Hybrid Centralized-Distributed Resource Allocation Based on Deep Reinforcement Learning for Cooperative D2D Communications

Device-to-device (D2D) technology enables direct communication between adjacent devices within cellular networks. Due to its high data rate, low latency, and performance improvement in spectrum and energy efficiency, it has been widely investigated and applied as a critical technology in 5G New Radi...

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Veröffentlicht in:IEEE access Jg. 12; S. 196609 - 196623
Hauptverfasser: Yu, Yang, Tang, Xiaoqing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Cellular communication
Cellular networks
Clean energy
Communication
Cooperative communication
Cooperative control
Copper
Deep learning
deep reinforcement learning
Device-to-device communication
Distributed processing
Energy efficiency
Graph matching
Graph theory
Heuristic algorithms
Interference
Mathematical programming
Mixed integer
Network latency
Optimization
Power control
Power management
Relays
Resource allocation
Resource management
Signaling
Spectrum allocation
Sustainable development
Wireless communication
ISSN:2169-3536, 2169-3536
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Abstract Device-to-device (D2D) technology enables direct communication between adjacent devices within cellular networks. Due to its high data rate, low latency, and performance improvement in spectrum and energy efficiency, it has been widely investigated and applied as a critical technology in 5G New Radio (NR). Cooperative D2D communication can achieve a win-win situation between cellular users (CUs) and D2D users (DUs) through cooperative relaying techniques. In addition to conventional overlay and underlay D2D communications, it has attracted extensive attention from academic and industrial circles in the past decade. This paper delves into optimizing joint spectrum allocation, power control, and link-matching between multiple CUs and DUs for cooperative D2D communications. Weighted sum energy efficiency (WSEE) is used as the performance metric to address the challenges of green communication and sustainable development. This mixed-integer fractional programming (MIFP) problem can be decomposed into: 1. a classic weighted bipartite graph matching; 2. a series of nonconvex spectrum allocation and power control problems between potentially matched cellular and D2D link pairs. To address this issue, we propose a hybrid centralized-distributed scheme based on deep reinforcement learning (DRL) and the Kuhn-Munkres (KM) algorithm. Leveraging the former, the CUs and DUs autonomously optimize spectrum allocation and power control by only utilizing local information. Then, the base station (BS) determines the link matching utilizing the latter. Simulation results reveal that it achieves more than 96% WSEE of the optimal scheme and 98% WSEE of the centralized DRL-based scheme. It significantly enhances the network convergence speed with low centralized computational overheads. In addition, we also propose and utilize cooperative link sets for corresponding D2D links to accelerate the proposed scheme and reduce signaling exchange further: an average of about 85% WSEE of the optimal scheme is achieved, while more than 50% of signaling and distributed computing overheads are reduced.
AbstractList Device-to-device (D2D) technology enables direct communication between adjacent devices within cellular networks. Due to its high data rate, low latency, and performance improvement in spectrum and energy efficiency, it has been widely investigated and applied as a critical technology in 5G New Radio (NR). Cooperative D2D communication can achieve a win-win situation between cellular users (CUs) and D2D users (DUs) through cooperative relaying techniques. In addition to conventional overlay and underlay D2D communications, it has attracted extensive attention from academic and industrial circles in the past decade. This paper delves into optimizing joint spectrum allocation, power control, and link-matching between multiple CUs and DUs for cooperative D2D communications. Weighted sum energy efficiency (WSEE) is used as the performance metric to address the challenges of green communication and sustainable development. This mixed-integer fractional programming (MIFP) problem can be decomposed into: 1. a classic weighted bipartite graph matching; 2. a series of nonconvex spectrum allocation and power control problems between potentially matched cellular and D2D link pairs. To address this issue, we propose a hybrid centralized-distributed scheme based on deep reinforcement learning (DRL) and the Kuhn-Munkres (KM) algorithm. Leveraging the former, the CUs and DUs autonomously optimize spectrum allocation and power control by only utilizing local information. Then, the base station (BS) determines the link matching utilizing the latter. Simulation results reveal that it achieves more than 96% WSEE of the optimal scheme and 98% WSEE of the centralized DRL-based scheme. It significantly enhances the network convergence speed with low centralized computational overheads. In addition, we also propose and utilize cooperative link sets for corresponding D2D links to accelerate the proposed scheme and reduce signaling exchange further: an average of about 85% WSEE of the optimal scheme is achieved, while more than 50% of signaling and distributed computing overheads are reduced.
Author Tang, Xiaoqing
Yu, Yang
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10.1109/TVT.2019.2897134
10.1109/LCOMM.2014.2351400
10.1109/TIT.2004.838089
10.1007/978-94-009-0035-6
10.1109/TVT.2023.3276647
10.1109/TGCN.2020.3036026
10.3390/app7050491
10.1109/JSAC.2023.3288261
10.1109/JSAC.2019.2933973
10.1109/LWC.2022.3170998
10.1016/j.comnet.2017.06.002
10.1109/mwc.2014.6757899
10.1109/LCOMM.2020.2984430
10.1109/comst.2014.2319555
10.1109/LWC.2018.2864644
10.1109/TVT.2023.3295821
10.1109/TNSE.2019.2917071
10.1109/TWC.2020.3001736
10.1109/comst.2018.2828120
10.1109/MWC.2015.7143335
10.1109/TWC.2017.2698032
10.1109/TCCN.2018.2809722
10.1109/LCOMM.2023.3292816
10.1109/TSP.2018.2812733
10.1002/9781119307600.ch1
10.1109/MWC.009.2300501
10.1561/0100000088
10.1109/MCOM.2017.1700410
10.1109/TMC.2012.231
10.1109/TVT.2015.2511924
10.1109/TII.2021.3139349
10.1109/ACCESS.2024.3447471
10.1109/COMST.2019.2916583
10.1109/ACCESS.2021.3090855
10.1109/JIOT.2022.3189445
10.1109/LWC.2020.3035898
10.1109/TWC.2021.3138733
10.1109/MWC.013.2300259
10.1038/nature14236
10.1109/TWC.2014.2360202
10.1109/MCOM.2017.1600271
10.1109/JCN.2016.000082
10.1109/TWC.2016.2646360
10.1109/ACCESS.2017.2669403
10.1109/TVT.2020.3046747
10.1109/lcomm.2020.3042490
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References ref13
ref12
Mnih (ref24) 2013
ref15
ref14
ref53
ref11
ref10
ref17
ref16
ref19
ref18
ref51
ref50
ref46
ref47
ref42
ref41
ref44
ref43
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref40
ref35
ref34
Liu (ref39) 2024
ref37
ref36
ref31
ref30
ref33
ref32
Rockafellar (ref45) 1997
ref2
ref1
ref38
ref23
(ref52) 2024
ref26
ref25
Achiam (ref49) 2017
ref20
ref22
ref21
Liang (ref48) 2018
ref28
ref27
ref29
References_xml – ident: ref15
  doi: 10.1109/TNET.2022.3152150
– ident: ref25
  doi: 10.1109/TVT.2019.2897134
– ident: ref50
  doi: 10.1109/LCOMM.2014.2351400
– ident: ref7
  doi: 10.1109/TIT.2004.838089
– ident: ref41
  doi: 10.1007/978-94-009-0035-6
– ident: ref27
  doi: 10.1109/TVT.2023.3276647
– ident: ref11
  doi: 10.1109/TGCN.2020.3036026
– ident: ref51
  doi: 10.3390/app7050491
– ident: ref17
  doi: 10.1109/JSAC.2023.3288261
– ident: ref32
  doi: 10.1109/JSAC.2019.2933973
– ident: ref35
  doi: 10.1109/LWC.2022.3170998
– volume-title: NR; User Equipment (UE) Radio Transmission and Reception; Part 1: Range 1 Standalone, v18.7.0
  year: 2024
  ident: ref52
– ident: ref23
  doi: 10.1016/j.comnet.2017.06.002
– ident: ref3
  doi: 10.1109/mwc.2014.6757899
– ident: ref36
  doi: 10.1109/LCOMM.2020.2984430
– ident: ref4
  doi: 10.1109/comst.2014.2319555
– year: 2018
  ident: ref48
  article-title: Accelerated primal-dual policy optimization for safe reinforcement learning
  publication-title: arXiv:1802.06480
– ident: ref43
  doi: 10.1109/LWC.2018.2864644
– ident: ref14
  doi: 10.1109/TVT.2023.3295821
– ident: ref13
  doi: 10.1109/TNSE.2019.2917071
– ident: ref33
  doi: 10.1109/TWC.2020.3001736
– ident: ref2
  doi: 10.1109/comst.2018.2828120
– ident: ref6
  doi: 10.1109/MWC.2015.7143335
– ident: ref8
  doi: 10.1109/TWC.2017.2698032
– ident: ref30
  doi: 10.1109/TCCN.2018.2809722
– ident: ref38
  doi: 10.1109/LCOMM.2023.3292816
– ident: ref42
  doi: 10.1109/TSP.2018.2812733
– ident: ref1
  doi: 10.1002/9781119307600.ch1
– year: 2013
  ident: ref24
  article-title: Playing Atari with deep reinforcement learning
  publication-title: arXiv:1312.5602
– ident: ref19
  doi: 10.1109/MWC.009.2300501
– ident: ref40
  doi: 10.1561/0100000088
– ident: ref18
  doi: 10.1109/MCOM.2017.1700410
– ident: ref12
  doi: 10.1109/TMC.2012.231
– ident: ref21
  doi: 10.1109/TVT.2015.2511924
– volume-title: Convex Analysis
  year: 1997
  ident: ref45
– ident: ref31
  doi: 10.1109/TII.2021.3139349
– ident: ref34
  doi: 10.1109/ACCESS.2024.3447471
– ident: ref29
  doi: 10.1109/COMST.2019.2916583
– ident: ref46
  doi: 10.1109/ACCESS.2021.3090855
– ident: ref37
  doi: 10.1109/JIOT.2022.3189445
– ident: ref26
  doi: 10.1109/LWC.2020.3035898
– ident: ref44
  doi: 10.1109/TWC.2021.3138733
– ident: ref16
  doi: 10.1109/MWC.013.2300259
– ident: ref28
  doi: 10.1038/nature14236
– year: 2017
  ident: ref49
  article-title: Constrained policy optimization
  publication-title: arXiv:1705.10528
– ident: ref5
  doi: 10.1109/TWC.2014.2360202
– ident: ref20
  doi: 10.1109/MCOM.2017.1600271
– year: 2024
  ident: ref39
  article-title: Multi-user MISO with stacked intelligent metasurfaces: A DRL-based sum-rate optimization approach
  publication-title: arXiv:2408.04837
– ident: ref10
  doi: 10.1109/JCN.2016.000082
– ident: ref22
  doi: 10.1109/TWC.2016.2646360
– ident: ref47
  doi: 10.1109/ACCESS.2017.2669403
– ident: ref9
  doi: 10.1109/TVT.2020.3046747
– ident: ref53
  doi: 10.1109/lcomm.2020.3042490
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Snippet Device-to-device (D2D) technology enables direct communication between adjacent devices within cellular networks. Due to its high data rate, low latency, and...
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SubjectTerms Algorithms
Cellular communication
Cellular networks
Clean energy
Communication
Cooperative communication
Cooperative control
Copper
Deep learning
deep reinforcement learning
Device-to-device communication
Distributed processing
Energy efficiency
Graph matching
Graph theory
Heuristic algorithms
Interference
Mathematical programming
Mixed integer
Network latency
Optimization
Power control
Power management
Relays
Resource allocation
Resource management
Signaling
Spectrum allocation
Sustainable development
Wireless communication
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Title Hybrid Centralized-Distributed Resource Allocation Based on Deep Reinforcement Learning for Cooperative D2D Communications
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