Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning

This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function fr...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhang, Xinran [verfasserIn] Sun, Songlin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Massive MIMO Wireless multicast Beamforming Reinforcement learning

Übergeordnetes Werk:	Enthalten in: Physical communication - Amsterdam [u.a.] : Elsevier, 2008, 32, Seite 81-87
Übergeordnetes Werk:	volume:32 ; pages:81-87

DOI / URN:	10.1016/j.phycom.2018.11.009

Katalog-ID:	ELV001641522

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520			\|a This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy.
650		4	\|a Massive MIMO
650		4	\|a Wireless multicast
650		4	\|a Beamforming
650		4	\|a Reinforcement learning
700	1		\|a Sun, Songlin \|e verfasserin \|4 aut
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publishDate	2018
allfields	10.1016/j.phycom.2018.11.009 doi (DE-627)ELV001641522 (ELSEVIER)S1874-4907(18)30385-9 DE-627 ger DE-627 rda eng 530 620 DE-600 Zhang, Xinran verfasserin aut Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy. Massive MIMO Wireless multicast Beamforming Reinforcement learning Sun, Songlin verfasserin aut Enthalten in Physical communication Amsterdam [u.a.] : Elsevier, 2008 32, Seite 81-87 Online-Ressource (DE-627)573751552 (DE-600)2441929-1 (DE-576)294350721 1876-3219 nnns volume:32 pages:81-87 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 32 81-87
spelling	10.1016/j.phycom.2018.11.009 doi (DE-627)ELV001641522 (ELSEVIER)S1874-4907(18)30385-9 DE-627 ger DE-627 rda eng 530 620 DE-600 Zhang, Xinran verfasserin aut Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy. Massive MIMO Wireless multicast Beamforming Reinforcement learning Sun, Songlin verfasserin aut Enthalten in Physical communication Amsterdam [u.a.] : Elsevier, 2008 32, Seite 81-87 Online-Ressource (DE-627)573751552 (DE-600)2441929-1 (DE-576)294350721 1876-3219 nnns volume:32 pages:81-87 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 32 81-87
allfields_unstemmed	10.1016/j.phycom.2018.11.009 doi (DE-627)ELV001641522 (ELSEVIER)S1874-4907(18)30385-9 DE-627 ger DE-627 rda eng 530 620 DE-600 Zhang, Xinran verfasserin aut Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy. Massive MIMO Wireless multicast Beamforming Reinforcement learning Sun, Songlin verfasserin aut Enthalten in Physical communication Amsterdam [u.a.] : Elsevier, 2008 32, Seite 81-87 Online-Ressource (DE-627)573751552 (DE-600)2441929-1 (DE-576)294350721 1876-3219 nnns volume:32 pages:81-87 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 32 81-87
allfieldsGer	10.1016/j.phycom.2018.11.009 doi (DE-627)ELV001641522 (ELSEVIER)S1874-4907(18)30385-9 DE-627 ger DE-627 rda eng 530 620 DE-600 Zhang, Xinran verfasserin aut Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy. Massive MIMO Wireless multicast Beamforming Reinforcement learning Sun, Songlin verfasserin aut Enthalten in Physical communication Amsterdam [u.a.] : Elsevier, 2008 32, Seite 81-87 Online-Ressource (DE-627)573751552 (DE-600)2441929-1 (DE-576)294350721 1876-3219 nnns volume:32 pages:81-87 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 32 81-87
allfieldsSound	10.1016/j.phycom.2018.11.009 doi (DE-627)ELV001641522 (ELSEVIER)S1874-4907(18)30385-9 DE-627 ger DE-627 rda eng 530 620 DE-600 Zhang, Xinran verfasserin aut Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy. Massive MIMO Wireless multicast Beamforming Reinforcement learning Sun, Songlin verfasserin aut Enthalten in Physical communication Amsterdam [u.a.] : Elsevier, 2008 32, Seite 81-87 Online-Ressource (DE-627)573751552 (DE-600)2441929-1 (DE-576)294350721 1876-3219 nnns volume:32 pages:81-87 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 32 81-87
language	English
source	Enthalten in Physical communication 32, Seite 81-87 volume:32 pages:81-87
sourceStr	Enthalten in Physical communication 32, Seite 81-87 volume:32 pages:81-87
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Massive MIMO Wireless multicast Beamforming Reinforcement learning
dewey-raw	530
isfreeaccess_bool	false
container_title	Physical communication
authorswithroles_txt_mv	Zhang, Xinran @@aut@@ Sun, Songlin @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	573751552
dewey-sort	3530
id	ELV001641522
language_de	englisch
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author	Zhang, Xinran
spellingShingle	Zhang, Xinran ddc 530 misc Massive MIMO misc Wireless multicast misc Beamforming misc Reinforcement learning Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning
authorStr	Zhang, Xinran
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topic_title	530 620 DE-600 Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning Massive MIMO Wireless multicast Beamforming Reinforcement learning
topic	ddc 530 misc Massive MIMO misc Wireless multicast misc Beamforming misc Reinforcement learning
topic_unstemmed	ddc 530 misc Massive MIMO misc Wireless multicast misc Beamforming misc Reinforcement learning
topic_browse	ddc 530 misc Massive MIMO misc Wireless multicast misc Beamforming misc Reinforcement learning
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title	Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning
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title_full	Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning
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author-letter	Zhang, Xinran
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title_sort	delay-aware packet scheduling for massive mimo beamforming transmission using large-scale reinforcement learning
title_auth	Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning
abstract	This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy.
abstractGer	This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy.
abstract_unstemmed	This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy.
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title_short	Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning
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author2	Sun, Songlin
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doi_str	10.1016/j.phycom.2018.11.009
up_date	2024-07-06T22:04:28.181Z
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Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning

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