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On the Performance of IRS-Assisted SSK and RPM over Rician Fading Channels

Harsh Raj, Ugrasen Singh, B. R. Manoj

TL;DR

The paper addresses reliable transmission in IRS-assisted wireless networks by combining space-shift keying at the base station with reflection phase modulation at the IRS under Rician fading. It develops a joint ML detector and a moment-generating function-based framework to derive a tight ABER bound via PEPs and a closed-form ergodic capacity expression, accounting for the sum of squares of Rician variables. The key contributions include a tractable PEP derivation with a practical approximation, a diversity-order analysis showing \\mathrm{N_r} \\)-fold diversity, and a DCMS-based capacity expression that captures the impact of IRS size, RPM constellation size, and MIMO diversity. The results, validated by Monte Carlo simulations, demonstrate significant performance gains from increasing IRS elements and receive antennas and highlight the practical viability of IRS-assisted SSK-RPM for enhanced reliability and spectral efficiency in 6G-like scenarios.

Abstract

This paper presents the index modulation, that is, the space-shift keying (SSK) and reflection phase modulation (RPM) schemes for intelligent reflecting surface (IRS)-assisted wireless network. IRS simultaneously reflects the incoming information signal from the base station and explicitly encodes the local information bits in the reflection phase shift of IRS elements. The phase shift of the IRS elements is employed according to local data from the RPM constellation. A joint detection using a maximum-likelihood (ML) decoder is performed for the SSK and RPM symbols over a realistic fading scenario modeled as the Rician fading channel. The pairwise error probability over Rician fading channels is derived and utilized to determine the average bit error rate. In addition, the ergodic capacity of the presented system is derived. The derived analytical results are verified and are in exact agreement with Monte-Carlo simulations.

On the Performance of IRS-Assisted SSK and RPM over Rician Fading Channels

TL;DR

The paper addresses reliable transmission in IRS-assisted wireless networks by combining space-shift keying at the base station with reflection phase modulation at the IRS under Rician fading. It develops a joint ML detector and a moment-generating function-based framework to derive a tight ABER bound via PEPs and a closed-form ergodic capacity expression, accounting for the sum of squares of Rician variables. The key contributions include a tractable PEP derivation with a practical approximation, a diversity-order analysis showing \\mathrm{N_r} \\)-fold diversity, and a DCMS-based capacity expression that captures the impact of IRS size, RPM constellation size, and MIMO diversity. The results, validated by Monte Carlo simulations, demonstrate significant performance gains from increasing IRS elements and receive antennas and highlight the practical viability of IRS-assisted SSK-RPM for enhanced reliability and spectral efficiency in 6G-like scenarios.

Abstract

This paper presents the index modulation, that is, the space-shift keying (SSK) and reflection phase modulation (RPM) schemes for intelligent reflecting surface (IRS)-assisted wireless network. IRS simultaneously reflects the incoming information signal from the base station and explicitly encodes the local information bits in the reflection phase shift of IRS elements. The phase shift of the IRS elements is employed according to local data from the RPM constellation. A joint detection using a maximum-likelihood (ML) decoder is performed for the SSK and RPM symbols over a realistic fading scenario modeled as the Rician fading channel. The pairwise error probability over Rician fading channels is derived and utilized to determine the average bit error rate. In addition, the ergodic capacity of the presented system is derived. The derived analytical results are verified and are in exact agreement with Monte-Carlo simulations.
Paper Structure (10 sections, 24 equations, 3 figures, 1 table)

This paper contains 10 sections, 24 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Signal Model
  5. Performance Analysis
  6. ABER Analysis
  7. Diversity Analysis
  8. Ergodic Capacity Analysis
  9. Results and discussion
  10. Conclusion

Figures (3)

  • Figure 1: ABER vs SNRs for $N_t=2$, $N_r=1$, $M=2$, and various values of $N$.
  • Figure 2: ABER as a function of SNRs for $N_t=2$, $N=20$, $d_r=4d_0$ and increasing values of $N_r$ and $M$.
  • Figure 3: Ergodic capacity vs SNRs for various values of $M$, $N_t$, $N_r$, and $N$.