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20182022
most citedDeep Learning based Blind Symbol Packing Ratio Estimation for Faster-than-Nyquist Signaling

7 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2022

Joint Secure Communication and Radar Beamforming: A Secrecy-Estimation Rate-Based Design

Rong Wen, Ying Zhang, Qiang Li +1

This paper considers transmit beamforming in dual-function radar-communication (DFRC) system, where a DFRC transmitter simultaneously communicates with a communication user and det…

cs.IT2022★ 2 cited

Channel Estimation and Multipath Diversity Reception for RIS-Empowered Broadband Wireless Systems Based on Cyclic-Prefixed Single-Carrier Transmission

Qiang Li, Miaowen Wen, Ertugrul Basar +3

In this paper, a cyclic-prefixed single-carrier (CPSC) transmission scheme with phase shift keying (PSK) signaling is presented for broadband wireless communications systems empowe…

eess.SP2019★ 7 cited

Deep Learning based Blind Symbol Packing Ratio Estimation for Faster-than-Nyquist Signaling

Peiyang Song, Fengkui Gong, Qiang Li

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 ra…

eess.SP2019★ 2 cited

Symbol-Based Multi-Layer Iterative Successive Interference Cancellation for Faster-Than-Nyquist Signaling

Qiang Li, Feng-Kui Gong, Pei-Yang Song +1

A symbol-based multi-layer iterative successive interference cancellation (MLISIC) algorithm is proposed to eliminate the inter-symbol interference (ISI) for faster-than-Nyquist (F…

eess.SP2018

Receiver Design for Faster-than-Nyquist Signaling: Deep-learning-based Architectures

Peiyang Song, Fengkui Gong, Qiang Li +2

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…