A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems
محورهای موضوعی : Communication Systems & Devices
Mojtaba Amiri
1
,
Amir Akhavan
2
1 - School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 - Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
کلید واژه: Massive MIMO, MMSE Detector, Error Recovery, Compressive Sensing, Iteratively Reweighted Least Squares (IRLS) Method.,
چکیده مقاله :
Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in large-scale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the compressive sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods.
Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in large-scale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the compressive sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods.
[1] E. G. Larsson, O. Edfors, F. Tufvesson, and T. L. Marzetta, "Massive MIMO for next generation wireless systems," IEEE communications magazine, vol. 52, no. 2, pp. 186-195, 2014.
[2] E. Björnson, E. G. Larsson, and T. L. Marzetta, "Massive MIMO: Ten myths and one critical question," IEEE Communications Magazine, vol. 54, no. 2, pp. 114-123, 2016.
[3] E. Björnson, J. Hoydis, M. Kountouris, and M. Debbah, "Massive MIMO systems with non-ideal hardware: Energy efficiency, estimation, and capacity limits," IEEE Transactions on Information Theory, vol. 60, no. 11, pp. 7112-7139, 2014.
[4] M. A. Albreem, M. Juntti, and S. Shahabuddin, "Massive MIMO detection techniques: a survey," IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3109-3132, 2019.
[5] M. A. Albreem et al., "Low complexity linear detectors for massive MIMO: A comparative study," IEEE Access, vol. 9, pp. 45740-45753, 2021.
[6] L. Fang, L. Xu, and D. D. Huang, "Low Complexity Iterative MMSE-PIC Detection for Medium-Size Massive MIMO," IEEE Wireless Communications Letters, vol. 5, no. 1, pp. 108-111, 2016.
[7] M. Wu, C. Dick, J. R. Cavallaro, and C. Studer, "High-Throughput Data Detection for Massive MU-MIMO-OFDM Using Coordinate Descent," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 63, no. 12, pp. 2357-2367, 2016.
[8] L. Dai, X. Gao, X. Su, S. Han, I. C. L, and Z. Wang, "Low-Complexity Soft-Output Signal Detection Based on Gauss&Side Method for Uplink Multiuser Large-Scale MIMO Systems," IEEE Transactions on Vehicular Technology, vol. 64, no. 10, pp. 4839-4845, 2015.
[9] G. Peng, L. liu, P. Zhang, S. Yin, and S. Wei, "Low-Computing-Load, High-Parallelism Detection Method based on Chebyshev Iteration for Massive MIMO Systems with VLSI Architecture," IEEE Transactions on Signal Processing, vol. PP, no. 99, pp. 1-1, 2017.
[10] A. Elgabli, A. Elghariani, V. Aggarwal, and M. R. Bell, "A low-complexity detection algorithm for uplink massive MIMO systems based on alternating minimization," IEEE Wireless Communications Letters, vol. 8, no. 3, pp. 917-920, 2019.
[11] M. Amiri and M. F. Naeiny, "Low-Complexity Iterative Detection for Uplink Multiuser Large-Scale MIMO," Journal of Information Systems and Telecommunication (JIST), vol. 1, no. 29, p. 25, 2020.
[12] Z. Gao, L. Dai, S. Han, I. Chih-Lin, Z. Wang, and L. Hanzo, "Compressive sensing techniques for next-generation wireless communications," IEEE Wireless Communications, vol. 25, no. 3, pp. 144-153, 2018.
[13] M. Amiri and A. Akhavan, "An iterative detector based on sparse bayesian error recovery for uplink large-scale MIMO systems," AEU-International Journal of Electronics and Communications, vol. 138, p. 153848, 2021.
[14] R. Ran, J. Wang, S. K. Oh, and S. N. Hong, "Sparse-aware minimum mean square error detector for MIMO systems," IEEE Communications Letters, vol. 21, no. 10, pp. 2214-2217, 2017.
[15] S. Kwon, J. Wang, and B. Shim, "Multipath matching pursuit," IEEE Transactions on Information Theory, vol. 60, no. 5, pp. 2986-3001, 2014.
[16] D. L. Donoho, Y. Tsaig, I. Drori, and J.-L. Starck, "Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit," IEEE transactions on Information Theory, vol. 58, no. 2, pp. 1094-1121, 2012.
[17] Z. Zhang, Y. Xu, J. Yang, X. Li, and D. Zhang, "A survey of sparse representation: algorithms and applications," IEEE access, vol. 3, pp. 490-530, 2015.
[18] H. Xu, C. Caramanis, and S. Mannor, "Robust regression and lasso," IEEE Transactions on Information Theory, vol. 56, no. 7, pp. 3561-3574, 2010.
[19] M. Tan, I. W. Tsang, and L. Wang, "Matching pursuit LASSO part I: Sparse recovery over big dictionary," IEEE Transactions on Signal Processing, vol. 63, no. 3, pp. 727-741, 2014.
[20] R. Torkamani and R. A. Sadeghzadeh, "Wavelet-based Bayesian Algorithm for Distributed Compressed Sensing," Information Systems & Telecommunication, p. 87, 2019.
[21] W.-C. Chang and Y. T. Su, "Sparse Bayesian Learning Based Tensor Dictionary Learning and Signal Recovery with Application to MIMO Channel Estimation," IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 847-859, 2021.
[22] I. Daubechies, M. Defrise, and C. De Mol, "An iterative thresholding algorithm for linear inverse problems with a sparsity constraint," Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, vol. 57, no. 11, pp. 1413-1457, 2004.
[23] T. Blumensath and M. E. Davies, "Iterative thresholding for sparse approximations," Journal of Fourier analysis and Applications, vol. 14, no. 5-6, pp. 629-654, 2008.
[24] C. J. Miosso, R. von Borries, M. Argaez, L. Velázquez, C. Quintero, and C. Potes, "Compressive sensing reconstruction with prior information by iteratively reweighted least-squares," IEEE Transactions on Signal Processing, vol. 57, no. 6, pp. 2424-2431, 2009.
[25] J. Yang and Y. Zhang, "Alternating direction algorithms for l_1-problems in compressive sensing," SIAM journal on scientific computing, vol. 33, no. 1, pp. 250-278, 2011.
[26] M. Elad, Sparse and redundant representations: from theory to applications in signal and image processing. Springer Science & Business Media, 2010.
[27] S. Boyd, N. Parikh, and E. Chu, Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc, 2011.
[28] A. Beck and M. Teboulle, "A fast iterative shrinkage-thresholding algorithm for linear inverse problems," SIAM journal on imaging sciences, vol. 2, no. 1, pp. 183-202, 2009.
