Impact of Low-Resolution ADC on DOA Estimation Performance for Massive MIMO Receive Array

Shi, Baihua; Zhu, Xi‐Cheng; Qian, Yuwen; Zhang, Yijin; Shu, Feng; Wang, Jiangzhou

doi:10.1109/jsyst.2021.3139449

Cited by 26 publications

(19 citation statements)

References 14 publications

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“…A CS-based framework can reduce the complexity of the hardware required for DOA estimation, and it is one of the most attractive research areas in B5G/6G wireless communications. Since DOA estimation mainly relies on spatial-temporal measurements, CS-based DOA estimation frameworks can be divided into three categories, namely spatial CS frameworks [ 48 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ], temporal CS frameworks [ 60 , 61 , 62 , 63 ], and spatial-temporal CS frameworks [ 64 ].…”

Section: Spatial/temporal Compressive Samplingmentioning

confidence: 99%

“…In [ 61 ], the DOA estimation issue is considered under the conditions of a low-bit ADC, and a deep-learning network is implemented to eliminate quantization noise. In [ 62 ], a linear additive quantization noise model is considered for low-bit sampling DOA estimation issues, and an improved MUSIC method is introduced. The impact of low-resolution ADC on DOA estimation is analyzed in detail.…”

Section: Spatial/temporal Compressive Samplingmentioning

confidence: 99%

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DOA Estimation in B5G/6G: Trends and Challenges

Ruan

Wang

Wen

et al. 2022

Sensors

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Direction-of-arrival (DOA) estimation is the preliminary stage of communication, localization, and sensing. Hence, it is a canonical task for next-generation wireless communications, namely beyond 5G (B5G) or 6G communication networks. Both massive multiple-input multiple-output (MIMO) and millimeter wave (mmW) bands are emerging technologies that can be implemented to increase the spectral efficiency of an area, and a number of expectations have been placed on them for future-generation wireless communications. Meanwhile, they also create new challenges for DOA estimation, for instance, through extremely large-scale array data, the coexistence of far-field and near-field sources, mutual coupling effects, and complicated spatial-temporal signal sampling. This article discusses various open issues related to DOA estimation for B5G/6G communication networks. Moreover, some insights on current advances, including arrays, models, sampling, and algorithms, are provided. Finally, directions for future work on the development of DOA estimation are addressed.

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Section: Spatial/temporal Compressive Samplingmentioning

confidence: 99%

Section: Spatial/temporal Compressive Samplingmentioning

confidence: 99%

DOA Estimation in B5G/6G: Trends and Challenges

Ruan

Wang

Wen

et al. 2022

Sensors

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“…In [24], a generalized sparse Bayesian learning algorithm is integrated into the 1-bit DOA estimation. In [25], a new receive array framework with low-resolution ADCs was designed, and a closed-form expression of CRLB was derived to evaluate the performance loss. From analysis, it is found that 2-3bit ADC is sufficient to achieve a trivial performance loss.…”

Section: Introductionmentioning

confidence: 99%

Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array

Zhan¹,

Chen²,

Feng³

et al. 2022

Preprint

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For a sub-connected hybrid multiple-input multipleoutput (MIMO) receiver with K subarrays and N antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. First, a DOA estimator of maximizing received power (Max-RP) is proposed to find the maximum value of K-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation (Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of Max-RP. Finally, to achieve the CRLB, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low-computational-complexities. In particular, the proposed Root-MUSIC plus Max-RP-QI scheme can reach the CRLB, and the proposed Max-RP and Max-RP-QI are still some performance losses 2dB ∼ 4dB compared to the CRLB.

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“…In [12], DOA estimation was performed for non-circular sources using a large uniform linear array of single snapshots, and the computational complexity was reduced in the process by using a Newton-Raphson iterative method. In [13], the authors made an analysis of performance loss on DOA estimation using a MIMO receive array with lowresolution analog-to-digital convertors (ADCs) and found the fact that three bits is sufficient to achieve a trivial performance loss.…”

Section: Introductionmentioning

confidence: 99%

Two Low-complexity DOA Estimators for Massive/Ultra-massive MIMO Receive Array

Chen¹,

Zhan²,

Feng³

et al. 2022

Preprint

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Eigen-decomposition-based direction finding methods of using large-scale/ultra-large-scale fully-digital receive antenna arrays leads to a high or ultra-high complexity. To address the complexity dilemma, in this paper, two low-complexity estimators are proposed: partitioned subarray combining (PSAC) and power iteration max correlation successive convex approximation (PI-Max-CSCA). Compared with the conventional nopartitioned direction finding method like root multiple signal classification (Root-MUSIC), in PSAC method, the total set of antennas are equally partitioned into subsets of antennas, called subarrays, each subarray performs independent DOA estimation, and finally all DOA estimates are coherently combined to give the final estimate. In PI-Max-CSCA method, using a fraction of all subarrays to make an initial coarse direction measurement (ICDM), the power iterative method is adopted to compute the more precise steering vector (SV) by exploiting the total array, and a more accurate DOA value is found using ICDM and SV through the maximum correlation method solved by successive convex approximation. Simulation results show that as the number of antennas goes to large-scale, the proposed PSCA and PI-Max-CSCA can achieve a dramatic complexity reduction over conventional Root-MUISC. Particularly, the PI-Max-CSCA can reach the Cramer-Rao lower bound (CRLB) while the former shows a substantial precision loss.

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Impact of Low-Resolution ADC on DOA Estimation Performance for Massive MIMO Receive Array

Cited by 26 publications

References 14 publications

DOA Estimation in B5G/6G: Trends and Challenges

DOA Estimation in B5G/6G: Trends and Challenges

Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array

Two Low-complexity DOA Estimators for Massive/Ultra-massive MIMO Receive Array

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