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A Review of Compressive Sensing Detection for Spatial Modulation in Massive MIMO System
Multiple Input Multiple Output (MIMO) low complexity receiver which utilizes the compressive sensing detection for Spatial Modulation in large scale MIMO system in order to reduce the system complexity. In conventional MIMO system; huge amount of antennas is used at both ends to exploit the multipath propagation. This system maximizes the throughput performance and data rates are increased but only at the cost of high hardware complexity and increased power-consumption. Spatial Modulation Matching Pursuit (SMMP) is the proposed enhanced CS technique used for the improvement of detection performance. Hence, this paper reviews recent research findings concerning normalized Compressive Sensing (CS) detection algorithm, used for Spatial Modulation (SM) in massive MIMO, to lowers the signal processing complexity, which in result improves the energy efficiency of system against that of conventional MIMO system.This strategy is achieved by involving additional structures and sparsity in which a single transmitter antenna or a subset of it is turned on at each case to transmit a certain data. The subset of antenna which is turned on for transmission depends on approaching data bits. Therefore, the total increase in the spectral efficiency of the system is given as base-two logarithm of whole antennas at the transmitter. It reduces the signal processing load at base station and doesn’t depend upon any synchronization between transmitters. The Spatial Modulation Matching Pursuit used prevents the Inter Channel Interference (ICI) of the system which in result improves the Bit Error Rate (BER) performance than the typical MIMO system.
Compressive Sensing, Energy Efficiency, Large-Scale MIMO, Spatial Modulation.
- Li G, et al. Energy-Efficient Wireless Communications: Tutorial, Survey, and Open Issues, IEEE Wireless Communication. 2011 Dec; 18(6):28–35.
- Bjornson E, Sanguinetti L, Hoydis J, Debbah M. Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer? IEEE Trans. Wireless Communication. 2015 Jun; 14(6):3059–75.
- Di Renzo M, Haas H, Ghrayeb A. Sugiura S, Hanzo L. Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation, Proc. IEEE. 2014 Jan; 102(1):56–103.
- Rusek F, et al. Scaling up MIMO: Opportunities and Challenges with Very Large Arrays, IEEE Signal Process. Mag. 2013 Jan; 30(1):40–60.
- Hoydis J, Ten Brink S, Debbah M. Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas do we Need? IEEE J. Sel. Areas Communication. 2013 Feb; 31(2):160–71.
- Masouros C, Sellathurai M, Ratnarajah T. Large-Scale MIMO Transmitters in Fixed Physical Spaces: The Effect of Transmit Correlation and Mutual Coupling, IEEE Trans. Communication. 2013 Jul; 61(7):2794–804.
- Masouros C, Matthaiou M. Space-Constrained Massive MIMO: Hitting the Wall of Favourable Propagation, IEEE Communication, Letters. 2015 May; 19(5):771–74.
- Ha D, Lee K, Kang J. Energy Efficiency Analysis with Circuit Power Consumption in Massive MIMO Systems, In: Proc. IEEE 24th Int. Symp. PIMRC, September 2013.
- Yang P, Di Renzo M, Xiao Y, Li S, Hanzo L. Design Guidelines for Spatial Modulation, IEEE Communication Surveys Tuts., 1st Quart. 2015; 17(1):6–26.
- Di Renzo M, Haas H, Grant PM. Spatial Modulation for Multiple Antenna Wireless Systems: A Survey, Ieee Communication Mag. 2011 Dec; 49(12);182–91.
- Golub GH, Van Loan CF. Matrix Computations, Baltimore, MD, USA: The Johns Hopkins Univ. Press, 2012, 3.
- Younis A, Serafimovski N, Mesleh R, Haas H. Generalised Spatial Modulation, In: Proc. Conf. Rec. 44th ASILOMAR, 2010 Nov, p. 1498–502.
- Peel C, Hochwald B, Swindlehurst A. A VectorPerturbation Technique for Near-Capacity Multi-Antenna Multiuser Communication—Part I: Channel Inversion and Regularization, IEEE Trans. Communication. 2005 Jan; 53(1):195–202.
- Yu C-M, et al. Compressed Sensing Detector Design for Space Shift Keying in MIMO Systems, IEEE Communication Lett. 2012 Oct; 16(10);1556–59.
- Candès EJ, Wakin MB. An Introduction to Compressive Sampling, IEEE Signal Process. Mag. 2008 Mar; 25(2):21– 30.
- Candès E. Compressive Sampling, In: Proc. Int Congr. Math., Madrid: Spain, 22–30 August 2006, p. 1433–52.
- Candes E, Romberg J; Tao T. Stable Signal Recovery from Incomplete and Inaccurate Measurements, Commun. Pure Appl. Math. 2006 Aug; 59(8):1207–23.
- Needell D, Tropp JA. CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples, Appl. Comput. Harmonic Anal. 2009 May; 26(3):301–21.
- Eldar YC, Mishali M. Robust Recovery of Signals from a Structured Union of Subspaces, IEEE Trans. Inf. Theory. 2009 Nov; 55(11):5302–16.
- Baraniuk RG, Cevher V, Duarte MF, Hegde C. Model Based Compressive Sensing, IEEE Trans. Inf. Theory. 2010 Apr; 56(4):1982–2001.
- Zheng J. Low-Complexity Detector for Spatial Modulation Multiple Access Channels with a Large Number of Receive Antennas, IEEE Communication Lett. 2014 Nov; 18(11):2055–58.
- Rao X, Lau VK. Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems, IEEE Trans. Signal Process. 2014 Jun; 62(12):3261–71.
- Vandenberghe L. Applied Numerical Computing, Univ.Calif., Los Angeles, CA, USA: Univ. Lecture, 2011.
- Björck A. Numerical Methods for Least Squares Problems, Philadelphia, PA, USA: SIAM, 1996.
- Wang S, Li Y, Zhao M, Wang J. Energy Efficient and Low-Complexity Uplink Transceiver for Massive Spatial Modulation MIMO, IEEE Trans. Veh. Technol. 2015; 64(10):4617−32.
- Kmutha D, Amutha Prabha N. Performance Analysis of PAPR Reduction in LTE System, Indian Journal of Science and Technology, 2016 Aug; 9.
- Cui S, Goldsmith AJ, Bahai A. Energy-Constrained Modulation Optimization, IEEE Trans. Wireless Communication. 2005 Sep; 4(5):2349–60.
- Miao G. Energy-Efficient Uplink Multi-User MIMO, IEEE Trans. Wireless Communication. 2013 May; 12(5):2302–13.
- Ntontin K, Di Renzo M, Perez-Neira A, Verikoukis C. Towards the Performance and Energy Efficiency Comparison of Spatial Modulation with Conventional Single-Antenna Transmission Over Generalized Fading Channels, In: Proc. IEEE Int. Workshop CAMAD, 2012 Sep, p. 120–24.
- Masouros C, Sellathurai M, Ratnarajah T. Maximizing Energy Efficiency in the Vector Precoded MU-MISO Downlink by Selective Perturbation, IEEE Trans. Wireless Communication. 2014 Sep; 13(9):4974–84.
- Masouros C, Sellathurai M, Ratnarajah T. Vector Perturbation Based on Symbol Scaling for Limited Feedback MISO Downlinks, Ieeetrans. Signal Process, 2014 Feb; 62(3):562–71.
- Garcia-Rodriguez A, Masouros C. Power-Efficient Tomlinson-Harashima Precoding for the Downlink of Multi-User MISO Systems, IEEE Trans. Commun. 2014 Jun; 62(6):1884–96.
- Garcia-Rodriguez A, Masouros C. Power Loss Reduction for MMSETHP with Multi-Dimensional Symbol Scaling, IEEE Communication, Lett. 2014 Jul; 18(7):1147–50.
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