Bayesian Channel Estimation Algorithms for Massive MIMO Systems With Hybrid Analog-Digital Processing and Low-Resolution ADCs

Ding, Yacong; Chiu, Sung-En; Rao, Bhaskar D.

doi:10.1109/jstsp.2018.2814008

Cited by 57 publications

(48 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in the high SNR regime with c > 0, which means that x * and cx * are indistinguishable because the magnitude information is lost by one-bit ADCs. The degradation of the recovery accuracy in the high SNR regime with one-bit ADCs is an inevitable phenomenon, as observed from other previous works on low-resolution ADCs [11], [14], [15], [33], [37]. In Figs.…”

Section: Resultssupporting

confidence: 62%

Channel estimation via gradient pursuit for mmWave massive MIMO systems with one-bit ADCs

Kim

Choi

2019

J Wireless Com Network

View full text Add to dashboard Cite

In millimeter wave (mmWave) massive multipleinput multiple-output (MIMO) systems, one-bit analog-to-digital converters (ADCs) are employed to reduce the impractically high power consumption, which is incurred by the wide bandwidth and large arrays. In practice, the mmWave band consists of a small number of paths, thereby rendering sparse virtual channels. Then, the resulting maximum a posteriori (MAP) channel estimation problem is a sparsity-constrained optimization problem, which is NP-hard to solve. In this paper, iterative approximate MAP channel estimators for mmWave massive MIMO systems with one-bit ADCs are proposed, which are based on the gradient support pursuit (GraSP) and gradient hard thresholding pursuit (GraHTP) algorithms. The GraSP and GraHTP algorithms iteratively pursue the gradient of the objective function to approximately optimize convex objective functions with sparsity constraints, which are the generalizations of the compressive sampling matching pursuit (CoSaMP) and hard thresholding pursuit (HTP) algorithms, respectively, in compressive sensing (CS). However, the performance of the GraSP and GraHTP algorithms is not guaranteed when the objective function is ill-conditioned, which may be incurred by the highly coherent sensing matrix. In this paper, the band maximum selecting (BMS) hard thresholding technique is proposed to modify the GraSP and GraHTP algorithms, namely the BMSGraSP and BMSGraHTP algorithms, respectively. The BMSGraSP and BMSGraHTP algorithms pursue the gradient of the objective function based on the band maximum criterion instead of the naive hard thresholding. In addition, a fast Fourier transform-based (FFTbased) fast implementation is developed to reduce the complexity. The BMSGraSP and BMSGraHTP algorithms are shown to be both accurate and efficient, whose performance is verified through extensive simulations.

show abstract

Section: Resultssupporting

confidence: 62%

Channel estimation via gradient pursuit for mmWave massive MIMO systems with one-bit ADCs

Kim

Choi

2019

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…More specifically, the authors of [12] proposed random hash functions to generate a random beamforming codebook whose acquisition time, they showed, grows only logarithmically with target resolution/error probability. The logarithmic scaling (of search time with angular resolution) could also be obtained when viewing the problem as that of sparse estimation with compressive measurements (see [14] and references therein). Indeed, the authors of [13] recover the signal direction with a non-negative least square estimate from Compressive Sensing by measuring the received power via a random beamforming codebook which hashes the angular directions similarly to [12].…”

Section: Introductionmentioning

confidence: 99%

Active Learning and CSI Acquisition for mmWave Initial Alignment

Chiu

Ronquillo

Javidi

2019

IEEE J. Select. Areas Commun.

Self Cite

109

133

View full text Add to dashboard Cite

Millimeter wave (mmWave) communication with large antenna arrays is a promising technique to enable extremely high data rates due to large available bandwidth in mmWave frequency bands. In addition, given the knowledge of an optimal directional beamforming vector, large antenna arrays have been shown to overcome both the severe signal attenuation in mmWave as well as the interference problem. However, fundamental limits on achievable learning rate of an optimal beamforming vector remain.This paper considers the problem of adaptive and sequential optimization of the beamforming vectors during the initial access phase of communication. With a single-path channel model, the problem is reduced to actively learning the Angle-of-Arrival (AoA) of the signal sent from the user to the Base Station (BS). Drawing on the recent results in the design of a hierarchical beamforming codebook [1], sequential measurement dependent noisy search strategies [2], and active learning from an imperfect labeler [3], an adaptive and sequential alignment algorithm is proposed.For any given resolution and error probability of the estimated AoA, an upper bound on the expected search time of the proposed algorithm is derived via Extrinsic Jensen-Shannon Divergence. The upper bound demonstrates that the search time of the proposed algorithm asymptotically matches the performance of the noiseless bisection search up to a constant factor, in effect, characterizing the AoA acquisition rate. Furthermore, the upper bound shows that the acquired AoA error probability decays exponentially fast with the search time with an exponent that is a decreasing function of the acquisition rate.Numerically, the proposed algorithm is compared with prior work where a significant improvement of the system communication rate is observed. Most notably, in the relevant regime of low (−10dB to +5dB) raw SNR, this establishes the first practically viable solution for initial access and, hence, the first demonstration of stand-alone mmWave communication.

show abstract

“…For a given r 1 , the distance r 11 in Fig. 1 can be solved from (r 11 cos θ 1 ) 2 + (r 11 sin θ 1 + s 1 ) 2 = r 2 1 (6) and, subsequently, the local AoA θ 2 can be expressed as θ 2 = tan −1 s 1 − s 2 + r 11 sin θ 1 r 11 cos θ 1 .…”

Section: B Statistical Dependency Among Local Aoasmentioning

confidence: 99%

A Geometry-aided Message Passing Method for AoA-Based Short Range MIMO Channel Estimation

Kaleva

Myers

Tölli

et al. 2019

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

View full text Add to dashboard Cite

Short range channels commonly arise in millimeter wave (mmWave) wearable settings, where the length of the antenna arrays can be comparable to the distance between the radios. Conventional mmWave MIMO channel estimation techniques based on the far field assumption may perform poorly in short range settings due to the large angular spread and, hence, high available rank. We propose a geometry-aided message passing algorithm that exploits structure in short range line-ofsight (LoS) channels for spatial sub-Nyquist channel estimation. Our approach parametrizes the channel using angle-of-arrivals (AoAs) that are locally defined for subarrays of an antenna array. Furthermore, it leverages the dependencies between the local AoAs using factors based on the array geometry. We show that the LoS MIMO channel can be reconstructed using the derived local AoA estimates and the known transceiver geometry. The proposed approach achieves a reasonable rate with greatly reduced pilot transmissions when compared to exhaustive beam search-based local AoA estimation.

show abstract

Bayesian Channel Estimation Algorithms for Massive MIMO Systems With Hybrid Analog-Digital Processing and Low-Resolution ADCs

Cited by 57 publications

References 42 publications

Channel estimation via gradient pursuit for mmWave massive MIMO systems with one-bit ADCs

Channel estimation via gradient pursuit for mmWave massive MIMO systems with one-bit ADCs

Active Learning and CSI Acquisition for mmWave Initial Alignment

A Geometry-aided Message Passing Method for AoA-Based Short Range MIMO Channel Estimation

Contact Info

Product

Resources

About