The method improves relay stability and reduces call drops.

Abstract

Blockage due to obstacles in millimeter wave (mmWave) device to device (D2D) communication is a prominent problem due to their severe penetration losses. Potential user equipments (UEs) in vicinity of the source UE must be explored in order to select a new relay when the current link gets blocked. However, dynamic obstacles are not known in advance and thus may cause unpredictable fluctuations to D2D channel quality causing newly selected relay link also to be susceptible to blockage. This might cause frequent relay switching leading to call drops and high energy consumption. We have proposed the idea of reducing frequency in relay exploration and switching and thus average end-to-end delay (in seconds) at the expense of additional exploration time units (few milliseconds) during beam alignment. We seek to learn the uncertainty in D2D link qualities by modeling the problem as finite horizon partially observable Markov decision process (POMDP) framework locally at each UE. We have derived an optimal threshold policy which maps the state to set of actions. We then give a simplified and easy to implement stationary threshold policy which counts the number of successive acknowledgment successes/failures for making decisions of selecting or not selecting a given relay locally. Through extensive simulation, we validate our theoretical findings and demonstrate that our approach captures the trade-off between average exploration time and average end-to-end (E2E) delay in presence of dynamic obstacles.