Adaptive sphere decoder for 1-bit ADC MIMO-constant envelope modulation detection

Abdelhameed, Doaa; Hussien, Hany S.; Mohamed, Ehab Mahmoud

doi:10.1109/ccnc.2017.7983203

Cited by 5 publications

(2 citation statements)

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“…In their solution, a kernel sparse selector based on the correntropy maximization is used to nominate number of candidates from the space of linear baseband maximum likelihood decoder. The proposed decoder can achieve a complexity reduction by 91% compared to the recently decoder algorithms in [146], [147].…”

Section: Data Detection and Receiver Designmentioning

confidence: 94%

“…Then, the maximum likelihood decoder is used to pick up the candidate that most likely to be transmitted. In [147], Doaa et al (2017) further constructed an adaptive sphere decoder based on an affine linear transformation model. Compared with the method in [146], the biggest difference of the proposed decoder is that the initial spherical radius can be adaptively adjusted based on the SNR condition.…”

Section: Data Detection and Receiver Designmentioning

confidence: 99%

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Low-Resolution ADCs for Wireless Communication: A Comprehensive Survey

Luo

Xiong

2019

IEEE Access

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With the rapid growth of wireless data traffic and antennas configuration, higher spectrum efficiency and lower power consumption processing have evoked remarkable attention from the research and industry community for the deployment of future wireless communication. It has become a heated topic quickly in recent years and gives rise to the widespread interest around the world. As a core technology of the fifth-generation (5G) mobile communication, massive multi-input multi-output (MIMO) technology can fully exploit the space resources and greatly improve the spectral and energy efficiency. However, massive MIMO systems are faced with the problems of mass data processing, high hardware cost, and huge total power consumption. To cope with these problems, a useful solution is that the receiver equips with finite resolution analog-to-digital (ADC) converters. A large number of research results show that the low-resolution quantization technology brings significant performance within the allowable loss of capacity. This promising technique has attracted many scholars to do tremendous endeavor on it. As a motivation, we make a comprehensive survey about low-resolution ADCs for wireless communication. This paper summarizes the latest developments in the design of low-resolution communication systems, focusing on system performance analysis, some key technologies of the receiver, and typical application scenarios for the low-resolution ADCs. In view of the adverse effects caused by coarse quantization, some potential implementations are presented to alleviate this dilemma. Future research directions are also given and suggested in this paper. This overview contributes significantly to providing an informative and tutorial reference for the key technologies of low-resolution ADCs as well as its applications in practical systems.INDEX TERMS Low resolution quantization, multi-input and multi-output, channel capacity, channel estimation, automatic gain control, synchronization, receiver, relay system. JUN LIU received the B.S. degree in communication engineering from the Sichuan University of Science and Engineering, Zigong, China, in 2017, where he is currently pursuing the M.S. degree. From 2018 to 2020, he will be a Visiting Scholar with the Department of Aeronautics and Astronautics, Shanghai Jiao Tong University (SJTU). His research interests include low-resolution quantization, signal processing for wireless communications, intelligent signal processing, computer vision, and information fusion.

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Section: Data Detection and Receiver Designmentioning

confidence: 94%

Section: Data Detection and Receiver Designmentioning

confidence: 99%