Power Efficient Resource Allocation for ISAC: Combing Lyapunov Optimization and DRL

Radar and wireless communication systems are two important applications of modern electromagnetic theory. However, the serious shortage of spectrum resources in recent years poses a significant challenge for the development of both systems. Integrated sensing and communication (ISAC) has emerged as...

Full description

Saved in:
Bibliographic Details
Published in:2023 IEEE Globecom Workshops (GC Wkshps) pp. 1135 - 1140
Main Authors: Wang, Haodong, Wang, Zifan, Chen, Yawen, Lu, Zhaoming, Wen, Xiangming
Format: Conference Proceeding
Language:English
Published: IEEE 04.12.2023
Subjects:
Computational modeling
Deep Reinforcement Learning
Integrated Sensing And Communication
Lyapunov Optimization
Network Stability
Power demand
Radar detection
Resource Allocation
Simulation
Stability analysis
Wireless communication
Wireless sensor networks
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Radar and wireless communication systems are two important applications of modern electromagnetic theory. However, the serious shortage of spectrum resources in recent years poses a significant challenge for the development of both systems. Integrated sensing and communication (ISAC) has emerged as a promising solution to address this issue. Due to shared infrastructure and spectrum, optimal resource allocation is essential to meet the quality requirements of both sensing and communication, while maximizing the system efficiency. This paper presents a combinatorial online optimization framework based on Lyapunov theory and deep reinforcement learning (DRL) for a typical ISAC system with time-varying wireless channels and random user arrivals. The framework aims to minimize power consumption while ensuring the performance of both communication and radar detection. It first applies Lyapunov optimization to decouple a long-run stochastic mixed-integer nonlinear programming problem into deterministic subproblems in each time frame. Subsequently, it combines model-based optimization with model-free DRL to solve these subproblems. Simulation results demonstrate that the proposed framework can stabilize the data backlog queue within a short computation time and significantly reduce power consumption.
AbstractList Radar and wireless communication systems are two important applications of modern electromagnetic theory. However, the serious shortage of spectrum resources in recent years poses a significant challenge for the development of both systems. Integrated sensing and communication (ISAC) has emerged as a promising solution to address this issue. Due to shared infrastructure and spectrum, optimal resource allocation is essential to meet the quality requirements of both sensing and communication, while maximizing the system efficiency. This paper presents a combinatorial online optimization framework based on Lyapunov theory and deep reinforcement learning (DRL) for a typical ISAC system with time-varying wireless channels and random user arrivals. The framework aims to minimize power consumption while ensuring the performance of both communication and radar detection. It first applies Lyapunov optimization to decouple a long-run stochastic mixed-integer nonlinear programming problem into deterministic subproblems in each time frame. Subsequently, it combines model-based optimization with model-free DRL to solve these subproblems. Simulation results demonstrate that the proposed framework can stabilize the data backlog queue within a short computation time and significantly reduce power consumption.
Author Wang, Haodong
Lu, Zhaoming
Chen, Yawen
Wang, Zifan
Wen, Xiangming
Author_xml – sequence: 1
  givenname: Haodong
  surname: Wang
  fullname: Wang, Haodong
  organization: School of Information and Communications, Beijing University of Posts and Telecommunications,Beijing Laboratory of Advanced Information Networks,Beijing,China
– sequence: 2
  givenname: Zifan
  surname: Wang
  fullname: Wang, Zifan
  organization: School of Information and Communications, Beijing University of Posts and Telecommunications,Beijing Laboratory of Advanced Information Networks,Beijing,China
– sequence: 3
  givenname: Yawen
  surname: Chen
  fullname: Chen, Yawen
  organization: School of Information and Communications, Beijing University of Posts and Telecommunications,Beijing Laboratory of Advanced Information Networks,Beijing,China
– sequence: 4
  givenname: Zhaoming
  surname: Lu
  fullname: Lu, Zhaoming
  organization: School of Information and Communications, Beijing University of Posts and Telecommunications,Beijing Laboratory of Advanced Information Networks,Beijing,China
– sequence: 5
  givenname: Xiangming
  surname: Wen
  fullname: Wen, Xiangming
  organization: School of Information and Communications, Beijing University of Posts and Telecommunications,Beijing Laboratory of Advanced Information Networks,Beijing,China
BookMark eNo1j0FLwzAYQCPoQef-gYeA59akSdrEW6lzDgqTTfE40vT7NNglpe2U-es9TE_v8njwrsh5iAEIueUs5ZyZu2X19jl-9KPSWoo0Y5lIOZO5VEackbkpjBaKiYJlXF-S7XP8hoEuEL3zECa6gTEeBge07Lro7ORjoBgHutqW1T2t4r7x4Z3WR9sfQvyi637ye_9z8mxo6cOmviYXaLsR5n-ckdfHxUv1lNTr5aoq68RzbqYkt22RN1ggl7LVgJnkslGaWeFQI9OgG4bolGKAMs9AcnSgjW10bsEJJ2bk5tT1ALDrB7-3w3H3_yp-AbBvUSs
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/GCWkshps58843.2023.10464593
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9798350370218
EndPage 1140
ExternalDocumentID 10464593
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i119t-6ad76bf7f144d8ef2414b580a3cf8f08e8b0ffc550ef462e41fce89ab86aec3c3
IEDL.DBID RIE
IngestDate Wed May 01 11:49:06 EDT 2024
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i119t-6ad76bf7f144d8ef2414b580a3cf8f08e8b0ffc550ef462e41fce89ab86aec3c3
PageCount 6
ParticipantIDs ieee_primary_10464593
PublicationCentury 2000
PublicationDate 2023-Dec.-4
PublicationDateYYYYMMDD 2023-12-04
PublicationDate_xml – month: 12
  year: 2023
  text: 2023-Dec.-4
  day: 04
PublicationDecade 2020
PublicationTitle 2023 IEEE Globecom Workshops (GC Wkshps)
PublicationTitleAbbrev GC WKSHPS
PublicationYear 2023
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.8824418
Snippet Radar and wireless communication systems are two important applications of modern electromagnetic theory. However, the serious shortage of spectrum resources...
SourceID ieee
SourceType Publisher
StartPage 1135
SubjectTerms Computational modeling
Deep Reinforcement Learning
Integrated Sensing And Communication
Lyapunov Optimization
Network Stability
Power demand
Radar detection
Resource Allocation
Simulation
Stability analysis
Wireless communication
Wireless sensor networks
Title Power Efficient Resource Allocation for ISAC: Combing Lyapunov Optimization and DRL
URI https://ieeexplore.ieee.org/document/10464593
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxFHzYIuJJxYrfBPS67W6T3WS9ldqqUGqxir2VbPKiRd2WfoH_3iTdKh48eAuBEDIhmbwk8wbgknOKKdM6EEnMA8ZVFEihdJBIZdmfxRmnxptN8G5XDAZprxCrey0MIvrPZ1h1Rf-Wr8dq4a7KapF_h0tpCUqcJyux1hZcFHkzazfN57fZ62TmtJe06nzBq-sWv7xTPHW0d_7Z6S5UfkR4pPdNL3uwgfk-9HvO1Yy0fOIH246sr99J493RkoOZ2HMoues3mlfErnYb-b6QzqecLPLxktzbHeKjkF4SmWty_dCpwFO79di8DQpfhGAURenc4qh5khlubDCkBRpLwiyLRSipMsKEAkUWGqNs7IGGJXVkkVEoUpmJRKKiih5AOR_neAikLilqbUcn3dkuU0KbugyFtkFiFCOnR1BxiAwnq9QXwzUYx3_Un8C2w93_92CnUJ5PF3gGm2o5H82m537CvgAuQpmU
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NTwMhECVajXpSY43fkuh1292FXVhvTW1tY62NrdFbw8Kgjbpt-pX47wW61Xjw4I2QEMIQeDMDbx5Cl4wRSKhSHo8j5lEmA09wqbxYSIP-NEoZ0U5sgrXb_Pk56eRkdceFAQD3-QxKtune8tVQzmyqrBy4d7iErKK1iNLQX9C1NtBFXjmzfFN9epu8jiaWfUlKVhm8tBzzSz3FgUd9-5_T7qDiDw0Pd74BZhetQLaHuh2ra4ZrrvSDGYeXCXhcebfAZA2NjSeKm91K9Qqb825i3xfc-hSjWTac43tzR3zk5EssMoWvH1pF9Fiv9aoNL1dG8AZBkEyNJRWLU820CYcUB21gmKYR9wWRmmufA099raWJPkDTOAQaaAk8ESmPBUgiyT4qZMMMDhAOBQGlzOqE9e5SyZUOhc-VCRODCBg5REVrkf5oUfyivzTG0R_952iz0btr9VvN9u0x2rJ74H5_0BNUmI5ncIrW5Xw6mIzP3OZ9AVlRnNs
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2023+IEEE+Globecom+Workshops+%28GC+Wkshps%29&rft.atitle=Power+Efficient+Resource+Allocation+for+ISAC%3A+Combing+Lyapunov+Optimization+and+DRL&rft.au=Wang%2C+Haodong&rft.au=Wang%2C+Zifan&rft.au=Chen%2C+Yawen&rft.au=Lu%2C+Zhaoming&rft.date=2023-12-04&rft.pub=IEEE&rft.spage=1135&rft.epage=1140&rft_id=info:doi/10.1109%2FGCWkshps58843.2023.10464593&rft.externalDocID=10464593