Distributed Beamforming Techniques for Cell-Free Wireless Networks Using Deep Reinforcement Learning

In a cell-free network, a large number of mobile devices are served simultaneously by several base stations (BSs)/access points(APs) using the same time/frequency resources. However, this creates high signal processing demands (e.g., for beamforming) at the transmitters and receivers. In this work,...

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Veröffentlicht in:	IEEE transactions on cognitive communications and networking Jg. 8; H. 2; S. 1186 - 1201
Hauptverfasser:	Fredj, Firas, Al-Eryani, Yasser, Maghsudi, Setareh, Akrout, Mohamed, Hossain, Ekram
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Array signal processing Beamforming Cell-free network Central processing units Clustering algorithms Complexity Computer architecture CPUs deep deterministic policy gradient algorithm (DDPG) Deep learning deep reinforcement learning (DRL) distributed distributional deterministic policy gradients algorithm (D4PG) Electronic devices Machine learning Probability density function Radio equipment Signal processing Signal processing algorithms successive interference cancellation Transmitters Uplink Uplinking Wireless networks
ISSN:	2332-7731, 2332-7731
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Abstract	In a cell-free network, a large number of mobile devices are served simultaneously by several base stations (BSs)/access points(APs) using the same time/frequency resources. However, this creates high signal processing demands (e.g., for beamforming) at the transmitters and receivers. In this work, we develop centralized and distributed deep reinforcement learning (DRL)-based methods to optimize beamforming at the uplink of a cell-free network. First, we propose a fully centralized uplink beamforming method (i.e., centralized learning) that uses the Deep Deterministic Policy Gradient algorithm (DDPG) for an offline-trained DRL model. We then enhance this method, in terms of convergence and performance, by using distributed experiences collected from different APs based on the Distributed Distributional Deterministic Policy Gradients algorithm (D4PG) in which the APs represent the distributed agents of the DRL model. To reduce the complexity of signal processing at the central processing unit (CPU), we propose a fully distributed DRL-based uplink beamforming scheme. This scheme divides the beamforming computations among distributed APs. The proposed schemes are then benchmarked against two common linear beamforming schemes, namely, minimum mean square estimation (MMSE) and the simplified conjugate symmetric schemes. The results show that the D4PG scheme with distributed experience achieves the best performance irrespective of the network size. Furthermore, although the proposed distributed beamforming technique reduces the complexity of centralized learning in the DDPG algorithm, it performs better than the DDPG algorithm only for small-scale networks. The performance superiority of the fully centralized DDPG model becomes more evident as the number of APs and/or UEs increases. The codes for all of our DRL implementations are available at https://github.com/RayRedd/Distributed_beamforming_rl .
AbstractList	In a cell-free network, a large number of mobile devices are served simultaneously by several base stations (BSs)/access points(APs) using the same time/frequency resources. However, this creates high signal processing demands (e.g., for beamforming) at the transmitters and receivers. In this work, we develop centralized and distributed deep reinforcement learning (DRL)-based methods to optimize beamforming at the uplink of a cell-free network. First, we propose a fully centralized uplink beamforming method (i.e., centralized learning) that uses the Deep Deterministic Policy Gradient algorithm (DDPG) for an offline-trained DRL model. We then enhance this method, in terms of convergence and performance, by using distributed experiences collected from different APs based on the Distributed Distributional Deterministic Policy Gradients algorithm (D4PG) in which the APs represent the distributed agents of the DRL model. To reduce the complexity of signal processing at the central processing unit (CPU), we propose a fully distributed DRL-based uplink beamforming scheme. This scheme divides the beamforming computations among distributed APs. The proposed schemes are then benchmarked against two common linear beamforming schemes, namely, minimum mean square estimation (MMSE) and the simplified conjugate symmetric schemes. The results show that the D4PG scheme with distributed experience achieves the best performance irrespective of the network size. Furthermore, although the proposed distributed beamforming technique reduces the complexity of centralized learning in the DDPG algorithm, it performs better than the DDPG algorithm only for small-scale networks. The performance superiority of the fully centralized DDPG model becomes more evident as the number of APs and/or UEs increases. The codes for all of our DRL implementations are available at https://github.com/RayRedd/Distributed_beamforming_rl .
Author	Al-Eryani, Yasser Akrout, Mohamed Hossain, Ekram Fredj, Firas Maghsudi, Setareh
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SubjectTerms	Algorithms Array signal processing Beamforming Cell-free network Central processing units Clustering algorithms Complexity Computer architecture CPUs deep deterministic policy gradient algorithm (DDPG) Deep learning deep reinforcement learning (DRL) distributed distributional deterministic policy gradients algorithm (D4PG) Electronic devices Machine learning Probability density function Radio equipment Signal processing Signal processing algorithms successive interference cancellation Transmitters Uplink Uplinking Wireless networks
Title	Distributed Beamforming Techniques for Cell-Free Wireless Networks Using Deep Reinforcement Learning
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