Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication (1910.01573v1)

Abstract: Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices can be optimized for different subcarriers while only one common set of IRS reflection coefficients can be designed to cater to all subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.

• The paper introduces an alternating optimization algorithm that jointly optimizes IRS reflection coefficients and MIMO transmit covariance, significantly boosting capacity.
• The paper extends the analysis to broadband MIMO-OFDM systems using convex relaxation to optimize common IRS coefficients across all subcarriers.
• The paper provides suboptimal solutions for low and high SNR regimes and adapts the approach for MISO and SIMO cases, enhancing practical deployment.

Capacity Characterization for Intelligent Reflecting Surface (IRS) Aided MIMO Communication

This paper by Shuowen Zhang and Rui Zhang presents a detailed analysis of the capacity limits for multiple-input multiple-output (MIMO) systems enhanced by intelligent reflecting surfaces (IRS). The work explores IRS as a potential low-cost, energy-efficient solution to augment wireless communication capabilities by actively managing the propagation of signals through many passive reflecting elements.

Key Contributions

1. Narrowband IRS-Aided MIMO Systems: The authors first consider narrowband transmission within frequency-flat fading channels. They propose an alternating optimization (AO) algorithm to jointly optimize the IRS reflection coefficients and the MIMO transmit covariance matrix. This method iteratively adjusts these parameters to find a locally optimal solution, achieving significant capacity increases over traditional MIMO configurations without IRS.
2. Broadband MIMO-OFDM Analysis: For broadband transmissions over frequency-selective fading channels, IRS reflection coefficients must be common across all subcarriers. The paper introduces an AO algorithm leveraging convex relaxation techniques to tackle this complexity, enabling effective capacity maximization in MIMO-OFDM systems.
3. Suboptimal Solutions in Asymptotic Regimes: The research expands on simplified methodologies for specific conditions such as low and high signal-to-noise ratio (SNR) scenarios, providing alternatives with reduced computational expenses.
4. Extensions to MISO and SIMO Cases: The paper also addresses the IRS-aided single-input multiple-output (SIMO) and multiple-input single-output (MISO) scenarios, leading to streamlined versions of the optimization algorithms.

Numerical Analysis and Implications

Numerical results underscore the proposed methodologies' efficacy, illustrating substantial capacity gains compared to benchmarks. The presence of IRS not only enhances channel power but also optimizes parameters such as channel rank and condition number. This suggests that IRS can substantially alter signal propagation environments, potentially revolutionizing future MIMO communications.

Future Directions

The insightful treatment of IRS and MIMO systems in both narrowband and broadband contexts highlights the promise of IRS in circumventing traditional MIMO limitations related to cost and power. Future research could delve into more complex scenarios involving user mobility, dynamic channel conditions, and advanced signal processing techniques to further harness IRS technology in varying practical deployments.

Conclusion

This work provides a foundational perspective on the integration of IRS within MIMO frameworks, establishing pathways for further exploration and optimization of IRS-related parameters in diverse communication environments. As such, the research sets a precedent for utilizing IRS in achieving scalable and efficient network enhancements.