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

Chen, Yiwen; Zhan, Xichao; Shu, Feng; Jie, Qijuan; Cheng, Xin; Zhuang, Zhihong; Wang, Jiangzhou

doi:10.1109/lwc.2022.3204173

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…In order to interpolate the channel weights within EVD gaps, we utilize N s EVD samples to extract the amplitudes and exponentials in (18). Denote the eigenvector samples within one EGVP interval T egsp by…”

Section: Eigenvector Interpolationmentioning

confidence: 99%

“…The channel weight interpolation is enabled by utilizing several samples a m,j (t) to estimate the complex exponential model in (18). This is a classic exponential parameter estimating problem.…”

Section: Assumptionmentioning

confidence: 99%

“…Many traditional algorithms, like Prony [25], maximum likelihood [26], Matrix Pencil [27], are capable to solve this problem. Due to the low complexity and robustness to noise, we utilize Matrix Pencil method to estimate the complex exponential model (18) under a prediction order constraint N s ≥ 2L evd . The detailed Matrix Pencil algorithm is omitted in our paper and can be found in [27], [28].…”

Section: Assumptionmentioning

confidence: 99%

“…Some works tried to alleviate the computation complexity brought by the EVD operation in massive MIMO systems. The authors proposed two lowcomplexity eigenvector-based estimators utilizing partitioned sub-array auto-correlation and cross-correlation combining in [18]. The authors in [19] exploited channel hardening and correlation across subcarriers to interpolate the Gram matrix for precoding.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Partial Reciprocity-Based Channel Prediction Framework for FDD Massive MIMO With High Mobility

Qin

Yin

Cao

et al. 2022

IEEE Trans. Wireless Commun.

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Eigenvector decomposition (EVD) is an inevitable operation to obtain the precoders in practical massive multipleinput multiple-output (MIMO) systems. Due to the large antenna size and at finite computation resources at the base station (BS), the overwhelming computation complexity of EVD is one of the key limiting factors of the system performance. To address this problem, we propose an eigenvector prediction (EGVP) method by interpolating the precoding matrix with predicted eigenvectors. The basic idea is to exploit a few historical precoders to interpolate the rest of them without EVD of the channel state information (CSI). We transform the nonlinear EVD into a linear prediction problem and prove that the prediction of the eigenvectors can be achieved with a complex exponential model. Furthermore, a channel prediction method called fast matrix pencil prediction (FMPP) is proposed to cope with the CSI delay when applying the EGVP method in mobility environments. The asymptotic analysis demonstrates how many samples are needed to achieve asymptotically error-free eigenvector predictions and channel predictions. Finally, the simulation results demonstrate the spectral efficiency improvement of our scheme over the benchmarks and the robustness to different mobility scenarios.

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Section: Eigenvector Interpolationmentioning

confidence: 99%

Section: Assumptionmentioning

confidence: 99%

Section: Assumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Partial Reciprocity-Based Channel Prediction Framework for FDD Massive MIMO With High Mobility

Qin

Yin

Cao

et al. 2022

IEEE Trans. Wireless Commun.

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“…The above proposed methods significantly reduce the circuit cost and computational complexity, but accompanied by a substantial performance loss. In order to achieve a significant reduction in complexity while maintaining the high performance of the FD structure, the authors proposed three low-complexity methods for reducing complexity of eigenvalue decomposition in [9], the latter two methods could achieve more than two orders of magnitude complexity reduction and achieve excellent performance.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-aided Low-complexity DOA Estimators for Ultra-Massive MIMO Overlapped Receive Array

Chen¹,

Zhan²,

Feng³

2023

Preprint

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Massive multiple input multiple output(MIMO)based fully-digital receive antenna arrays bring huge amount of complexity to both traditional direction of arrival(DOA) estimation algorithms and neural network training, which is difficult to satisfy high-precision and low-latency applications in future wireless communications. To address this challenge, two estimators called OPSC and OSAP-CBAM-CNN are proposed in this paper. The computational complexity of the traditional DOA algorithm is first considered to be reduced by dividing the total set of antennas into multiple overlapped subarrays uniformly, each subarray crosses each other proportionally and performs DOA estimation to generate coarse angles, and all angles are coherently combined to get the better estimation, the final DOA estimation can given by maximum likelihood alternating projection(ML-AP) in a very small range, which has a better performance than the direct partitioning of subarrays. To further reduce the complexity of traditional estimation algorithms, deep neural networks(DNN) are utilized to offline train the relationship between the received signal covariance matrix and the estimated angles. Due to the high complexity of the training network based on large-scale arrays, in the OSAP-CBAM-CNN method, the complex network is divided into several smaller networks based on the overlapped subarray to give rough DOA estimations, followed by coherent combining and AP algorithm to get the final DOA estimation. Simulation results show that as the number of antennas goes to large-scale, the proposed methods can achieve a remarkable complexity reduction over conventional ML-AP algorithm.

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Dual-Sparse Parallel Nested Array for Two-Dimensional Direction of Arrival Estimation

Jiang,

Huang,

Yang

2024

Circuits Syst Signal Process

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

Cited by 11 publications

References 10 publications

A Partial Reciprocity-Based Channel Prediction Framework for FDD Massive MIMO With High Mobility

A Partial Reciprocity-Based Channel Prediction Framework for FDD Massive MIMO With High Mobility

Deep-learning-aided Low-complexity DOA Estimators for Ultra-Massive MIMO Overlapped Receive Array

Dual-Sparse Parallel Nested Array for Two-Dimensional Direction of Arrival Estimation

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