Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access

• URL: http://arxiv.org/abs/2204.01731v1
• Date: Mon, 4 Apr 2022 12:35:37 GMT
• Title: Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access
• Authors: Shuang Liang, Yinan Zou, and Yong Zhou
• Abstract summary: We propose a novel model-free learning method based on generative adversarial network (GAN) to tackle the JADCE problem. By leveraging the properties of the pseudoinverse, the generator is refined by using an affine projection and a skip connection. We show that the proposed method outperforms the existing methods in high SNR regimes.
• Score: 10.586509586304771
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks. However, the existing model-free learning method can only achieve either activity detection or channel estimation, but not both. In this paper, we propose a novel model-free learning method based on generative adversarial network (GAN) to tackle the JADCE problem. We adopt the U-net architecture to build the generator rather than the standard GAN architecture, where a pre-estimated value that contains the activity information is adopted as input to the generator. By leveraging the properties of the pseudoinverse, the generator is refined by using an affine projection and a skip connection to ensure the output of the generator is consistent with the measurement. Moreover, we build a two-layer fully-connected neural network to design pilot matrix for reducing the impact of receiver noise. Simulation results show that the proposed method outperforms the existing methods in high SNR regimes, as both data consistency projection and pilot matrix optimization improve the learning ability.

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)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications [37.35929546347294]
  We investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme. Due to the correlated sparsity pattern introduced by the non-coherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. We propose a deep learning modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation.
  arXiv  Detail & Related papers  (2023-01-02T04:02:32Z)
• Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning [23.05003652536773]
  Federated edge learning (FEEL) is a promising distributed machine learning (ML) framework to drive edge intelligence applications. We propose time-correlated sparsification with hybrid aggregation (TCS-H) for communication-efficient FEEL.
  arXiv  Detail & Related papers  (2022-02-17T02:48:07Z)
• Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
  We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
  arXiv  Detail & Related papers  (2021-10-20T06:15:45Z)
• 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 Learning-Based Active User Detection for Grant-free SCMA Systems [12.565459084483045]
  We propose two novel group-based deep neural network active user detection schemes. Schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. offline pre-trained model is able to detect the active devices without any channel state information.
  arXiv  Detail & Related papers  (2021-06-21T15:34:14Z)
• 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)
• Robust Attack Detection Approach for IIoT Using Ensemble Classifier [0.0]
  The objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network. The proposed model is tested on standard IoT attack outliers such as WUSTL_IIOT-2018, N_BaIoT, and Bot_IoT. The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.
  arXiv  Detail & Related papers  (2021-01-30T07:21:44Z)
• Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network [92.01145655155374]
  We propose a quality attention generative adversarial network (QAGAN) trained on unpaired data. Key novelty of the proposed QAGAN lies in the injected QAM for the generator. Our proposed method achieves better performance in both objective and subjective evaluations.
  arXiv  Detail & Related papers  (2020-12-30T05:57:20Z)
• 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)

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.