Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

• URL: http://arxiv.org/abs/2205.03636v1
• Date: Sat, 7 May 2022 11:21:19 GMT
• Title: Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
• Authors: Junghoon Kim, Seyyedali Hosseinalipour, Andrew C. Marcum, Taejoon Kim, David J. Love, Christopher G. Brinton
• Abstract summary: We develop a novel adaptive codebook-based limited feedback protocol to control the intelligent reflecting surfaces (IRS) We propose two solutions for adaptive IRS codebook design: (i) random adjacency (RA), which utilizes correlations across the channel realizations, and (ii) deep neural network policy-based IRS control (DPIC) Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by the proposed schemes.
• Score: 26.312293813063558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients. We consider adaptive IRS control in the practical setting where (i) the IRS reflection coefficients are attained by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station (BS) to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not practical in this setting due to the difficulty of channel estimation and the low data rate of the feedback channel. To address these challenges, we develop a novel adaptive codebook-based limited feedback protocol to control the IRS. We propose two solutions for adaptive IRS codebook design: (i) random adjacency (RA), which utilizes correlations across the channel realizations, and (ii) deep neural network policy-based IRS control (DPIC), which is based on a deep reinforcement learning. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by the proposed schemes.

Related papers

• Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications [80.35959154762381]
  Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs. We propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation.
  arXiv  Detail & Related papers  (2023-12-08T01:57:10Z)
• Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments [93.30657979626858]
  We propose a deep actor-critic algorithm that accounts for channel correlations and destination motion. We show that, when channels aretemporally correlated, the inclusion of the SNR in the state representation with function approximation in ways that inhibit convergence.
  arXiv  Detail & Related papers  (2022-11-02T22:07:36Z)
• Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals [25.592568132720157]
  This paper proposes a federated learning (FL) framework to jointly estimate both direct and cascaded channels in IRS-assisted wireless systems. We show that the proposed FL-based channel estimation approach requires approximately 60% fewer pilot signals and it exhibits 12 times lower transmission overhead than CL.
  arXiv  Detail & Related papers  (2022-05-06T13:23:39Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Learning Resilient Radio Resource Management Policies with Graph Neural Networks [124.89036526192268]
  We formulate a resilient radio resource management problem with per-user minimum-capacity constraints. We show that we can parameterize the user selection and power control policies using a finite set of parameters. Thanks to such adaptation, our proposed method achieves a superior tradeoff between the average rate and the 5th percentile rate.
  arXiv  Detail & Related papers  (2022-03-07T19:40:39Z)
• On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems [31.70191055921352]
  We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input single-output (MISO) system for downlink transmission. We compare the performance of Deep Reinforcement Learning (DRL) and conventional optimization methods in finding optimal phase shifts of the IRS elements. We demonstrate numerically that DRL solutions show more robustness to noisy channels and user mobility.
  arXiv  Detail & Related papers  (2021-07-17T17:42:25Z)
• Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA [6.3510536583205255]
  We show that IRS assisted NOMA based on our DRL scheme achieves the capability high sum rate compared to OMA based one, and as the power increases, the serving more users increases.
  arXiv  Detail & Related papers  (2021-04-03T14:10:40Z)
• Deep Learning-based Phase Reconfiguration for Intelligent Reflecting Surfaces [0.0]
  We present a deep learning (DL) approach for phase reconfiguration at an IRS in order to learn and make use of the local propagation environment. The proposed method uses the received pilot signals reflected through the IRS to train the deep feedforward network.
  arXiv  Detail & Related papers  (2020-09-29T13:18:24Z)
• A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces [22.51807198305316]
  Intelligent reflecting surfaces (IRSs) have recently received significant attention for wireless communications. Data-driven techniques, such as deep learning (DL), are critical in addressing these challenges. This article provides a synopsis of these techniques for designing DL-based IRS-assisted wireless systems.
  arXiv  Detail & Related papers  (2020-09-05T14:26:48Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)
• Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning [119.97450366894718]
  A novel framework is proposed to optimize the downlink multi-user communication of a millimeter wave base station. A channel estimation approach is developed to measure the channel state information (CSI) in real-time. A distributional reinforcement learning (DRL) approach is proposed to learn the optimal IR reflection and maximize the expectation of downlink capacity.
  arXiv  Detail & Related papers  (2020-02-24T22:18:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.