DeepMuD: Multi-user Detection for Uplink Grant-Free NOMA IoT Networks via Deep Learning

• URL: http://arxiv.org/abs/2102.09196v1
• Date: Thu, 18 Feb 2021 07:43:03 GMT
• Title: DeepMuD: Multi-user Detection for Uplink Grant-Free NOMA IoT Networks via Deep Learning
• Authors: Ahmet Emir, Ferdi Kara, Hakan Kaya, Halim Yanikomeroglu
• Abstract summary: We propose a deep learning-aided multi-user detection (DeepMuD) in uplink non-orthogonal multiple access (NOMA) The proposed DeepMuD improves the error performance of the uplink NOMA significantly. This gain becomes superb with the increase in the number of Internet of Things (IoT) devices.
• Score: 22.345609845425493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this letter, we propose a deep learning-aided multi-user detection (DeepMuD) in uplink non-orthogonal multiple access (NOMA) to empower the massive machine-type communication where an offline-trained Long Short-Term Memory (LSTM)-based network is used for multi-user detection. In the proposed DeepMuD, a perfect channel state information (CSI) is also not required since it is able to perform a joint channel estimation and multi-user detection with the pilot responses, where the pilot-to-frame ratio is very low. The proposed DeepMuD improves the error performance of the uplink NOMA significantly and outperforms the conventional detectors (even with perfect CSI). Moreover, this gain becomes superb with the increase in the number of Internet of Things (IoT) devices. Furthermore, the proposed DeepMuD has a flexible detection and regardless of the number of IoT devices, the multi-user detection can be performed. Thus, an arbitrary number of IoT devices can be served without a signaling overhead, which enables the grant-free communication.

Related papers

• Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery [51.83786195178233]
  We design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables synergistic focusing'' of multi-scale features. RCs extend the multi-level feature's divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks.
  arXiv  Detail & Related papers  (2024-09-09T13:56:22Z)
• 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)
• Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication [7.231199109999097]
  Non-orthogonal multiple access (NOMA)-based multi-user semantic communication system named NOMASC is proposed in this paper. System can support semantic tranmission of multiple users with diverse modalities of source information.
  arXiv  Detail & Related papers  (2023-03-12T07:32:42Z)
• Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks [101.59367762974371]
  In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments. It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications. We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
  arXiv  Detail & Related papers  (2022-10-08T04:32:58Z)
• Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO [56.022764337221325]
  Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging. This paper proposes a deep learning (DL)-based rate-splitting multiple access scheme for RIS-aided Tera-Hertz multi-user multiple access systems.
  arXiv  Detail & Related papers  (2022-09-18T03:07:37Z)
• Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network [14.569960447904327]
  We propose an attention-based bidirectional long short-term memory (BiLSTM) network to solve the multi-user detection problem. The proposed framework does not require prior knowledge of device sparsity levels and channels for performing MUD. The results show that the proposed network achieves better performance compared to existing benchmark schemes.
  arXiv  Detail & Related papers  (2022-09-14T03:18:23Z)
• Graph Neural Network Aided MU-MIMO Detectors [31.202097630016286]
  Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. A base station serves many users in an uplink MU-MIMO system, leading to a substantial multi-user interference (MUI)
  arXiv  Detail & Related papers  (2022-06-19T11:23:02Z)
• A Single-Target License Plate Detection with Attention [56.83051142257412]
  Neural Network is commonly adopted to the License Plate Detection (LPD) task and achieves much better performance and precision, especially CNN-based networks can achieve state of the art RetinaNet. For a single object detection task such as LPD, modified general object detection would be time-consuming, unable to cope with complex scenarios and a cumbersome weights file that is too hard to deploy on the embedded device.
  arXiv  Detail & Related papers  (2021-12-12T03:00:03Z)
• 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)
• Pilot Interval Reduction by Deep Learning Based Detectors in Uplink NOMA [0.0]
  In uplink communication systems that the channel is not known at the receiver, pilot signals sent from each user in different time intervals have reduced the spectral efficiency of NOMA. In this study, in the uplink communication system, DL-deep learning based detectors which are known to respond to the pilot signals sent from the users at the base station have been researched.
  arXiv  Detail & Related papers  (2020-04-26T15:22:44Z)
• DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection [98.43451011898212]
  In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. We propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear.
  arXiv  Detail & Related papers  (2020-02-08T18:31:00Z)

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.