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RSRP Measurement Based Channel Autocorrelation Estimation for IRS-Aided Wideband Communication

He Sun, Lipeng Zhu, Weidong Mei, Rui Zhang

TL;DR

A novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems and significantly outperforms existing power-measurement-based IRS reflection designs in wideband channels.

Abstract

The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing powermeasurement-based IRS reflection designs in wideband channels.

RSRP Measurement Based Channel Autocorrelation Estimation for IRS-Aided Wideband Communication

TL;DR

A novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems and significantly outperforms existing power-measurement-based IRS reflection designs in wideband channels.

Abstract

The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing powermeasurement-based IRS reflection designs in wideband channels.

Paper Structure

This paper contains 8 sections, 2 theorems, 24 equations, 5 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. NN-enabled Channel Estimation Based on RSRP Measurements
  4. RSRP Measurement
  5. NN-Empowered Channel Autocorrelation Matrix Estimation
  6. IRS Reflection Design
  7. Simulation Results
  8. Conclusion

Key Result

Lemma 1

When $M_0 \geq K$, the RSRP measurement in (Eqs0030101) based on a subset of subcarriers in ${\cal M}_0$ is equal to the average received signal power over all the $M$ subcarriers in (Eqs002010), i.e.,

Figures (5)

  • Figure 1: An IRS-assisted wideband OFDM system.
  • Figure 2: An example of inserted RSs over OFDM symbols with $Q=3$, $M_0=2$, and $M=6$.
  • Figure 3: NN structure for wideband channel autocorrelation estimation.
  • Figure 4: IRS channel autocorrelation estimation NMSE versus the number of random IRS reflection sets, $L$.
  • Figure 5: Average SNR performance versus the number of random IRS reflection sets, $L$.

Theorems & Definitions (3)

  • Lemma 1
  • Proof 1
  • Lemma 2