[Paper Review] Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization

Yifei Yang, Beixiong Zheng+2 more|arXiv (Cornell University)|Jun 21, 2019

Advanced Wireless Communication Technologies22 references30 citations

TL;DR

This paper proposes a practical transmission protocol for IRS-enhanced OFDM systems that groups adjacent intelligent reflecting surface (IRS) elements to reduce channel training overhead while enabling joint optimization of transmit power allocation and IRS reflection coefficients. The method achieves significant rate gains by balancing training overhead and beamforming flexibility, with simulation results showing superior performance over conventional schemes, especially under realistic channel conditions.

ABSTRACT

Intelligent reflecting surface (IRS) is a promising new technology for achieving both spectrum and energy efficient wireless communication systems in the future. However, existing works on IRS mainly consider frequency-flat channels and assume perfect knowledge of channel state information (CSI) at the transmitter. Motivated by this, in this paper we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system under frequency-selective channels and propose a practical transmission protocol with channel estimation. First, to reduce the overhead in channel training and estimation and to exploit the channel spatial correlation, we propose a novel IRS elements grouping method, where each group consists of a set of adjacent IRS elements that share a common reflection coefficient. Based on this grouping method, we propose a practical transmission protocol where only the combined channel of each group needs to be estimated, thus substantially reducing the training overhead. Next, with any given grouping and estimated CSI, we formulate the problem to maximize the achievable rate by jointly optimizing the transmit power allocation and the IRS passive array reflection coefficients. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the power allocation and the passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the case without using IRS. Moreover, it is shown that there exists an optimal size for IRS elements grouping which achieves the maximum achievable rate due to the trade-off between the training overhead and IRS passive beamforming flexibility.

Motivation & Objective

• To address the high training overhead and imperfect CSI in IRS-aided OFDM systems with frequency-selective fading.
• To reduce channel estimation overhead by grouping adjacent IRS elements with shared reflection coefficients.
• To jointly optimize transmit power allocation and IRS passive beamforming for rate maximization under estimated CSI.
• To analyze the trade-off between training overhead and beamforming gain via optimal IRS grouping ratio.
• To evaluate performance across varying SNR regimes and channel coherence times.

Proposed method

• Proposes an IRS element grouping strategy where adjacent elements share a common reflection coefficient, reducing the number of channels to estimate.
• Designs a practical transmission protocol where only the combined channel per group is estimated, significantly lowering training overhead.
• Formulates a non-convex optimization problem to maximize achievable rate via joint power allocation and passive beamforming coefficient design.
• Develops an iterative algorithm alternating between power allocation and reflection coefficient optimization, with a customized initialization method for high-quality suboptimal solutions.
• Employs a channel estimation model that accounts for spatial correlation and pilot overhead in frequency-selective fading environments.
• Introduces a coherence time-aware grouping strategy, showing that optimal grouping ratio increases with longer channel coherence time.

Experimental results

Research questions

• RQ1 How can IRS element grouping reduce training overhead in IRS-OFDM systems without sacrificing beamforming gain?
• RQ2 What is the optimal IRS grouping ratio that balances training overhead and passive beamforming flexibility?
• RQ3 How does the achievable rate performance vary with SNR and channel coherence time under the proposed protocol?
• RQ4 Can joint optimization of power allocation and IRS reflection coefficients outperform benchmark schemes with random or fixed phase shifts?
• RQ5 How does channel estimation error impact system performance, especially in low-SNR regimes?

Key findings

• The proposed protocol achieves significantly higher achievable rates than systems without IRS grouping, especially in low-SNR and high-coherence-time scenarios.
• An optimal IRS grouping ratio exists due to the trade-off between training overhead and beamforming diversity, with performance peaking at moderate grouping ratios.
• At low SNR (γd = 0 dB), performance degrades at both low and high grouping ratios due to increased channel estimation error, with a clear peak in achievable rate at intermediate grouping.
• At high SNR (γd = 20 dB), the optimal grouping ratio increases with channel coherence time, as longer coherence times allow better compensation for training overhead.
• The proposed iterative algorithm with customized initialization achieves near-optimal rate performance with much lower complexity than full optimization.
• Even with high training overhead, the proposed method outperforms the random phase benchmark across all SNR regimes and coherence times.