Deep Learning-based List Sphere Decoding for Faster-than-Nyquist (FTN) Signaling Detection

• URL: http://arxiv.org/abs/2204.07569v1
• Date: Fri, 15 Apr 2022 17:46:03 GMT
• Title: Deep Learning-based List Sphere Decoding for Faster-than-Nyquist (FTN) Signaling Detection
• Authors: Sina Abbasi and Ebrahim Bedeer
• Abstract summary: Faster-than-Nyquist (FTN) signaling is a candidate non-orthonormal transmission technique. In this paper, we investigate the use of deep learning (DL) to reduce the detection complexity of FTN signaling.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Faster-than-Nyquist (FTN) signaling is a candidate non-orthonormal transmission technique to improve the spectral efficiency (SE) of future communication systems. However, such improvements of the SE are at the cost of additional computational complexity to remove the intentionally introduced intersymbol interference. In this paper, we investigate the use of deep learning (DL) to reduce the detection complexity of FTN signaling. To eliminate the need of having a noise whitening filter at the receiver, we first present an equivalent FTN signaling model based on using a set of orthonormal basis functions and identify its operation region. Second, we propose a DL-based list sphere decoding (DL-LSD) algorithm that selects and updates the initial radius of the original LSD to guarantee a pre-defined number $N_{\text{L}}$ of lattice points inside the hypersphere. This is achieved by training a neural network to output an approximate initial radius that includes $N_{\text{L}}$ lattice points. At the testing phase, if the hypersphere has more than $N_{\text{L}}$ lattice points, we keep the $N_{\text{L}}$ closest points to the point corresponding to the received FTN signal; however, if the hypersphere has less than $N_{\text{L}}$ points, we increase the approximate initial radius by a value that depends on the standard deviation of the distribution of the output radii from the training phase. Then, the approximate value of the log-likelihood ratio (LLR) is calculated based on the obtained $N_{\text{L}}$ points. Simulation results show that the computational complexity of the proposed DL-LSD is lower than its counterpart of the original LSD by orders of magnitude.

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