- A Deep Reinforcement Learning based Analog Beamforming ...🔍
- Deep Reinforcement Learning|Based On|Off Analog Beamforming ...🔍
- Deep Reinforcement Learning Based Beam Selection for Hybrid ...🔍
- Deep Reinforcement Learning|Based Coordinated Beamforming for ...🔍
- Deep reinforcement learning|based beam training with energy and ...🔍
- Deep Reinforcement Learning|Based Adaptive Beam Tracking and ...🔍
- Deep Reinforcement Learning Based Beamforming Codebook ...🔍
- Deep Reinforcement Learning based Joint Active and Passive ...🔍
A Deep Reinforcement Learning based Analog Beamforming ...
A Deep Reinforcement Learning based Analog Beamforming ...
In this paper, we propose a deep reinforcement learning based distributed analog beamforming approach to improve the energy efficiency for a downlink multiple- ...
Deep Reinforcement Learning-Based On-Off Analog Beamforming ...
In this paper, we address the throughput maximization problem in small cell networks by utilizing low cost on-off analog beamforming coordination.
A Deep Reinforcement Learning based Analog Beamforming ...
References (16) · Energy Efficient Beamforming for Small Cell Systems: A distributed Learning and Multicell Coordination Approach. Article. Sep 2023. Hang Zhou ...
Deep Reinforcement Learning-Based On-Off Analog Beamforming ...
Deep Reinforcement Learning-Based On-off Analog. Beamforming Coordination for Downlink MISO. Networks. Hang Zhou. Ibaraki University. Hitachi, japan. 21nd304t@ ...
Deep Reinforcement Learning-Based On-Off Analog Beamforming ...
Request PDF | On Nov 14, 2023, Hang Zhou and others published Deep Reinforcement Learning-Based On-Off Analog Beamforming Coordination for Downlink MISO ...
Deep Reinforcement Learning Based Beam Selection for Hybrid ...
This paper presents a deep reinforcement learning-based beam-user selection and hybrid beamforming design for the multiuser massive ...
Deep Reinforcement Learning-Based Coordinated Beamforming for ...
... analog beamforming and then transmits the resulting signal. At the receiver end, the received signal is converted to the frequency domain using ...
Deep reinforcement learning-based beam training with energy and ...
The DRL-based beam training approach was initially developed in our previous work in [31], where only one RF chain is used for analog ...
Deep Reinforcement Learning-Based Adaptive Beam Tracking and ...
In this paper, we propose a novel switched beam antenna system model integrated with deep reinforcement learning (DRL) for 6G ...
Deep Reinforcement Learning Based Beamforming Codebook ...
For instance, the proposed MA-DRL approach with only 6 beams outperforms a 256-beam discrete Fourier transform (DFT) codebook with a. 97% beam training overhead ...
Deep Reinforcement Learning based Joint Active and Passive ...
... analog precoder and passive beamformer simultaneously. Then, the digital precoder is determined by minimum mean square error (MMSE) method ...
a Deep Reinforcement Learning approach for 5G NR mmWave ...
For codebook based methods, beam configurations need to be tested by sweeping through all possible combinations of precoders and combiners in the analog ...
Deep reinforcement learning-based beam training with energy and ...
The DRL-based beam training approach was initially developed in our previous work in [31], where only one RF chain is used for analog beamforming and SE is ...
Deep Reinforcement Learning Based Joint Downlink Beamforming ...
We introduce a novel deep reinforcement learning (DRL) approach to jointly optimize transmit beamforming and reconfigurable intelligent surface (RIS) phase ...
Fast MIMO Beamforming via Deep Reinforcement Learning for High ...
Heath, Jr., “Energy-efficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays,” IEEE J. Sel. Areas Commun., vol. 34, no. 4 ...
Dowhuszko, Alexis A. Deep reinforcement learning for hybrid ...
Analog beamforming matrix. The columns of the analog beamforming matrix (i.e., the analog beamforming vectors) are codebook-based, such that.
Hybrid Beamforming for Millimeter Wave Systems Using Deep ...
... analog and digital beamforming (HBF) scheme based on deep reinforcement learning (DRL) to improve the spectral efficiency and reduce system bit error rate ...
Deep Reinforcement Learning Based Beam Selection for Hybrid ...
Deep Reinforcement Learning Based Beam Selection for Hybrid Beamforming and User Grouping in Massive MIMO-NOMA System.
Experience-driven learning-based intelligent hybrid beamforming for ...
... reinforcement learning with traditional optimization techniques to obtain the analog ... deep reinforcement learning-based hybrid beamforming approaches. We used ...
Deep Scanning—Beam Selection Based on Deep Reinforcement ...
A deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station with a large ...