Total views : 278

Optimum Detector for Spatial Modulation using Sparsity Recovery in Compressive Sensing


  • Department Electronics and Communication Engineering, SRM University, Chennai - 603203, Tamil Nadu, India
  • School of Electronics Engineering, VIT University, Vellore - 632014, Tamil Nadu, India


Objective: Spatial Modulation (SM) is proposed for next generation green communications due its high spectral and energy efficiency. SM is incorporated with massive MIMO structures to leverage its high potential in the application for future generation wireless networks. Methods/statistical Analysis: The optimal ML detector for SM-MIMO systems requires enormous computational complexity, which makes the implementation infeasible in practice. However, the greedy low complexity detectors suffer from inferior performance and have huge performance gap from the optimal detectors. In this paper we propose new transmission schemes and detector structures for SM-MIMO systems. Findings: In particular the transmitter imposes certain structures, known as joint sparse, and the receiver exploits the information in detecting the symbols. We have shown that our proposed detector performs better than other greedy algorithms in the literature and performs close to the ML solution. We establish theoretical recovery guarantees for our proposed approach and compare the performance in theoretical and simulation results. Improvements: The theoretical characterization shows significant improvement in the detection performance compared to the conventional schemes. It is shown in simulation that the proposed algorithm achieves a gain of 4 dB compared to the conventional detectors.


SM-MIMO System, Wireless Networks.

Full Text:

 |  (PDF views: 302)


  • Di Renzo M, Haas H, Ghrayeb A, Sugiura S, Hanzo L. Spatial modulation for generalized MIMO: Challenges, opportunities, and implementation. Proceedings of the IEEE. 2014 Jan; 102(1):56–103.
  • Yang P, Di Renzo M, Xiao Y, Li S, Hanzo L. Design guidelines for spatial modulation. IEEE Communications Surveys and Tutorials. 2015 Mar; 17(1):6–26.
  • Zheng J. Signal vector based list detection for spatial modulation. IEEE Wireless Communications Letters. 2012 Aug; 1(4):265–67.
  • Wang J, Jia S, Song J. Generalised spatial modulation system with multiple active transmit antennas and low complexity detection scheme. IEEE Transactions on Wireless Communications. 2012 Apr; 11(4):1605–15.
  • Legnain RM, Hafez RH, Legnain AM. Improved spatial modulation for high spectral efficiency. International Journal of Distributed and Parallel Systems. 2012 Mar; 3(2):13–9.
  • A novel spatial modulation using MIMO spatial multiplexing. Available from:
  • Cal-Braz JA, Sampaio-Neto R. Low-complexity sphere decoding detector for generalized spatial modulation systems. IEEE Communications letters. 2014 Jun; 18(6):949–52.
  • Duarte MF, Eldar YC. Structured compressed sensing: From theory to applications. IEEE Transactions on Signal Processing. 2011 Sep; 59(9):4053–85.
  • Shim B, Kwon S, Song B. Sparse detection with integer constraint using multipath matching pursuit. IEEE Communications Letters. 2014 Oct; 18(10):1851–4.
  • Garcia-Rodriguez A, Masouros C. Low-complexity compressive sensing detection for spatial modulation in large-scale multiple access channels. IEEE Transactions on Communications. 2015 Jul; 63(7):2565–79.
  • Yu CM, Hsieh SH, Liang HW, Lu CS, Chung WH, Kuo SY, Pei SC. Compressed sensing detector design for space shift keying in MIMO systems. IEEE Communications Letters. 2012 Oct; 16(10):1556–9.
  • Liu W, Wang N, Jin M, Xu H. Denoising detection for the generalized spatial modulation system using sparse property. IEEE Communications Letters. 2014 Jan; 18(1):22–5.
  • Wu X, Claussen H, Di Renzo M, Haas H. Channel estimation for spatial modulation. IEEE Transactions on Communications. 2014 Dec; 62(12):4362–72.
  • Kay SM. Fundamentals of statistical signal processing: Practical algorithm development. Pearson Education; 2013.
  • Gao Z, Dai L, Qi C, Yuen C, Wang Z. Near-optimal signal detector based on structured compressive sensing for massive SM-MIMO. arXiv:1601.07701v3 [cs.IT]; 2016 Apr. p. 1–8.
  • Nandakumar S, Khara S. Modeling and performance analysis of an improved Data Channel Assignment (DCA) scheme for 3G/WLAN Mixed Cells. International Journal of Wireless Information Networks. 2015 Mar; 22(1):10–28.
  • Velmurugan T, Khara S, Basavaraj B. Modified handoff algorithm for providing optimization in heterogeneous wireless networks. Evolving Systems. 2015 Sep; 6(3):199–208.
  • Velmurugan T, Khara S, Nandakumar S, Saravanan B. Seamless vertical handoff using Invasive Weed Optimization (IWO) algorithm for heterogeneous wireless networks. Aim Shams Engineering Journal. 2016 Mar; 7(1):101–11.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.