Low Complexity Iterative Detection for a Large-Scale Distributed MIMO Prototyping System

Yang, Feng; Wang, Menghan; Wang, Dongming; You, Xiaohu

doi:10.1109/icc.2019.8761605

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…A pioneering work [20] has investigated the asynchronous reception effects caused by DSDs on distributed massive MIMO-OFDM systems at microwave frequencies. The same effect has been observed in a distributed MIMO prototyping system [21]. However, to our knowledge, the impact of DSDs on mmWave DASs with hybrid structures has not been investigated.…”

Section: Introductionsupporting

confidence: 70%

Hybrid beamforming design for mmWave OFDM distributed antenna systems

Wang

Huo

Dong

et al. 2020

Sci. China Inf. Sci.

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Energy-efficient Butler-matrix-based hybrid beamforming for multiuser mmWave MIMO system SCIENCE CHINA Information Sciences 60, 080304 (2017); Circulate shifted OFDM chirp waveform diversity design with digital beamforming for MIMO SAR SCIENCE CHINA Information Sciences 60, 102307 (2017); Design of multi-antenna relaying for OFDM in impulsive noise environment 数字通信和网络 5, 189 (2019); Distributed space-time-frequency coding for cooperative OFDM systems Science in China Series F-Information Sciences 52, 2424 (2009); Design of a novel hybrid intercarrier interference mitigation technique through wavelet implication in an OFDM system

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Section: Introductionsupporting

confidence: 70%

Hybrid beamforming design for mmWave OFDM distributed antenna systems

Wang

Huo

Dong

et al. 2020

Sci. China Inf. Sci.

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“…CF massive MIMO has been shown to achieve higher spectral efficiency than centralized massive MIMO and small cell [117]. Our recent prototyping system in largescale distributed MIMO has demonstrated that the data rate of 10 Gbps could be achieved by a 128×128 large-scale distributed MIMO (or CF massive MIMO) with 100 MHz bandwidth [118]. However, CF massive MIMO is also facing some obstacles such as high implementation complexity, high requirement of backhaul/fronthaul, and difficulty in synchronization and channel state information (CSI) acquisition.…”

Section: Cf Massive Mimomentioning

confidence: 99%

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

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The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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“…However, this optimization is a non-convex problem which causes a high computational complexity. In [130], a low-complexity iterative soft-input soft-output (SISO) detection algorithm in a distributed large-scale MIMO system based on an improved MMSE iterative soft decision interference cancellation is proposed and experimentally tested. The prototype system has shown that a data rate of 10 Gbps could be achieved by a 128×128 cell-free massive MIMO with 100 MHz bandwidth.…”

Section: B Detection In Cell-free Massive Mimomentioning

confidence: 99%

Deep Learning for Massive MIMO Uplink Detectors

Albreem

Alhabbash²,

Shahabuddin

et al. 2022

IEEE Commun. Surv. Tutorials

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Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers' performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector's architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.

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Low Complexity Iterative Detection for a Large-Scale Distributed MIMO Prototyping System

Cited by 11 publications

References 26 publications

Hybrid beamforming design for mmWave OFDM distributed antenna systems

Hybrid beamforming design for mmWave OFDM distributed antenna systems

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Deep Learning for Massive MIMO Uplink Detectors

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