A Sequential Experience-driven Contextual Bandit Policy for MIMO TWAF Online Relay Selection

Ankit Gupta; Mathini Sellathurai; Tharmalingam Ratnarajah

doi:10.1109/SPAWC51304.2022.9834018

A Sequential Experience-driven Contextual Bandit Policy for MIMO TWAF Online Relay Selection

Ankit Gupta, Mathini Sellathurai, Tharmalingam Ratnarajah

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this work, we derive a sequential experience-driven contextual bandit (CB)-based policies for online relay selection in multiple-input multiple-output (MIMO) two-way amplify-and-forward (TWAF) relay networks, where the relays are provided with quantized imperfect channel gain information. The proposed CB-based policy acquires information about the optimal relay node by resolving the exploration-versus-exploitation dilemma. In particular, we propose a linear upper confidence bound (LinUCB)-based CB policy, and an adaptive active greedy (AAG)-based CB policy that utilizes active learning heuristics. With simulation results, we show that the proposed CB-based policies can reduce the feedback overhead by a factor of eight and time-cost by 70% while outperforming the best conventional Gram-Schmidt (GS) algorithm.

Original language	English
Title of host publication	23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication 2022
Publisher	IEEE
ISBN (Electronic)	9781665494557
DOIs	https://doi.org/10.1109/SPAWC51304.2022.9834018
Publication status	Published - 28 Jul 2022
Event	23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication 2022 - Oulu, Finland Duration: 4 Jul 2022 → 6 Jul 2022

Conference

Conference	23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication 2022
Abbreviated title	SPAWC 2022
Country/Territory	Finland
City	Oulu
Period	4/07/22 → 6/07/22

ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Science Applications
Information Systems

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10.1109/SPAWC51304.2022.9834018

Cite this

@inproceedings{1c0eb888c0904d3b95ea3541730b5774,

title = "A Sequential Experience-driven Contextual Bandit Policy for MIMO TWAF Online Relay Selection",

abstract = "In this work, we derive a sequential experience-driven contextual bandit (CB)-based policies for online relay selection in multiple-input multiple-output (MIMO) two-way amplify-and-forward (TWAF) relay networks, where the relays are provided with quantized imperfect channel gain information. The proposed CB-based policy acquires information about the optimal relay node by resolving the exploration-versus-exploitation dilemma. In particular, we propose a linear upper confidence bound (LinUCB)-based CB policy, and an adaptive active greedy (AAG)-based CB policy that utilizes active learning heuristics. With simulation results, we show that the proposed CB-based policies can reduce the feedback overhead by a factor of eight and time-cost by 70% while outperforming the best conventional Gram-Schmidt (GS) algorithm.",

author = "Ankit Gupta and Mathini Sellathurai and Tharmalingam Ratnarajah",

note = "Funding Information: This work was supported by the COG-MHEAR: Towards cognitively-inspired 5G IoT enabled, multi-modal Hearing Aids (https://cogmhear.org) under Grant EP/T021063/1. Publisher Copyright: {\textcopyright} 2022 IEEE.; 23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication 2022, SPAWC 2022 ; Conference date: 04-07-2022 Through 06-07-2022",

year = "2022",

month = jul,

day = "28",

doi = "10.1109/SPAWC51304.2022.9834018",

language = "English",

booktitle = "23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication 2022",

publisher = "IEEE",

address = "United States",

}

Gupta, A, Sellathurai, M & Ratnarajah, T 2022, A Sequential Experience-driven Contextual Bandit Policy for MIMO TWAF Online Relay Selection. in 23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication 2022., 9834018, IEEE, 23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication 2022, Oulu, Finland, 4/07/22. https://doi.org/10.1109/SPAWC51304.2022.9834018

TY - GEN

T1 - A Sequential Experience-driven Contextual Bandit Policy for MIMO TWAF Online Relay Selection

AU - Gupta, Ankit

AU - Sellathurai, Mathini

AU - Ratnarajah, Tharmalingam

N1 - Funding Information: This work was supported by the COG-MHEAR: Towards cognitively-inspired 5G IoT enabled, multi-modal Hearing Aids (https://cogmhear.org) under Grant EP/T021063/1. Publisher Copyright: © 2022 IEEE.

PY - 2022/7/28

Y1 - 2022/7/28

N2 - In this work, we derive a sequential experience-driven contextual bandit (CB)-based policies for online relay selection in multiple-input multiple-output (MIMO) two-way amplify-and-forward (TWAF) relay networks, where the relays are provided with quantized imperfect channel gain information. The proposed CB-based policy acquires information about the optimal relay node by resolving the exploration-versus-exploitation dilemma. In particular, we propose a linear upper confidence bound (LinUCB)-based CB policy, and an adaptive active greedy (AAG)-based CB policy that utilizes active learning heuristics. With simulation results, we show that the proposed CB-based policies can reduce the feedback overhead by a factor of eight and time-cost by 70% while outperforming the best conventional Gram-Schmidt (GS) algorithm.

AB - In this work, we derive a sequential experience-driven contextual bandit (CB)-based policies for online relay selection in multiple-input multiple-output (MIMO) two-way amplify-and-forward (TWAF) relay networks, where the relays are provided with quantized imperfect channel gain information. The proposed CB-based policy acquires information about the optimal relay node by resolving the exploration-versus-exploitation dilemma. In particular, we propose a linear upper confidence bound (LinUCB)-based CB policy, and an adaptive active greedy (AAG)-based CB policy that utilizes active learning heuristics. With simulation results, we show that the proposed CB-based policies can reduce the feedback overhead by a factor of eight and time-cost by 70% while outperforming the best conventional Gram-Schmidt (GS) algorithm.

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U2 - 10.1109/SPAWC51304.2022.9834018

DO - 10.1109/SPAWC51304.2022.9834018

M3 - Conference contribution

AN - SCOPUS:85136080580

BT - 23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication 2022

PB - IEEE

T2 - 23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication 2022

Y2 - 4 July 2022 through 6 July 2022

ER -

A Sequential Experience-driven Contextual Bandit Policy for MIMO TWAF Online Relay Selection

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