Toward Massive Connectivity for IoT in Mixed-ADC Distributed Massive MIMO

Yuan, Jide; He, Qi; Matthaiou, Michail; Quek, Tony Q. S.; Jin, Shi

doi:10.1109/jiot.2019.2957281

Cited by 32 publications

(19 citation statements)

References 53 publications

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“…The GTurbo-MMV algorithm in [10] exploits the joint sparsity of channel response and obtains good results and robustness in channel estimation as well as user detection. An algorithm that can simultaneously achieve good performance in uplink channel estimation and user activity detection in mixed-ADC distributed massive MIMO is proposed in [11].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Channel Estimation for Massive-MIMO With Mixed-Resolution ADCs and Low-Resolution Information Utilization

et al. 2021

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In this paper, we propose two deep-learning based uplink channel estimation approaches that can utilize not only high-resolution-ADC-quantized but also low-resolution-ADC-quantized received pilot signals to improve estimation performance for mixed analog-to-digital converters (ADCs) massive multipleinput multiple-output (MIMO) systems. In each approach, low-resolution-ADC-quantized received pilot signals are utilized with one of three different schemes, i.e., High-resolution quantized pilot + All lowresolution quantized pilot(High+All), High-resolution quantized pilot + Argument of low-resolution quantized pilot (High+Arg) or High-resolution quantized pilot + Modulus of low-resolution quantized pilot (High+Mod). All three schemes include the intact quantized pilot signals at high-resolution antennas, but the quantized pilot signals at low-resolution ADCs are exploited differently in each scheme. Modified selectiveinput prediction deep neural network (Modified SIP-DNN) is developed to predict more realistic channels and test the effectiveness of the utilization scheme. To achieve further performance improvement, a deep neural network (DNN) based two-stage network is proposed where the recovering DNN (RC-DNN) in the first stage forms a coarse estimation for channels at antennas with low-resolution ADCs and the refining DNN (Ref-DNN) in the second stage outputs a refined estimation for channels at all antennas. Simulation results show that our proposed approaches outperform state-of-the-art channel estimation method especially when most antennas are equipped with low-resolution ADCs.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Channel Estimation for Massive-MIMO With Mixed-Resolution ADCs and Low-Resolution Information Utilization

et al. 2021

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“…Ke et al [18] addressed the JUICE problem in an enhanced mobile broadband system and proposed a generalized AMP algorithm that exploits the channel sparsity present in both the spatial and the angular domains. Yuan et al [19] addressed the JUICE problem in a distributed mMTC system with mixed-analogto-digital converters under two different user traffic classes.…”

Section: A Related Workmentioning

confidence: 99%

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Djelouat

Leinonen

Juntti

2022

IEEE Trans. Wireless Commun.

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Grant-free access is considered as a key enabler for massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted 2,1-norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximum a posteriori probability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms.

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“…In addition, [13] considers the utilization of radio frequency energy transfer using unmanned aerial vehicles to extend the operating time of battery-limited IoT devices. [14] considers the access phase for IoT networks in a mixed-analog-todigital converter distributed massive MIMO system, in which users are classified into light-load users and heavy-load users depending on their traffic load requirements. To meet the low-latency and low-cost demands in IoT, the access scheme for both types of users are designed in a grant-free fashion.…”

Section: Related Workmentioning

confidence: 99%

Beamforming Optimization and Power Allocation for User-Centric MIMO-NOMA IoT Networks

Wang

2021

IEEE Access

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Toward Massive Connectivity for IoT in Mixed-ADC Distributed Massive MIMO

Cited by 32 publications

References 53 publications

Deep Learning-Based Channel Estimation for Massive-MIMO With Mixed-Resolution ADCs and Low-Resolution Information Utilization

Deep Learning-Based Channel Estimation for Massive-MIMO With Mixed-Resolution ADCs and Low-Resolution Information Utilization

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Beamforming Optimization and Power Allocation for User-Centric MIMO-NOMA IoT Networks

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