Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization (1906.09956v2)

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.

• The paper introduces an IRS grouping method that significantly reduces CSI estimation overhead in OFDM systems.
• It employs a pilot-based three-phase protocol to jointly optimize power allocation and IRS reflection coefficients using water-filling and SCA.
• Numerical results demonstrate enhanced achievable rates and robust performance across diverse channel conditions, highlighting practical beamforming benefits.

Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization

The paper explores the integration of Intelligent Reflecting Surfaces (IRS) with Orthogonal Frequency Division Multiplexing (OFDM), aiming to develop a transmission protocol that maximizes achievable rates in frequency-selective channels. The authors propose a sophisticated approach addressing two key challenges: channel estimation and the joint optimization of power allocation and IRS reflection coefficients.

System Model and IRS Grouping

The research investigates a system where an IRS is deployed between a base station (BS) and a user to enhance communication. Traditional methods require estimation of channel state information (CSI) for each reflecting element, which significantly increases complexity and overhead. This paper introduces an IRS elements grouping method to mitigate this. By grouping adjacent IRS elements and assigning a common reflection coefficient, the approach reduces training overhead while maintaining adequate channel estimation.

Channel Estimation and Transmission Protocol

The authors propose a pilot-based transmission protocol divided into three phases:

1. Pilot Transmission: Utilizes K+1 pilot symbols for channel estimation, where channels are estimated sequentially by turning on only specific IRS element groups.
2. Processing and Feedback: Employs estimated channels to optimize transmit power allocation and IRS coefficients, reducing feedback and processing time.
3. Data Transmission: Utilizes optimized parameters for efficient data transfer with reduced estimation errors.

This method ensures reduced overhead and provides flexibility in beamforming, critical for optimizing overall system performance.

Optimization Problem and Solutions

The paper formulates a non-convex optimization problem aiming to maximize the achievable rate by jointly optimizing power allocation and IRS coefficients. An alternating optimization algorithm is proposed:

• Power Allocation: Solved using a water-filling approach based on estimated CSI.
• IRS Coefficient Optimization: Utilizes successive convex approximation (SCA) to efficiently navigate the non-convex landscape, achieving at least local optimality.

For initialization, a strategy to maximize channel power is implemented using semidefinite relaxation (SDR) or a lower-complexity successive alignment (SA) method. Empirical results indicate the proposed methods effectively enhance system performance.

Numerical Results and Implications

Numerical simulations demonstrate significant improvements in the achievable rate through the proposed methods, especially under high IRS element numbers. The results highlight:

• Markedly increased rates with improved IRS design.
• Optimal IRS grouping ratios vary depending on the channel coherence time, showcasing the importance of adaptive strategies depending on specific scenarios.
• Notable sensitivity to channel estimation accuracy, with robust performance in low-SNR and high-SNR contexts.

These insights emphasize the IRS's potential to improve wireless communication systems by facilitating sophisticated beamforming techniques with reasonable computational overhead.

Future Directions

Possible expansions of this research include incorporating multi-user scenarios, enhancing pilot designs tailored to IRS systems, and optimizing grouping strategies further for varied communication environments. This work lays the groundwork for evolving IRS in comprehensive wireless networks, promising substantial enhancements in communication efficacy and efficiency without excessive resource utilization.