TY - GEN

T1 - Sparse Activity Detection in Cell-Free Massive MIMO systems

AU - Guo, Mangqing

AU - Gursoy, M. Cenk

AU - Varshney, Pramod K.

PY - 2020/6

Y1 - 2020/6

N2 - We investigate the sparse activity detection problem in cell-free massive multiple-input multiple-output (MIMO) systems in this paper. With the approximate message passing (AMP) algorithm, the received pilot signals at the access points (APs) are decomposed into independent circularly symmetric complex Gaussian noise corrupted components. By using the minimum mean-squared error (MMSE) denoiser during the AMP procedure, we obtain a threshold detection rule, and analytically describe the noise covariance matrix of the corrupted components via the state evolution equations, which is helpful for the performance analysis of the detection rule. Using the law of large numbers, it can be shown that the error probability of this threshold detection rule tends to zero when the number of APs, pilots and users tend to infinity while the ratio of the number of pilots and users is kept constant. Numerical results show that the error probability decreases while the number of APs increases, corroborating our theoretical analysis. In addition, we investigate the relationship between the error probability of the threshold detection rule and the number of symbols used for pilot transmissions during each channel coherence interval via numerical results.

AB - We investigate the sparse activity detection problem in cell-free massive multiple-input multiple-output (MIMO) systems in this paper. With the approximate message passing (AMP) algorithm, the received pilot signals at the access points (APs) are decomposed into independent circularly symmetric complex Gaussian noise corrupted components. By using the minimum mean-squared error (MMSE) denoiser during the AMP procedure, we obtain a threshold detection rule, and analytically describe the noise covariance matrix of the corrupted components via the state evolution equations, which is helpful for the performance analysis of the detection rule. Using the law of large numbers, it can be shown that the error probability of this threshold detection rule tends to zero when the number of APs, pilots and users tend to infinity while the ratio of the number of pilots and users is kept constant. Numerical results show that the error probability decreases while the number of APs increases, corroborating our theoretical analysis. In addition, we investigate the relationship between the error probability of the threshold detection rule and the number of symbols used for pilot transmissions during each channel coherence interval via numerical results.

KW - Approximate message passing

KW - cell-free massive MIMO

KW - minimum mean-squared error denoiser

KW - sparse activity detection

UR - http://www.scopus.com/inward/record.url?scp=85090426045&partnerID=8YFLogxK

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U2 - 10.1109/ISIT44484.2020.9174169

DO - 10.1109/ISIT44484.2020.9174169

M3 - Conference contribution

AN - SCOPUS:85090426045

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 1177

EP - 1182

BT - 2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Symposium on Information Theory, ISIT 2020

Y2 - 21 July 2020 through 26 July 2020

ER -