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Hybrid analog/digital signal detector with reduction in number of receive antennas for multiuser uplink massive MIMO systems

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SIGNAL PROCESSING 2022年 198卷

作者： Lee, Yinman Natl Chi Nan Univ Dept Elect Engn Puli Taiwan

Low-complexity signal detection with a large number of receive antennas is crucial to the success of multiuser uplink massive multiple-input multiple-output (MIMO) systems. In this paper, we introduce a new hybrid analog/digital signal detector with a reduction in the number of receive antennas (RNRA) for this case. Specifically, a convex optimization problem is formulated to decrease the number of active antennas used for receiving signals. With the proposed hybrid structure, the number of radiofrequency (RF) chains is reduced to equal the number of uplink users, and the tuning of phase shifters in the analog part is free from optimization. Besides, an efficient decision-aided Gauss-Seidel (DA-GS) iteration is used in the digital part to lower the implementation complexity and improve the ultimate error rate of the system at the same time. All these make our detector be comparable to the suboptimal fully-digital minimum-mean-squared error (MMSE) detector in terms of the error-rate performance, and become a complete low-complexity solution for signal detection in this kind of usage. (c) 2022 Elsevier B.V. All rights reserved.

关键词： massive multiple-input multiple-output (MIMO) system Signal detection Antenna selection Hybrid analog digital structure Equal-gain combining (EGC) Gauss-Seidel (GS) iteration

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Optimal Uplink Pilot-Data Power Allocation for Large-Scale Antenna Array-Aided OFDM Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第1期69卷 428-442页

作者： Guo, Xinying Zhang, Jiankang Chen, Sheng Zhu, Chunhua Yang, Jing Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450000 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England King Abdulaziz Univ Jeddah 21589 Saudi Arabia

The effective aggregate rate (AR) performance of large-scale multiple-input multiple-output aided orthogonal frequency division multiplexing systems based on time division duplexing protocol critically depends on uplink (UL) pilot-data power allocations for all users. We derive an asymptotic closed-form expression of the system's achievable AR for typical channel estimation schemes, which is a function of individual users' UL power allocation factors. We prove that the effective AR is a convex function of each UL power allocation factor, when all the other factors are given. The globally optimal UL pilot-data power allocation that maximizes the system's effective AR can easily be obtained using an iterative procedure, by solving an univariate convex optimization with the golden section method for individual UL power allocation factors one by one in each iteration. Owing to the piecewise convexity of the effective AR, this iterative algorithm guarantees to find the unique globally optimal solution with only one iteration. The simulation results confirm that the achievable effective AR is significantly enhanced through the proposed optimization of UL pilot-data power allocation.

关键词： Aggregation rate channel estimation massive multiple-input multiple-output multi-cell systems orthogonal frequency division multiplexing uplink power allocation

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Low-Resolution ADC Quantized Full-Duplex massive MIMO-Enabled Wireless Backhaul in Heterogeneous Networks Over Rician Channels

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第8期19卷 5503-5517页

作者： Anokye, Prince Ahiadormey, Roger Kwao Jo, Han-Shin Song, Changick Lee, Kyoung-Jae Hanbat Natl Univ Dept Elect & Control Engn Daejeon 34158 South Korea Korea Natl Univ Transportat Dept Elect Engn Chungju 27469 South Korea

This paper studies the spectral/energy efficiency (SE/EE) of a heterogeneous network with the backhaul enabled by low-resolution analog-to-digital converters (ADCs) quantized full-duplex massive multiple-input multiple-output (MIMO) over Rician channels. Backhaul communication is completed over two phases. During the first phase, the macro-cell (MC) base station (BS) deploys massive receive antennas and a few transmit antennas;the small-cell (SC) BSs employ large-scale receive antennas and a single transmit antenna. For the second phase, the roles of the transmit and receive antennas are switched. Due to the low-resolution ADCs, we account for quantization noise (QN). We characterize the joint impact of the number of antennas, self-interference, SC-to-SC interference, QN, and Rician K-factor. For the first phase, the SE is enhanced with the massive receive antennas and the loss due to QN is limited. For the second phase, the desired signal and QN have the same order. Therefore, the SE saturates with the massive transmit antennas. As the Rician K-factor increases, the SE converges. Power scaling laws are derived to demonstrate that the transmit power can be scaled down proportionally to the massive antennas. We investigate the EE/SE trade-offs. The envelope of the EE/SE region grows with increase in the Rician K-factor.

关键词： Heterogeneous networks backhaul massive multiple-input multiple-output full-duplex low-resolution analog-to-digital converters Rician fading quantization noise

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Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: massive Access Meets massive MIMO

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 764-779页

作者： Ke, Malong Gao, Zhen Wu, Yongpeng Gao, Xiqi Schober, Robert Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China Shanghai JiaoFong Univ Dept Elect Engn Shanghai 200240 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Friedrich Alexander Univ Erlangen Nnuberg Inst Digital Commun D-91054 Erlangen Germany

This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize joint active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity present in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.

关键词： massive access active user detection channel estimation massive multiple-input multiple-output compressive sensing structured sparsity approximate message passing

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Joint Pilot Allocation and Pilot Sequence Optimization in massive MIMO Systems

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IEEE ACCESS 2020年 8卷 60637-60644页

作者： Nie, Xinhua Zhao, Feng Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China Yulin Normal Univ Guangxi Coll & Univ Key Lab Complex Syst Optimizat & Big Data Proc Yulin 537000 Peoples R China

Pilot contamination (PC) is recognized as a bottleneck for the achievable throughput of multi-cell massive multiple-input multiple-output (MIMO) systems. In this paper, we propose a joint pilot allocation and pilot sequences optimization (JPA-PSO) scheme to mitigate the effects of PC and maximize the system spectral efficiency (SE). Specifically, we first construct a range of pilot sequence in line with the coherent interval. Then, we establish an allocation basis by defining a similarity to distinguish PC among users. An angle of arrival (AOA) positioning method is designed to obtain the user's location information. Under the location information, we can monitor and estimate the distance between users in different cells to denote the potential interference of transmit power among users. Then, we execute pilot allocation strategy in line with the similarity and distance between users in different cells, that is, assign orthogonal pilots to the users with greater similarity and shorter distance. Finally, we provide a pilot sequence optimization algorithm to maximize the system SE. Simulation results verify that combining pilot allocation and pilot sequence optimization can significantly improve the system SE.

关键词： massive multiple-input multiple-output pilot contamination pilot allocation pilot sequence optimization spectral efficiency

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Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond

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Frontiers of Information Technology & Electronic Engineering 2021年第6期22卷 777-789页

作者： Zhao YI Weixia ZOU Xuebin SUN MOE Key Laboratory of Universal Wireless Communications Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

Millimeter wave(mm Wave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5 G and beyond. To realize applications in future vehicular communications, it is important to take a robust mm Wave vehicular network into consideration. However, one challenge in such a network is that mm Wave should provide an ultra-fast and high-rate data exchange among vehicles or vehicle-to-infrastructure(V2 I). Moreover,traditional real-time channel estimation strategies are unavailable because vehicle mobility leads to a fast variation mm Wave channel. To overcome these issues, a channel estimation approach for mm Wave V2 I communications is proposed in this paper. Specifically, by considering a fast-moving vehicle secnario, a corresponding mathematical model for a fast time-varying channel is first established. Then, the temporal variation rule between the base station and each mobile user and the determined direction-of-arrival are used to predict the time-varying channel prior information(PI). Finally, by exploiting the PI and the characteristics of the channel, the time-varying channel is estimated. The simulation results show that the scheme in this paper outperforms traditional ones in both normalized mean square error and sum-rate performance in the mm Wave time-varying vehicular system.

关键词： massive multiple-input multiple-output Millimeter wave Channel estimation Vehicular communication Time-varying

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Graph Neural Network-Based Channel Tracking for massive MIMO Networks

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IEEE COMMUNICATIONS LETTERS 2020年第8期24卷 1747-1751页

作者： Yang, Yindi Zhang, Shun Gao, Feifei Ma, Jianpeng Dobre, Octavia A. Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Tsinghua Univ Inst Artificial Intelligence Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Automat Beijing 100084 Peoples R China Mem Univ Fac Engn & Appl Sci Ece St John NF A1B 3X5 Canada

In this letter, we resort to the graph neural network (GNN) and propose the new channel tracking method for the massive multiple-input multiple-output networks under the high mobility scenario. We first utilize a small number of pilots to achieve the initial channel estimation. Then, we represent the obtained channel data in the form of graphs and describe the channel spatial correlation by the weights along the edges of the graph. Furthermore, we introduce the computation steps of the main unit for the GNN and design a GNN-based channel tracking framework, which includes an encoder, a core network and a decoder. Simulation results corroborate that our proposed GNN-based scheme can achieve better performance than the works with feedforward neural network.

关键词： massive MIMO Channel estimation Correlation Artificial neural networks Feature extraction Decoding massive multiple-input multiple-output graph channel tracking spatial correlation graph neural network

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Machine Learning-Based Channel Prediction in massive MIMO With Channel Aging

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第5期19卷 2960-2973页

作者： Yuan, Jide Ngo, Hien Quoc Matthaiou, Michail Queens Univ Belfast Inst Elect Commun & Informat Technol Belfast BT3 9DT Antrim North Ireland

To support the ever increasing number of devices in massive multiple-input multiple-output (mMIMO) systems, an excessive amount of overhead is required for conventional orthogonal pilot-based channel estimation schemes. To circumvent this fundamental constraint, we design a machine learning (ML)-based time-division duplex scheme in which channel state information (CSI) can be obtained by leveraging the temporal channel correlation. The presence of the temporal channel correlation is due to the stationarity of the propagation environment across time. The proposed ML-based predictors involve a pattern extraction implemented via a convolutional neural network, and a CSI predictor realized by an autoregressive (AR) predictor or an autoregressive network with exogenous inputs recurrent neural network. Closed-form expressions for the user uplink and downlink achievable spectral efficiency and average per-user throughput are provided for the ML-based time division duplex schemes. Our numerical results demonstrate that the proposed ML-based predictors can remarkably improve the prediction quality for both low and high mobility scenarios, and offer great performance gains on the per-user achievable throughput.

关键词： Achievable spectral efficiency channel estimation machine learning massive multiple-input multiple-output

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Pilot Decontamination for massive MIMO Network With UAVs

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IEEE WIRELESS COMMUNICATIONS LETTERS 2020年第11期9卷 1830-1834页

作者： Lu, Rui Wu, Qingqing Zhang, Rui Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Univ Macau Dept Elect & Comp Engn Macau Peoples R China

This letter studies the pilot contamination (PC) problem for massive multiple-input multiple-output (MIMO) networks with coexisting terrestrial users and unmanned aerial vehicles (UAVs). Due to the strong line-of-sight (LoS) air-to-ground channels between UAVs and base stations (BSs), UAVs usually cause a more severe PC issue as compared to the traditional terrestrial users. To mitigate the PC caused by UAVs, we propose a low-complexity distributed scheme by exploiting the full-dimensional beamforming of massive MIMO BSs and the angle-dependent LoS channels between them and high-altitude UAVs. Numerical results show the effectiveness of the proposed pilot decontamination scheme and the significant signal-to-interference-plus-noise ratio (SINR) gains in both the uplink and downlink after pilot decontamination.

关键词： Interference Uplink Channel estimation Signal to noise ratio Downlink Decontamination massive MIMO massive multiple-input multiple-output pilot contamination unmanned aerial vehicle (UAV)

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Deep Learning Based Antenna Selection for Channel Extrapolation in FDD massive MIMO 12

Deep Learning Based Antenna Selection for Channel Extrapolat...

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12th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yang, Yindi Zhang, Shun Gao, Feifei Xu, Chao Ma, Jianpeng Dobre, Octavia A. Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Northwest A&F Univ Sch Informat Engn Yangling Shanxi Peoples R China Mem Univ Fac Engn & Appl Sci St John NF Canada

ISBN: (纸本)9781728172361

In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information, especially in the frequency division duplex mode. To overcome the bottleneck of the limited number of radio links in hybrid beamforming, we utilize the neural networks (NNs) to capture the inherent connection between the uplink and downlink channel data sets and extrapolate the downlink channels from a subset of the uplink channel state information. We study the antenna subset selection problem in order to achieve the best channel extrapolation and decrease the data size of NNs. The probabilistic sampling theory is utilized to approximate the discrete antenna selection as a continuous and differentiable function, which makes the back propagation of the deep learning feasible. Then, we design the proper off-line training strategy to optimize both the antenna selection pattern and the extrapolation NNs. Finally, numerical results are presented to verify the effectiveness of our proposed massive MIMO channel extrapolation algorithm.

关键词： massive multiple-input multiple-output antenna selection channel extrapolation deep learning probabilistic sampling

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