Superposition Coded Modulation Based Faster‐Than‐Nyquist Signaling

Li, Shuangyang; Bai, Baoming; Zhou, Jing; He, Qingli; Li, Qian

doi:10.1155/2018/4181626

Cited by 17 publications

(6 citation statements)

References 19 publications

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“…Moreover, NOMA transmission based on faster-than-Nyquist (FTN) signaling was also considered in the literature [12]. Indeed, FTN signaling is a classic nonorthogonal signaling scheme exhibiting an enhanced spectral efficiency [13]- [16], where data is transmitted at a symbol rate higher than the Nyquist intersymbol interference (ISI)-free rate 1 [19]. Although FTN-based NOMA (FTN-NOMA) transmission is expected to have an increased achievable rate, its theoretical rate analysis has not been disseminated in the open literature.…”

Section: Introductionmentioning

confidence: 99%

Faster-Than-Nyquist Asynchronous NOMA Outperforms Synchronous NOMA

Wei

Yuan

et al. 2022

IEEE J. Select. Areas Commun.

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Faster-than-Nyquist (FTN) signaling aided nonorthogonal multiple access (NOMA) is conceived and its achievable rate is quantified in the presence of random link delays of the different users. We reveal that exploiting the link delays may potentially lead to a signal-to-interference-plus-noise ratio (SINR) gain, while transmitting the data symbols at FTN rates has the potential of increasing the degree-of-freedom (DoF). We then unveil the fundamental trade-off between the SINR and DoF. In particular, at a sufficiently high symbol rate, the SINR gain vanishes while the DoF gain achieves its maximum, where the achievable rate is almost (1 + β) times higher than that of the conventional synchronous NOMA transmission in the high signal-to-noise ratio (SNR) regime, with β being the roll-off factor of the signaling pulse. Our simulation results verify our analysis and demonstrate considerable rate improvements over the conventional power-domain NOMA scheme.

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Section: Introductionmentioning

confidence: 99%

Faster-Than-Nyquist Asynchronous NOMA Outperforms Synchronous NOMA

Wei

Yuan

et al. 2022

IEEE J. Select. Areas Commun.

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“…In his pioneering paper [1], Mazo showed that when signaling transmits 25% faster than Nyquist rate, the minimum Euclidean distance between binary phase shift keying (BPSK) modulated sinc pulses remains the same, which indicates that FTN signaling can boost the data rate by 25% without performance degradation or bandwidth expansion. More importantly, it has been proved that FTN signaling can achieve a higher Shannon capacity than classic Nyquist signaling with a non-sinc signaling pulse [2], [3]. Therefore FTN signaling has been recognized as bandwidth-efficient signaling scheme, which has a great potential for supporting high speed data transmissions in next generation wireless communication systems [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Time-Domain vs. Frequency-Domain Equalization for FTN Signaling

Yuan

et al. 2020

IEEE Trans. Veh. Technol.

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Faster-than-Nyquist (FTN) signaling has been recognized as a promising technique for next-generation high data rate communications. By intentionally reducing the symbol interval, FTN signaling is capable of transmitting more symbols than classic Nyquist signaling within the same time period and bandwidth. However, the intentional non-orthogonality of the bandlimited signaling pulses imposes severe inter-symbol interference (ISI), which requires powerful equalization at the receiver. Hence, we embark on the comparison of time-and frequency-domain equalization for FTN signaling both by theoretical analysis and numerical simulations. It is shown that frequency-domain equalization fails to reliably detect the FTN signal with a low FTN packing factor, while the time-domain equalization still performs well.

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“…e simulation results of Anderson et al [10] reveal that when the transmitted pulse has excessive bandwidth, FTN transmission can achieve higher capacity than transmission under Nyquist rate. In other works, the research of FTN has been extended on multicarrier systems [11,12] and low-complexity demodulation [13].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Aided Detection Method for FTN-Based NOMA

Pan

Wang

et al. 2020

Wireless Communications and Mobile Computing

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The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses. Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology. The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.

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Superposition Coded Modulation Based Faster‐Than‐Nyquist Signaling

Cited by 17 publications

References 19 publications

Faster-Than-Nyquist Asynchronous NOMA Outperforms Synchronous NOMA

Faster-Than-Nyquist Asynchronous NOMA Outperforms Synchronous NOMA

Time-Domain vs. Frequency-Domain Equalization for FTN Signaling

A Deep Learning-Aided Detection Method for FTN-Based NOMA

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