Efficient Deep Unfolding for SISO-OFDM Channel Estimation
- URL: http://arxiv.org/abs/2210.06588v1
- Date: Tue, 11 Oct 2022 11:29:54 GMT
- Title: Efficient Deep Unfolding for SISO-OFDM Channel Estimation
- Authors: Baptiste Chatelier (IRT b-com, INSA Rennes, IETR), Luc Le Magoarou
(IRT b-com, INSA Rennes, IETR), Getachew Redieteab (IRT b-com)
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In modern communication systems, channel state information is of paramount
importance to achieve capacity. It is then crucial to accurately estimate the
channel. It is possible to perform SISO-OFDM channel estimation using sparse
recovery techniques. However, this approach relies on the use of a physical
wave propagation model to build a dictionary, which requires perfect knowledge
of the system's parameters. In this paper, an unfolded neural network is used
to lighten this constraint. Its architecture, based on a sparse recovery
algorithm, allows SISO-OFDM channel estimation even if the system's parameters
are not perfectly known. Indeed, its unsupervised online learning allows to
learn the system's imperfections in order to enhance the estimation
performance. The practicality of the proposed method is improved with respect
to the state of the art in two aspects: constrained dictionaries are introduced
in order to reduce sample complexity and hierarchical search within
dictionaries is proposed in order to reduce time complexity. Finally, the
performance of the proposed unfolded network is evaluated and compared to
several baselines using realistic channel data, showing the great potential of
the approach.
Related papers
- Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling [9.20186865054847]
Anomaly detection (AD) is increasingly recognized as a key component for ensuring the resilience of future communication systems.
This work considers AD in network flows using incomplete measurements.
We propose a novel block-successive convex approximation algorithm based on a regularized model-fitting objective.
Inspired by Bayesian approaches, we extend the model architecture to perform online adaptation to per-flow and per-time-step statistics.
arXiv Detail & Related papers (2024-09-17T19:59:57Z) - 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) - 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) - 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 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) - LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers [104.01415343139901]
We propose a deep detector entitled LoRD-Net for recovering information symbols from one-bit measurements.
LoRD-Net has a task-based architecture dedicated to recovering the underlying signal of interest.
We evaluate the proposed receiver architecture for one-bit signal recovery in wireless communications.
arXiv Detail & Related papers (2021-02-05T04:26:05Z) - DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator
Search [55.164053971213576]
convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead.
Structured (channel) pruning is usually applied to reduce the model redundancy while preserving the network structure.
Existing structured pruning methods require hand-crafted rules which may lead to tremendous pruning space.
arXiv Detail & Related papers (2020-11-04T07:43:01Z) - Online unsupervised deep unfolding for MIMO channel estimation [0.0]
We propose to perform online learning for channel estimation in a massive context.
This leads to a computationally efficient neural network that can be trained online when with an imperfect model.
It is applied to realistic channels and shows great performance, achieving channel estimation error almost as low as one would get with a perfectly calibrated system.
arXiv Detail & Related papers (2020-04-30T07:32:58Z)
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.