Polar Coded Faster-than-Nyquist (FTN) Signaling with Symbol-by-Symbol Detection

Caglan, Abdulsamet; Çıçek, Adem; Cavus, Enver; Bedeer, Ebrahim; Yanıkömeroğlu, Halim

doi:10.1109/wcnc45663.2020.9120701

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…Fig. 3 displays the BER performance of polar coded FTN system with the soSDR detector when different P values are used in the calculation of the LLR values in (16). Assuming iteration number Q in Gaussian randomization is set to 100 and τ = 0.8, the BER simulations are performed for different Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Also, the performance of soSDR is compared with that of SSSgbKSE in [15], [16], for τ = 0.8 in Fig. 5.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The total complexity is O(N 3.5 + N 2 Q) in the hard-output SDR FTN detector [12], with an additional complexity increase of O(2N P) for the soSDR. The extra complexity of soSDR consists of O(N P) for calculating (16) for N bits using P sequences and of O(N P) for creating a new set in case we have an empty set.…”

Section: Complexity Analysismentioning

confidence: 99%

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Low Complexity Soft-Output Faster-than-Nyquist Detector Based on Semidefinite Relaxation

Çıçek¹,

Cavus²,

Bedeer³

et al. 2020

Preprint

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Faster than Nyquist (FTN) signaling is an attractive transmission technique that is capable of improving the spectral efficiency with additional detection complexity at the receiver. Semidefinite relaxation (SDR) based FTN detectors are appealing as they provide good performance with linear decoding complexity. In this paper, we propose a soft-output semidefinite relaxation (soSDR) based FTN detector which has a similar polynomial complexity order when compared to its counterpart that only produces hard-output decisions. The main complexity reduction lies in re-using the candidate sequences generated in the Gaussian randomization (GR) step to produce reliable soft-output values, which approximate the calculation of the log-likelihood ratio (LLR) inputs for the channel decoder. The effectiveness of the proposed soSDR algorithm is evaluated using polar codes with successive cancellation decoding (SCD) through simulations, and its performance is compared against the state-of-the-art techniques from the literature. Simulation results show that the proposed soSDR algorithm provides reliable LLR values and strikes a good balance between detection complexity and bit error rate (BER) performance.

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Section: Simulation Resultsmentioning

confidence: 99%

“…Also, the performance of soSDR is compared with that of SSSgbKSE in [15], [16], for τ = 0.8 in Fig. 5.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Low Complexity Soft-Output Faster-than-Nyquist Detector Based on Semidefinite Relaxation

Çıçek¹,

Cavus²,

Bedeer³

et al. 2020

Preprint

Self Cite

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show abstract

“…There are also studies on joint designs for LDPC, turbo or polar codes and FTN signaling [23]- [25], and also on joint space-time codes for MIMO FTN [26].…”

Section: A Other Related Workmentioning

confidence: 99%

Faster-than-Nyquist Signaling for MIMO Communications

Zhang¹,

Yüksel²,

Yanıkömeroğlu³

2021

Preprint

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Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique, which has the potential to provide significant spectral efficiency improvement. This paper studies the capacity of FTN signaling for point-to-point single-input single-output (SISO) and multiple-input multiple-output (MIMO) channels. Although the capacity of SISO FTN was studied previously, due to some incomplete prerequisites, the exact capacity expression was not found. In this paper we resolve these issues for SISO FTN and generalize the result to MIMO FTN. We show that joint waterfilling in time and space is capacity achieving for MIMO FTN.

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“…Therefore, there are many efforts to find sub-optimal implementations. For example, the work in [ 15 ] introduces the successful symbol-by-symbol with go-back-K sequence estimation (SSSgbKSE) detection technology into the polar codes of FTN signaling, which is closer to the BCJR algorithm while reducing the complexity. The authors of [ 16 ] proposed an FPGA-based sliding-window max-log MAP algorithm that can be directly applied to FTN transmission systems to mitigate ISI.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven and Model-Driven Joint Detection Algorithm for Faster-Than-Nyquist Signaling in Multipath Channels

Deng

Bian

2021

Sensors

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In recent years, Faster-than-Nyquist (FTN) transmission has been regarded as one of the key technologies for future 6G due to its advantages in high spectrum efficiency. However, as a price to improve the spectrum efficiency, the FTN system introduces inter-symbol interference (ISI) at the transmitting end, whicheads to a serious deterioration in the performance of traditional receiving algorithms under high compression rates and harsh channel environments. The data-driven detection algorithm has performance advantages for the detection of high compression rate FTN signaling, but the current related work is mainly focused on the application in the Additive White Gaussian Noise (AWGN) channel. In this article, for FTN signaling in multipath channels, a data and model-driven joint detection algorithm, i.e., DMD-JD algorithm is proposed. This algorithm first uses the traditional MMSE or ZFinear equalizer to complete the channel equalization, and then processes the serious ISI introduced by FTN through the deepearning network based on CNN or LSTM, thereby effectively avoiding the problem of insufficient generalization of the deepearning algorithm in different channel scenarios. The simulation results show that in multipath channels, the performance of the proposed DMD-JD algorithm is better than that of purely model-based or data-driven algorithms; in addition, the deepearning network trained based on a single channel model can be well adapted to FTN signal detection under other channel models, thereby improving the engineering practicability of the FTN signal detection algorithm based on deepearning.

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Polar Coded Faster-than-Nyquist (FTN) Signaling with Symbol-by-Symbol Detection

Cited by 10 publications

References 19 publications

Low Complexity Soft-Output Faster-than-Nyquist Detector Based on Semidefinite Relaxation

Low Complexity Soft-Output Faster-than-Nyquist Detector Based on Semidefinite Relaxation

Faster-than-Nyquist Signaling for MIMO Communications

Data-Driven and Model-Driven Joint Detection Algorithm for Faster-Than-Nyquist Signaling in Multipath Channels

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