2019/10/31 by Durgesh Singh, Singh, Durgesh, Arpan Chattopadhyay +3 · 1 citation
Engineering · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #Millimeter-Wave Propagation and Modeling #Networking and Internet Architecture (cs.NI)
paper · pdf · doi:10.48550/arxiv.1910.14367
openalex publication_date 2019/10/31 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Millimeter wave (mmWave) device to device (D2D) communication is highly\nsusceptible to obstacles due to severe penetration losses and requires almost a\nline of sight (LOS) communication path. D2D channel condition is local to\ndevices/user equipments (UEs) and hence is \not directly visible to the\nbase station (BS). Thus quality of the D2D channel needs to be propagated to BS\nby UEs which may incur some delay. Hence the solution provided by BS to UEs\nusing this gathered channel information might become less useful to establish\ncommunication due to moving obstacles. These types of obstacles might not be\nknown in advance and hence may cause unpredictable fluctuations to the D2D\nchannel quality. Hence we seek to learn the D2D channels using the finite\nhorizon partially observable Markov decision process (POMDP) framework to model\nthe uncertainty in such kind of network environments with dynamic obstacles.\nThe objective is to minimize delay when channel quality deteriorates, by making\nUEs choose locally the best possible decision between i) to continue on the\ncurrent relay link on which communication is taking place or ii) to switch to\nanother good relay by exploring other possible UEs in its locality. We derive\nan optimal threshold policy which tells the UE to take appropriate decision\nlocally. Later, we give a simplified and easy to implement stationary threshold\npolicy which counts the number of successive acknowledgement failures, based on\nwhich UE make appropriate decision locally. Through extensive simulation, we\ndemonstrate that our approach outperforms recent algorithms.\n