DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems

• URL: http://arxiv.org/abs/2212.07816v1
• Date: Thu, 15 Dec 2022 13:32:36 GMT
• Title: DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems
• Authors: Reinhard Wiesmayr, Chris Dick, Jakob Hoydis, Christoph Studer
• Abstract summary: Iterative detection and decoding (IDD) is known to achieve near-capacity performance in multi-antenna wireless systems. We propose deep-unfolded interleaved detection and decoding (DUIDD), a new paradigm that reduces the complexity of IDD while achieving even lower error rates.
• Score: 25.372069988747114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Iterative detection and decoding (IDD) is known to achieve near-capacity performance in multi-antenna wireless systems. We propose deep-unfolded interleaved detection and decoding (DUIDD), a new paradigm that reduces the complexity of IDD while achieving even lower error rates. DUIDD interleaves the inner stages of the data detector and channel decoder, which expedites convergence and reduces complexity. Furthermore, DUIDD applies deep unfolding to automatically optimize algorithmic hyperparameters, soft-information exchange, message damping, and state forwarding. We demonstrate the efficacy of DUIDD using NVIDIA's Sionna link-level simulator in a 5G-near multi-user MIMO-OFDM wireless system with a novel low-complexity soft-input soft-output data detector, an optimized low-density parity-check decoder, and channel vectors from a commercial ray-tracer. Our results show that DUIDD outperforms classical IDD both in terms of block error rate and computational complexity.

Related papers

• Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based? [89.05848771674773]
  A novel antenna system ()-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed. It consists of multiple waveguides, which equip numerous low-cost antennas, named (PAs) The positions of PAs can be reconfigured to both spanning large-scale path and space.
  arXiv  Detail & Related papers  (2025-02-12T18:54:10Z)
• Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar [8.674241138986925]
  The system employs a planar array configuration and adapts its transmitted waveforms and beamforming patterns to optimize detection performance. A robust Wald-type detector is integrated with a SARSA-based RL algorithm, enabling the radar to learn and adapt to complex clutter environments.
  arXiv  Detail & Related papers  (2025-02-07T14:31:58Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  In neuromorphic computing, spiking neural networks (SNNs) perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have demonstrated that embedding a few bits of payload in each spike exchanged between the spiking neurons can further enhance inference accuracy. This paper investigates a wireless neuromorphic split computing architecture employing multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture [24.824596231020585]
  We propose a packet-level network intrusion detection solution that makes use of Recurrent Autoencoders to integrate an arbitrary-length sequence of packets into a more compact joint feature embedding. We show that our approach leads to an extremely efficient, real-time solution with high detection accuracy at the packet level.
  arXiv  Detail & Related papers  (2023-11-27T17:30:19Z)
• Complexity Matters: Rethinking the Latent Space for Generative Modeling [65.64763873078114]
  In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion. In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity.
  arXiv  Detail & Related papers  (2023-07-17T07:12:29Z)
• Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless Systems [0.6091702876917281]
  We propose a simple low-complexity TPC algorithm based on the deep unfolding of the iterative projected gradient (PGD) algorithm into layers of a deep neural network and learning the step-size parameters. Performance evaluation in dense device-to-device (D2D) communication scenarios showed that the proposed method can achieve better performance than the iterative algorithm with more than a factor of 2 lower number of iterations.
  arXiv  Detail & Related papers  (2023-06-20T19:51:21Z)
• DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data [0.0]
  We propose an anomaly detection and diagnosis model, DTAAD, based on Transformer and Dual Temporal Convolutional Network (TCN) scaling methods and feedback mechanisms are introduced to improve prediction accuracy and expand correlation differences. Our experiments on seven public datasets validate that DTAAD exceeds the majority of currently advanced baseline methods in both detection and diagnostic performance.
  arXiv  Detail & Related papers  (2023-02-17T06:59:45Z)
• Hubbard-Stratonovich Detector for Simple Trainable MIMO Signal Detection [7.969977930633441]
  Deep unfolding (DU) has been applied with remarkable detection performance. The proposed detector based on the Hubbard--Stratonovich (HS) transformation and DU is called the trainable HS (THS) detector. It requires only $O(1)$ trainable parameters and its training and execution cost is $O(n2)$ per iteration, where $n$ is the number of transmitting antennas.
  arXiv  Detail & Related papers  (2023-02-09T06:51:25Z)
• Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding [94.40747235081466]
  We propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. We develop a DNN architecture that maps the received pilots into feedback bits at the receiver, and then further maps the feedback bits into the hybrid precoder at the transmitter.
  arXiv  Detail & Related papers  (2021-10-22T20:49:02Z)
• Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving [94.40107679615618]
  This paper proposes a novel binarized driveable area detection network (binary DAD-Net) It uses only binary weights and activations in the encoder, the bottleneck, and the decoder part. It outperforms state-of-the-art semantic segmentation networks on public datasets.
  arXiv  Detail & Related papers  (2020-06-15T07:09:01Z)
• ADMM-based Decoder for Binary Linear Codes Aided by Deep Learning [40.25456611849273]
  This work presents a deep neural network aided decoding algorithm for binary linear codes. Based on the concept of deep unfolding, we design a decoding network by unfolding the alternating direction method of multipliers (ADMM)-penalized decoder. Numerical results show that the resulting DL-aided decoders outperform the original ADMM-penalized decoder.
  arXiv  Detail & Related papers  (2020-02-14T03:32:14Z)
• 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)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

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.