Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2101.10300v5
• Date: Thu, 28 Mar 2024 03:47:39 GMT
• Title: Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach
• Authors: Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim, Christopher G. Brinton, David J. Love,
• Abstract summary: We present a frequency-domain denoising method based on a reinforcement learning framework. Our algorithm provides a significant improvement over the practical least squares (LS) estimation method.
• Score: 23.57305243608369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In general, reliable communication via multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) requires accurate channel estimation at the receiver. The existing literature largely focuses on denoising methods for channel estimation that depend on either (i) channel analysis in the time-domain with prior channel knowledge or (ii) supervised learning techniques which require large pre-labeled datasets for training. To address these limitations, we present a frequency-domain denoising method based on a reinforcement learning framework that does not need a priori channel knowledge and pre-labeled data. Our methodology includes a new successive channel denoising process based on channel curvature computation, for which we obtain a channel curvature magnitude threshold to identify unreliable channel estimates. Based on this process, we formulate the denoising mechanism as a Markov decision process, where we define the actions through a geometry-based channel estimation update, and the reward function based on a policy that reduces mean squared error (MSE). We then resort to Q-learning to update the channel estimates. Numerical results verify that our denoising algorithm can successfully mitigate noise in channel estimates. In particular, our algorithm provides a significant improvement over the practical least squares (LS) estimation method and provides performance that approaches that of the ideal linear minimum mean square error (LMMSE) estimation with perfect knowledge of channel statistics.

Related papers

• Direct Unsupervised Denoising [60.71146161035649]
  Unsupervised denoisers do not directly produce a single prediction, such as the MMSE estimate. We present an alternative approach that trains a deterministic network alongside the VAE to directly predict a central tendency.
  arXiv  Detail & Related papers  (2023-10-27T13:02:12Z)
• 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)
• An Efficient Machine Learning-based Channel Prediction Technique for OFDM Sub-Bands [0.0]
  We propose an efficient machine learning (ML)-based technique for channel prediction in OFDM sub-bands. The novelty of the proposed approach lies in the training of channel fading samples used to estimate future channel behaviour in selective fading.
  arXiv  Detail & Related papers  (2023-05-31T09:41:27Z)
• Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots [41.213515826100696]
  We propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods.
  arXiv  Detail & Related papers  (2023-03-02T05:34:25Z)
• Efficient Deep Unfolding for SISO-OFDM Channel Estimation [0.0]
  It is possible to perform SISO-OFDM channel estimation using sparse recovery techniques. In this paper, an unfolded neural network is used to lighten this constraint. Its unsupervised online learning allows to learn the system's imperfections in order to enhance the estimation performance.
  arXiv  Detail & Related papers  (2022-10-11T11:29:54Z)
• Channel Estimation under Hardware Impairments: Bayesian Methods versus Deep Learning [2.055949720959582]
  A deep feedforward neural network is designed and trained to estimate the effective channels. Its performance is compared with state-of-the-art distortion-aware and unaware Bayesian linear minimum mean-squared error (LMMSE) estimators.
  arXiv  Detail & Related papers  (2022-08-08T10:32:32Z)
• 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)
• Deep Networks for Direction-of-Arrival Estimation in Low SNR [89.45026632977456]
  We introduce a Convolutional Neural Network (CNN) that is trained from mutli-channel data of the true array manifold matrix. We train a CNN in the low-SNR regime to predict DoAs across all SNRs. Our robust solution can be applied in several fields, ranging from wireless array sensors to acoustic microphones or sonars.
  arXiv  Detail & Related papers  (2020-11-17T12:52:18Z)
• 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)
• Data-Driven Symbol Detection via Model-Based Machine Learning [117.58188185409904]
  We review a data-driven framework to symbol detection design which combines machine learning (ML) and model-based algorithms. In this hybrid approach, well-known channel-model-based algorithms are augmented with ML-based algorithms to remove their channel-model-dependence. Our results demonstrate that these techniques can yield near-optimal performance of model-based algorithms without knowing the exact channel input-output statistical relationship.
  arXiv  Detail & Related papers  (2020-02-14T06:58:27Z)

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.