Massive MIMO Channel Estimation Using Signed Measurements with Antenna-Varying Thresholds

Liu, Fangqing; Zhu, Heng; Li, Jian; Wang, Pu; Orlik, Philip V.

doi:10.1109/ssp.2018.8450804

Cited by 24 publications

(14 citation statements)

References 23 publications

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“…Additionally, a nonzero estimation error floor arises due to 1-bit quantization and cannot be eliminated by increasing SNR, as also discussed in [15]. The CRBs of θ are provided as benchmarks, which are derived in [6]. Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System with One-Bit Quantization via Approximate Message Passing

Gao

Cheng

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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We develop a direction of arrival (DoA) and channel estimation algorithm for the one-bit quantized millimeterwave (mmWave) massive multiple-input multiple-output (MIMO) system. By formulating the estimation problem as a noisy one-bit compressed sensing problem, we propose a computationally efficient gridless solution based on the expectationmaximization generalized approximate message passing (EM-GAMP) approach. The proposed algorithm does not need the prior knowledge about the number of DoAs and outperforms the existing methods in distinguishing extremely close DoAs for the case of one-bit quantization. Both the DoAs and the channel coefficients are estimated for the case of one-bit quantization. The simulation results show that the proposed algorithm has effective estimation performances when the DoAs are very close to each other.

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Section: Simulation Resultsmentioning

confidence: 99%

“…The work [4] provided a gridless estimation method based on the atomic norm and the Vandermonde decomposition. In [6], 1-bit RELAX algorithm which extended the maximum likelihood (ML) estimator was utilized. However, this method needs iterative 2-dimensional coarse searches, which is computationally prohibitive and impractical.…”

Section: Introductionmentioning

confidence: 99%

Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System with One-Bit Quantization via Approximate Message Passing

Gao

Cheng

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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“…Mathematically, the goal of the target reconstruction problem is to find a sparse vector x κ consistent with the observation model (35). Here we recover x κ via Bayesian methods.…”

Section: B Gamp Reconstructionmentioning

confidence: 99%

“…The SNR losses indicate that the dynamic range is reduced for one-bit radar due to the application of one-bit quantization. A possible solution is to adopt the time varying threshold in one-bit radar system by accommodating large dynamic range of the received signal [7,18,35].…”

Section: ) Fas Caused By High-order Harmonicsmentioning

confidence: 99%

One-Bit LFMCW Radar: Spectrum Analysis and Target Detection

Jin

Zhu

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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One-bit radar involving direct one-bit sampling is a promising technology for many civilian applications due to its low-cost and low-power consumptions. In this paper, problems encountered by one-bit LFMCW radar are studied and a two-stage target detection approach termed as DR-GAMP is proposed.Firstly, the spectrum of one-bit signal in a scenario of multiple targets is analyzed. It is indicated that high-order harmonics may result in false alarms (FAs) and cannot be neglected. Secondly, DR-GAMP is used to suppress the high order harmonics. Specifically, linear preprocessing and predetection are proposed to perform dimension reduction (DR), and then, generalized approximate message passing (GAMP) is utilized to suppress high-order harmonics. Finally, numerical simulations are conducted to evaluate the performance of one-bit LFMCW radar with typical parameters. It is shown that compared to conventional radar with linear processing approach, one-bit LFMCW radar has 0.5 dB performance gain when the input signal-to-noise ratios (SNRs) of targets are low. Moreover, it has 1.6 dB performance loss in a scenario with an additional high SNR target.

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“…More precisely, one-bit sampling can be seen as a process through which we repeatedly compare the amplitude of a signal (at each sample) to some reference threshold level and use only one bit to convey whether the signal amplitude resides above or below that threshold. Due to its appealing sampling properties, the problem of recovering a signal from its one-bit measurements has attracted a great deal of interest over the past few years [4][5][6][7][8]. Therefore, it is vital to develop algorithms that can deal with low-resolution samples for different applications.…”

Section: Introductionmentioning

confidence: 99%

Deep Signal Recovery with One-bit Quantization

Khobahi

Naimipour

Soltanalian

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference. In this paper, we consider the application of the deep unfolding technique in the problem of signal reconstruction from its one-bit noisy measurements. Namely, we propose a model-based machine learning method and unfold the iterations of an inference optimization algorithm into the layers of a deep neural network for one-bit signal recovery. The resulting network, which we refer to as DeepRec, can efficiently handle the recovery of high-dimensional signals from acquired one-bit noisy measurements. The proposed method results in an improvement in accuracy and computational efficiency with respect to the original framework as shown through numerical analysis.

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Massive MIMO Channel Estimation Using Signed Measurements with Antenna-Varying Thresholds

Cited by 24 publications

References 23 publications

Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System with One-Bit Quantization via Approximate Message Passing

Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System with One-Bit Quantization via Approximate Message Passing

One-Bit LFMCW Radar: Spectrum Analysis and Target Detection

Deep Signal Recovery with One-bit Quantization

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