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Register a new userReceiver Design for Faster-than-Nyquist Signaling: Deep-learning-based Architectures
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Added by
Peiyang Song
Publication date
2018
fields
Electronic Engineering
and research's language is
English
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Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth utilization at the expense of additional intersymbol interference (ISI). In this paper, we apply state-of-the-art deep learning (DL) technology into receiver design for FTN signaling and propose two DL-based new architectures. Firstly, we propose an FTN signal detection based on DL and connect it with the successive interference cancellation (SIC) to replace traditional detection algorithms. Simulation results show that this architecture can achieve near-optimal performance in both uncoded and coded scenarios. Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers. The performance of this new architecture has also been illustrated by simulation results. Finally, both the proposed DL-based receiver architecture has the robustness to signal to noise ratio (SNR). In a nutshell, DL has been proved to be a powerful tool for the FTN receiver design.
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This letter proposes a blind symbol packing rartio estimation for faster-than-Nyquist (FTN) signaling based on state-of-the-art deep learning (DL) technology. The symbol packing rartio is a vital parameter to obtain the real symbol rate and recover the origin symbols from the received symbols by calculating the intersymbol interference (ISI). To the best of our knowledge, this is the first effective estimation approach for symbol packing rartio in FTN signaling and has shown its fast convergence and robustness to signal-to-noise ratio (SNR) by numerical simulations. Benefiting from the proposed blind estimation, the packing-ratio-based adaptive FTN transmission without dedicate channel or control frame becomes available. Also, the secure FTN communications based on secret symbol packing rartio can be easily cracked.
In this paper, we investigate the sequence estimation problem of faster-than-Nyquist (FTN) signaling as a promising approach for increasing spectral efficiency (SE) in future communication systems. In doing so, we exploit the concept of Gaussian separability and propose two probabilistic data association (PDA) algorithms with polynomial time complexity to detect binary phase-shift keying (BPSK) FTN signaling. Simulation results show that the proposed PDA algorithm outperforms the recently proposed SSSSE and SSSgb$K$SE algorithms for all SE values with a modest increase in complexity. The PDA algorithm approaches the performance of the semidefinite relaxation (SDRSE) algorithm for SE values of $0.96$ bits/sec/Hz, and it is within the $0.5$ dB signal-to-noise ratio (SNR) penalty at SE values of $1.10$ bits/sec/Hz for the fixed values of $beta = 0.3$.
A deep learning assisted sum-product detection algorithm (DL-SPA) for faster-than-Nyquist (FTN) signaling is proposed in this paper. The proposed detection algorithm concatenates a neural network to the variable nodes of the conventional factor graph of the FTN system to help the detector converge to the a posterior probabilities based on the received sequence. More specifically, the neural network performs as a function node in the modified factor graph to deal with the residual intersymbol interference (ISI) that is not modeled by the conventional detector with a limited number of ISI taps. We modify the updating rule in the conventional sum-product algorithm so that the neural network assisted detector can be complemented to a Turbo equalization. Furthermore, a simplified convolutional neural network is employed as the neural network function node to enhance the detectors performance and the neural network needs a small number of batches to be trained. Simulation results have shown that the proposed DL-SPA achieves a performance gain up to 2.5 dB with the same bit error rate compared to the conventional sum-product detection algorithm under the same ISI responses.
Reduced complexity faster-than-Nyquist (FTN) signaling systems are gaining increased attention as they provide improved bandwidth utilization for an acceptable level of detection complexity. In order to have a better understanding of the tradeoff between performance and complexity of the reduced complexity FTN detection techniques, it is necessary to study these techniques in the presence of channel coding. In this paper, we investigate the performance a polar coded FTN system which uses a reduced complexity FTN detection, namely, the recently proposed successive symbol-by-symbol with go-backK sequence estimation (SSSgbKSE) technique. Simulations are performed for various intersymbol-interference (ISI) levels and for various go-back-K values. Bit error rate (BER) performance of Bahl-Cocke-Jelinek-Raviv (BCJR) detection and SSSgbKSE detection techniques are studied for both uncoded and polar coded systems. Simulation results reveal that polar codes can compensate some of the performance loss incurred in the reduced complexity SSSgbKSE technique and assist in closing the performance gap between BCJR and SSSgbKSE detection algorithms.
Ultra-reliable low-latency communication (URLLC) requires short packets of data transmission. It is known that when the packet length becomes short, the achievable rate is subject to a penalty when compared to the channel capacity. In this paper, we propose to use faster-than-Nyquist (FTN) signaling to compensate for the achievable rate loss of short packet communications. We investigate the performance of a combination of a low complexity detector of FTN signaling used with nonbinary low-density parity-check (NB-LDPC) codes that is suitable for low-latency and short block length requirements of URLLC systems. Our investigation shows that such combination of low-complexity FTN signaling detection and NB-LDPC codes outperforms the use of close-to-optimal FTN signaling detectors with LDPC codes in terms of error rate performance and also has a considerably lower computational complexity.
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