Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments

• URL: http://arxiv.org/abs/2107.07423v1
• Date: Tue, 13 Jul 2021 07:30:43 GMT
• Title: Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments
• Authors: Nipuni Ginige, K. B. Shashika Manosha, Nandana Rajatheva, and Matti Latva-aho
• Abstract summary: This paper introduces a deep learning-based, low complexity channel estimator for the RIS-assisted multi-user single-input-multiple-output (SIMO) frequency division multiplexing (OFDM) system. We show that our proposed method has high performance in terms of accuracy and low complexity compared to conventional methods.
• Score: 11.012356843958282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconfigurable intelligent surface (RIS) is an emerging technology for improving performance in fifth-generation (5G) and beyond networks. Practically channel estimation of RIS-assisted systems is challenging due to the passive nature of the RIS. The purpose of this paper is to introduce a deep learning-based, low complexity channel estimator for the RIS-assisted multi-user single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system with hardware impairments. We propose an untrained deep neural network (DNN) based on the deep image prior (DIP) network to denoise the effective channel of the system obtained from the conventional pilot-based least-square (LS) estimation and acquire a more accurate estimation. We have shown that our proposed method has high performance in terms of accuracy and low complexity compared to conventional methods. Further, we have shown that the proposed estimator is robust to interference caused by the hardware impairments at the transceiver and RIS.

Related papers

• Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging [11.867884158309373]
  Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology to enhance the performance of sixth-generation (6G) and beyond communication systems. The passive nature of RISs and their large number of reflecting elements pose challenges to the channel estimation process. We propose an extended channel estimation framework for RIS-assisted multiple-input multiple-output (MIMO) systems based on a convolutional neural network (CNN) integrated with an autoregressive (AR) predictor.
  arXiv  Detail & Related papers  (2024-05-09T19:45:49Z)
• Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO [56.022764337221325]
  Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging. This paper proposes a deep learning (DL)-based rate-splitting multiple access scheme for RIS-aided Tera-Hertz multi-user multiple access systems.
  arXiv  Detail & Related papers  (2022-09-18T03:07:37Z)
• 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)
• Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning [15.444805225936992]
  Reconfigurable intelligent surface (RIS) has become a promising technology to improve the signal transmission quality of wireless communications. However, accurate, low-latency and low-pilot-overhead channel state information (CSI) acquisition remains a considerable challenge in RIS-assisted systems. We propose a three-stage joint channel decomposition and prediction framework to require CSI.
  arXiv  Detail & Related papers  (2021-11-09T07:26:51Z)
• Learning to Estimate RIS-Aided mmWave Channels [50.15279409856091]
  We focus on uplink cascaded channel estimation, where known and fixed base station combining and RIS phase control matrices are considered for collecting observations. To boost the estimation performance and reduce the training overhead, the inherent channel sparsity of mmWave channels is leveraged in the deep unfolding method. It is verified that the proposed deep unfolding network architecture can outperform the least squares (LS) method with a relatively smaller training overhead and online computational complexity.
  arXiv  Detail & Related papers  (2021-07-27T06:57:56Z)
• 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)
• Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces [50.622375361505824]
  Reconfigurable Intelligent Surfaces (RISs) are highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation. One of the major challenges with RIS-empowered wireless communications is the low-overhead dynamic configuration of multiple RISs. We devise low-complexity supervised learning approaches for the RISs' phase configurations.
  arXiv  Detail & Related papers  (2020-10-09T05:35:27Z)
• Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications [35.207416803526876]
  We present two iterative estimation algorithms for the channels between the base station and RIS. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels. We also discuss the downlink achievable sum rate with estimated channels and different precoding schemes for the base station.
  arXiv  Detail & Related papers  (2020-08-04T10:53:51Z)
• Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces [99.34306447202546]
  This paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems. We first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. The complete channel matrix can be reconstructed from the limited measurements based on compressive sensing.
  arXiv  Detail & Related papers  (2020-06-03T12:18:57Z)
• RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design [116.88396201197533]
  A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency. A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
  arXiv  Detail & Related papers  (2020-01-28T14:37:38Z)

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.