On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge

• URL: http://arxiv.org/abs/2401.12645v1
• Date: Tue, 23 Jan 2024 10:55:29 GMT
• Title: On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge
• Authors: Chin-Hung Chen, Boris Karanov, Wim van Houtum, Wu Yan, Alex Young, Alex Alvarado
• Abstract summary: This paper expands upon existing literature to encompass a variety of imperfect channel knowledge cases that appear in real-world transmissions. BCJRNet significantly outperforms the conventional BCJR algorithm for stationary transmission scenarios. Our results also show the importance of memory assumptions for conventional BCJR and BCJRNet.
• Score: 1.9107347888374506
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, a data-driven Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm tailored to channels with intersymbol interference has been introduced. This so-called BCJRNet algorithm utilizes neural networks to calculate channel likelihoods. BCJRNet has demonstrated resilience against inaccurate channel tap estimations when applied to a time-invariant channel with ideal exponential decay profiles. However, its generalization capabilities for practically-relevant time-varying channels, where the receiver can only access incorrect channel parameters, remain largely unexplored. The primary contribution of this paper is to expand upon the results from existing literature to encompass a variety of imperfect channel knowledge cases that appear in real-world transmissions. Our findings demonstrate that BCJRNet significantly outperforms the conventional BCJR algorithm for stationary transmission scenarios when learning from noisy channel data and with imperfect channel decay profiles. However, this advantage is shown to diminish when the operating channel is also rapidly time-varying. Our results also show the importance of memory assumptions for conventional BCJR and BCJRNet. An underestimation of the memory largely degrades the performance of both BCJR and BCJRNet, especially in a slow-decaying channel. To mimic a situation closer to a practical scenario, we also combined channel tap uncertainty with imperfect channel memory knowledge. Somewhat surprisingly, our results revealed improved performance when employing the conventional BCJR with an underestimated memory assumption. BCJRNet, on the other hand, showed a consistent performance improvement as the level of accurate memory knowledge increased.

Related papers

• Distilling Channels for Efficient Deep Tracking [68.13422829310835]
  This paper presents a novel framework termed channel distillation to facilitate deep trackers. We show that an integrated formulation can turn feature compression, response map generation, and model update into a unified energy minimization problem. The resulting deep tracker is accurate, fast, and has low memory requirements.
  arXiv  Detail & Related papers  (2024-09-18T08:09:20Z)
• Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive Noise [1.9462217839524345]
  We developed machine learning approaches for data-driven trellis-based soft symbol detection in coded transmission over intersymbol interference (ISI) channels. This enabled us to obtain optimized detectors based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. We demonstrated the viability of a hybrid NN and HMM BCJR detection where NN is used for learning the likelihoods, while the state transitions are optimized via HMM.
  arXiv  Detail & Related papers  (2024-05-17T14:35:09Z)
• From Similarity to Superiority: Channel Clustering for Time Series Forecasting [61.96777031937871]
  We develop a novel and adaptable Channel Clustering Module ( CCM) CCM dynamically groups channels characterized by intrinsic similarities and leverages cluster information instead of individual channel identities. CCM can boost the performance of CI and CD models by an average margin of 2.4% and 7.2% on long-term and short-term forecasting, respectively.
  arXiv  Detail & Related papers  (2024-03-31T02:46:27Z)
• Fault-tolerant quantum architectures based on erasure qubits [49.227671756557946]
  We exploit the idea of erasure qubits, relying on an efficient conversion of the dominant noise into erasures at known locations. We propose and optimize QEC schemes based on erasure qubits and the recently-introduced Floquet codes. Our results demonstrate that, despite being slightly more complex, QEC schemes based on erasure qubits can significantly outperform standard approaches.
  arXiv  Detail & Related papers  (2023-12-21T17:40:18Z)
• 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)
• An Efficient Machine Learning-based Channel Prediction Technique for OFDM Sub-Bands [0.0]
  We propose an efficient machine learning (ML)-based technique for channel prediction in OFDM sub-bands. The novelty of the proposed approach lies in the training of channel fading samples used to estimate future channel behaviour in selective fading.
  arXiv  Detail & Related papers  (2023-05-31T09:41:27Z)
• Soft Masking for Cost-Constrained Channel Pruning [17.138115344464513]
  Structured channel pruning has been shown to significantly accelerate inference time for convolution neural networks (CNNs) on modern hardware. Recent works permanently zero these channels during training, which we observe to significantly hamper final accuracy. We propose Soft Masking for cost-constrained Channel Pruning (SMCP) to allow pruned channels to adaptively return to the network.
  arXiv  Detail & Related papers  (2022-11-04T01:28:45Z)
• CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization [61.71504948770445]
  We propose a novel channel pruning method via Class-Aware Trace Ratio Optimization (CATRO) to reduce the computational burden and accelerate the model inference. We show that CATRO achieves higher accuracy with similar cost or lower cost with similar accuracy than other state-of-the-art channel pruning algorithms. Because of its class-aware property, CATRO is suitable to prune efficient networks adaptively for various classification subtasks, enhancing handy deployment and usage of deep networks in real-world applications.
  arXiv  Detail & Related papers  (2021-10-21T06:26:31Z)
• Group Fisher Pruning for Practical Network Compression [58.25776612812883]
  We present a general channel pruning approach that can be applied to various complicated structures. We derive a unified metric based on Fisher information to evaluate the importance of a single channel and coupled channels. Our method can be used to prune any structures including those with coupled channels.
  arXiv  Detail & Related papers  (2021-08-02T08:21:44Z)
• Channel Pruning Guided by Spatial and Channel Attention for DNNs in Intelligent Edge Computing [15.248962858090431]
  A critical challenge is to determine which channels are to be removed, so that the model accuracy will not be negatively affected. We propose a new attention module combining both spatial and channel attention. With the guidance of SCA, our CPSCA approach achieves higher inference accuracy than other state-of-the-art pruning methods.
  arXiv  Detail & Related papers  (2020-11-08T02:40:06Z)

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.