Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach

• URL: http://arxiv.org/abs/2308.13616v2
• Date: Sat, 16 Dec 2023 18:36:19 GMT
• Title: Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach
• Authors: Firas Fredj, Amal Feriani, Amine Mezghani, Ekram Hossain
• Abstract summary: We study the separate channel estimation problem in a fully passive RIS-aided communication system. First, we adopt a variational-inference (VI) approach to jointly estimate the UE-RIS and RIS-BS instantaneous channel state information (I-CSI) Second, we extend our first approach to estimate the slow-varying statistical CSI of the UE-RIS link overcoming the highly variant I-CSI.
• Score: 19.748435313030566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Channel estimation in reconfigurable intelligent surfaces (RIS)-aided systems is crucial for optimal configuration of the RIS and various downstream tasks such as user localization. In RIS-aided systems, channel estimation involves estimating two channels for the user-RIS (UE-RIS) and RIS-base station (RIS-BS) links. In the literature, two approaches are proposed: (i) cascaded channel estimation where the two channels are collapsed into a single one and estimated using training signals at the BS, and (ii) separate channel estimation that estimates each channel separately either in a passive or semi-passive RIS setting. In this work, we study the separate channel estimation problem in a fully passive RIS-aided millimeter-wave (mmWave) single-user single-input multiple-output (SIMO) communication system. First, we adopt a variational-inference (VI) approach to jointly estimate the UE-RIS and RIS-BS instantaneous channel state information (I-CSI). In particular, auxiliary posterior distributions of the I-CSI are learned through the maximization of the evidence lower bound. However, estimating the I-CSI for both links in every coherence block results in a high signaling overhead to control the RIS in scenarios with highly mobile users. Thus, we extend our first approach to estimate the slow-varying statistical CSI of the UE-RIS link overcoming the highly variant I-CSI. Precisely, our second method estimates the I-CSI of RIS-BS channel and the UE-RIS channel covariance matrix (CCM) directly from the uplink training signals in a fully passive RIS-aided system. The simulation results demonstrate that using maximum a posteriori channel estimation using the auxiliary posteriors can provide a capacity that approaches the capacity with perfect CSI.

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)
• Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System [32.74137740936128]
  This paper proposes a practical channel estimation approach for the first time to an IRS-assisted multiuser ISAC system. A two-stage approach is proposed to transfer the overall estimation problem into sub-ones. Considering a low-cost demand of the ISAC BS and downlink users, the proposed two-stage approach is realized by an efficient neural network (NN) framework.
  arXiv  Detail & Related papers  (2024-01-29T14:15:11Z)
• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• Spatial Processing Front-End For Distant ASR Exploiting Self-Attention Channel Combinator [11.248169478873344]
  We present a novel multi-channel front-end based on channel shortening with theWeighted Prediction Error (WPE) method. We show that the proposed system used as part of a ContextNet based end-to-end (E2E) ASR system outperforms leading ASR systems.
  arXiv  Detail & Related papers  (2022-03-25T21:43:15Z)
• 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 Perform Downlink Channel Estimation in Massive MIMO Systems [72.76968022465469]
  We study downlink (DL) channel estimation in a Massive multiple-input multiple-output (MIMO) system. A common approach is to use the mean value as the estimate, motivated by channel hardening. We propose two novel estimation methods.
  arXiv  Detail & Related papers  (2021-09-06T13:42:32Z)
• 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)
• Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems [61.78590389147475]
  This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for millimeter-wave (mmWave) systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains, we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-04-22T13:34:53Z)
• 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)
• 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.