Transfer Learning Capabilities of Untrained Neural Networks for MIMO CSI Recreation

• URL: http://arxiv.org/abs/2111.07858v1
• Date: Mon, 15 Nov 2021 16:01:45 GMT
• Title: Transfer Learning Capabilities of Untrained Neural Networks for MIMO CSI Recreation
• Authors: Brenda Vilas Boas and Wolfgang Zirwas and Martin Haardt
• Abstract summary: One of the biggest challenges for real world Machine learning (ML) deployment is the need for labeled signals and big measurement campaigns. To overcome those problems, we propose the use of untrained neural networks (UNNs) UNNs learn the propagation environment by fitting a few channel measurements and we exploit their learned prior to provide higher channel estimation gains.
• Score: 19.0581196881206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning (ML) applications for wireless communications have gained momentum on the standardization discussions for 5G advanced and beyond. One of the biggest challenges for real world ML deployment is the need for labeled signals and big measurement campaigns. To overcome those problems, we propose the use of untrained neural networks (UNNs) for MIMO channel recreation/estimation and low overhead reporting. The UNNs learn the propagation environment by fitting a few channel measurements and we exploit their learned prior to provide higher channel estimation gains. Moreover, we present a UNN for simultaneous channel recreation for multiple users, or multiple user equipment (UE) positions, in which we have a trade-off between the estimated channel gain and the number of parameters. Our results show that transfer learning techniques are effective in accessing the learned prior on the environment structure as they provide higher channel gain for neighbouring users. Moreover, we indicate how the under-parameterization of UNNs can further enable low-overhead channel state information (CSI) reporting.

Related papers

• Deep learning approaches to indoor wireless channel estimation for low-power communication [0.0]
  This paper presents two Fully Connected Neural Networks (FCNNs)-based Low Power (LP-IoT) channel estimation models, leveraging RSSI for accurate channel estimation in LP-IoT communication. Our Model A exhibits a remarkable 99.02% reduction in Mean Squared Error (MSE), and Model B demonstrates a notable 90.03% MSE reduction compared to the benchmarks set by current studies.
  arXiv  Detail & Related papers  (2024-05-21T00:36:34Z)
• 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)
• Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges [137.47736805685457]
  We show that interference can still be effectively mitigated even without knowing its channel information. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs.
  arXiv  Detail & Related papers  (2023-05-11T18:06:46Z)
• 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)
• Age of Information in Deep Learning-Driven Task-Oriented Communications [78.84264189471936]
  This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter. The transmitter-receiver operations are modeled as an encoder-decoder pair of deep neural networks (DNNs) that are jointly trained.
  arXiv  Detail & Related papers  (2023-01-11T04:15:51Z)
• DeepTx: Deep Learning Beamforming with Channel Prediction [8.739166282613118]
  In this study, we focus on machine learning algorithms for the transmitter. We consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain.
  arXiv  Detail & Related papers  (2022-02-16T11:19:54Z)
• Machine Learning for CSI Recreation Based on Prior Knowledge [19.0581196881206]
  We propose to combine untrained neural networks (UNNs) and conditional generative adversarial networks (cGANs) UNNs learn the prior-CSI for some locations which are used to build the input to a cGAN. Based on the prior-CSI, their locations and the location of the desired channel, the cGAN is trained to output the channel expected at the desired location. Our results show that our method is successful in modelling the wireless channel and robust to location quantization errors in line of sight conditions.
  arXiv  Detail & Related papers  (2021-11-15T15:49:08Z)
• Semi-supervised Network Embedding with Differentiable Deep Quantisation [81.49184987430333]
  We develop d-SNEQ, a differentiable quantisation method for network embedding. d-SNEQ incorporates a rank loss to equip the learned quantisation codes with rich high-order information. It is able to substantially compress the size of trained embeddings, thus reducing storage footprint and accelerating retrieval speed.
  arXiv  Detail & Related papers  (2021-08-20T11:53:05Z)
• Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO [54.54508321463112]
  In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information. We utilize the neural networks (NNs) to capture the inherent connection between the uplink and downlink channel data sets and extrapolate the downlink channels from a subset of the uplink channel state information. We study the antenna subset selection problem in order to achieve the best channel extrapolation and decrease the data size of NNs.
  arXiv  Detail & Related papers  (2020-09-03T13:38:52Z)
• Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO [12.487990897680422]
  Channel estimation via machine learning requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output. In previous works, model training is mostly done via centralized learning (CL), where the whole training dataset is collected from the users at the base station (BS) We propose a federated learning (FL) framework for channel estimation. We design a convolutional neural network (CNN) trained on the local datasets of the users without sending them to the BS. We evaluate the performance for noisy and quantized model transmission and show that the proposed approach provides approximately 16 times lower overhead than CL
  arXiv  Detail & Related papers  (2020-08-25T06:51:18Z)
• 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)

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.