Affine linear transformation based sphere decoder for 1-bit ADC MIMO-constant envelope modulation

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

doi:10.1109/ict.2016.7500482

Cited by 10 publications

(5 citation statements)

References 14 publications

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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%

“…The results demonstrated that the proposed method significantly outperforms the other MIMO detectors with hard-decision outputs. In addition, the signal decoding techniques for low-resolution ADCs MIMO-CEM are reported in [146]- [148]. Doaa et al (2016) [146] designed a low complexity sphere decoder.…”

Section: Data Detection and Receiver Designmentioning

confidence: 99%

“…In addition, the signal decoding techniques for low-resolution ADCs MIMO-CEM are reported in [146]- [148]. Doaa et al (2016) [146] designed a low complexity sphere decoder. In their method, the baseband MIMO-CEM is used to define the sphere radius and enumerate the candidate sequences laying within the sphere.…”

Section: Data Detection and Receiver Designmentioning

confidence: 99%

“…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. Therefore, the redundancy in the selected candidates can be greatly reduced, thereby reducing the computational complexity.…”

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%

Section: Data Detection and Receiver Designmentioning

confidence: 99%

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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“…The aim of this paper is to consider the affine transform modulation (ATM) to represent a sequence of Quadrature Amplitude Modulation (QAM) symbols as the affine transform (AT) of its bit labels. Further, we propose a reduced complexity SD algorithm based on the ATM concept, representing the received vector as a linear transformation of the transmitted binary information plus the receiver noise 5. The complexity reduction relies on a subtle simplification in the cost dominant inner function of every SD algorithm, the Euclidean distance (ED) computation at each visited node of the tree search structure.…”

Section: Introductionmentioning

confidence: 99%

Sphere detector with low complexity Euclidean distance computation based on affine transform modulation

Gaspar

Mendes

Pimenta

2022

Electronics Letters

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The foreseen scale of connected autonomous and low power consumption devices in the near future is a challenging aspect for the next generation of mobile communication networks. As the number of available connection resources is limited, frequency‐time multiplexing may not be sufficient to meet the expected demand for capacity, where scheduling solutions might incur in undesired latency levels and also increase the power expenditure to meet a more stringent synchronisation. In this scenario, spatial multiplexing based on multi‐user multiple‐input multiple‐output can extend the uplink capacity at the cost of inter‐antenna interference at the base station, requiring more complex detection schemes. The sphere detector can mitigate the inter‐antenna interference, achieving close‐to‐optimum bit error rate performance with average polynomial complexity that still challenges the field programmable gate array implementation in terms of resources and area. To further reduce the sphere detector complexity, the authors introduce the concept of affine transform modulation, which allows us to describe the received signal in terms of the sequence of transmitted bits per symbol. The authors demonstrate that this approach allows to evaluate the Euclidean distance in the sphere detector algorithm in terms of the transmitted bits, replacing the complex inner product by a complex accumulator in its extensively accessed core function. The approach proposed in this paper leads to a considerable complexity reduction in terms of FLOPs for low‐order modulations while attaining the conventional sphere detector performance.

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