Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access

• URL: http://arxiv.org/abs/2205.00849v1
• Date: Mon, 2 May 2022 12:23:55 GMT
• Title: Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access
• Authors: Rafael Cerna Loli, Onur Dizdar, Bruno Clerckx, Cong Ling
• Abstract summary: 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.
• Score: 65.21117658030235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been intensively studied in recent years, albeit mostly under the assumption of perfect Channel State Information at the Receiver (CSIR) and ideal capacity-achieving modulation and coding schemes. To assess its practical performance, benefits, and limits under more realistic conditions, this work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods, which aims to unite the simple structure of the conventional SIC receiver and the robustness and model agnosticism of deep learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS), and average training overhead. Also, a comparison with the SIC receiver, with perfect and imperfect CSIR, is given. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR, due to its ability to generate on demand non-linear symbol detection boundaries in a pure data-driven manner.

Related papers

• Latent Diffusion Model-Enabled Real-Time Semantic Communication Considering Semantic Ambiguities and Channel Noises [18.539501941328393]
  This paper constructs a latent diffusion model-enabled SemCom system, and proposes three improvements compared to existing works. A lightweight single-layer latent space transformation adapter completes one-shot learning at the transmitter. An end-to-end consistency distillation strategy is used to distill the diffusion models trained in latent space.
  arXiv  Detail & Related papers  (2024-06-09T23:39:31Z)
• 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)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• 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)
• HybridDeepRx: Deep Learning Receiver for High-EVM Signals [13.678714245633596]
  We propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals. A novel deep learning based convolutional neural network receiver is devised, containing layers in both time- and frequency domains.
  arXiv  Detail & Related papers  (2021-06-30T14:10:01Z)
• End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels [11.182920270301304]
  A novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent receiver design (JTRD) in fading channels. The transmitter side is modeled as a group of parallel linear layers, which are responsible for multiuser waveform design. The non-coherent receiver is formed by a deep feed-forward neural network (DFNN) so as to provide multiuser detection (MUD) capabilities.
  arXiv  Detail & Related papers  (2021-05-04T02:47:59Z)
• 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)
• FedRec: Federated Learning of Universal Receivers over Fading Channels [92.15358738530037]
  We propose a neural network-based symbol detection technique for downlink fading channels. Multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics.
  arXiv  Detail & Related papers  (2020-11-14T11:29:55Z)
• Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning [119.97450366894718]
  A novel framework is proposed to optimize the downlink multi-user communication of a millimeter wave base station. A channel estimation approach is developed to measure the channel state information (CSI) in real-time. A distributional reinforcement learning (DRL) approach is proposed to learn the optimal IR reflection and maximize the expectation of downlink capacity.
  arXiv  Detail & Related papers  (2020-02-24T22:18:54Z)

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.