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A comprehensive framework for Double Spatial Modulation under imperfect channel state information

  • Goutham Simha Goutham*
  • , K. Shriharsha
  • , Raghavendra M.A.N.S.
  • , U. Shripathi Acharya
  • *Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    Abstract

    The essential requirement for a 5G wireless communication system is the realization of energy efficient as well as spectrally efficient modulation schemes. Double Spatial Modulation (DSM) is a recently proposed high rate Index Modulation (IM) scheme, designed for use in Multiple Input Multiple Output (MIMO) wireless systems. The aim of this scheme is to increase the spectral efficiency of conventional Spatial Modulation (SM) systems while keeping the energy efficiency intact. In this paper, the impact of imperfect channel knowledge on the performance of DSM system under Rayleigh, Rician and Nakagami-m fading channels has been quantified. Later, a modified low complexity decoder for the DSM scheme has been designed using ordered block minimum mean square error (OB-MMSE) criterion. Its performance under varied fading environments have been quantified via Monte Carlo simulations. Finally, a closed form expression for the pairwise error probability (PEP) for a DSM scheme under conditions of perfect and imperfect channel state information has been derived. This is employed to calculate the upper bound on the average bit error probability (ABEP) over aforementioned fading channels. It is observed that, under perfect and imperfect channel conditions DSM outperforms all the other variants of SM by at least 2dB at an average bit error ratio (ABER) of 10−5. Tightness of the derived upper bound is illustrated by Monte Carlo simulation results.

    Original languageEnglish
    Pages (from-to)519-526
    Number of pages8
    JournalPhysical Communication
    Volume25
    DOIs
    Publication statusPublished - 12-2017

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

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