[Paper Review] mmWave Doubly-Massive-MIMO Communications Enhanced with an Intelligent Reflecting Surface

Dian‐Wu Yue, Ha H. Nguyen+1 more|arXiv (Cornell University)|Feb 29, 2020

Advanced Wireless Communication Technologies30 references4 citations

TL;DR

This paper proposes an intelligent reflecting surface (IRS)-assisted mmWave doubly-massive MIMO system to enhance spectral efficiency and reliability. By leveraging multiple IRS subsurfaces with large-scale passive reflecting elements, the system achieves a favorable number of controllable propagation paths equal to the number of subsurfaces, with SNR per path increasing quadratically with reflecting elements. The key contribution is the independent optimization of power allocation, precoding/combining, and IRS phase shifts, enabling high spectral efficiency with minimal signaling overhead.

ABSTRACT

As a means to control wireless propagation environments, the use of emerging and novel intelligent reflecting surfaces (IRS) is envisioned to enhance and broaden many applications in future wireless networks. This paper is concerned with a point-to-point IRS-assisted millimeter-wave (mmWave) system in which the IRS consists of multiple subsurfaces, each having the same number of passive reflecting elements, whereas both the transmitter and receiver are equipped with massive antenna arrays. Under the scenario of having very large numbers of antennas at both transmit and receive ends, the achievable rate of the system is derived. Furthermore, with the objective of maximizing the achievable rate, the paper presents optimal solutions of power allocation, precoding/combining, and IRS's phase shifts. Then it is shown that when the number of reflecting elements at each subsurface is very large, the number of favorable and controllable propagation paths provided by the IRS is simply equal to the number of subsurfaces while the received signal-to-noise ratio corresponding to each of the favorable paths increases quadratically with the number of reflecting elements. In addition, the problem of minimizing the transmit power subject to the rate constraint is analyzed for the scenario without direct paths in the pure LOS propagation. Finally, numerical results are provided to corroborate the obtained analysis.

Motivation & Objective

• To address the high path loss and blockage vulnerability in mmWave communications by introducing an IRS to enhance signal propagation.
• To analyze the asymptotic achievable rate in a point-to-point mmWave doubly-massive MIMO system enhanced by an IRS with multiple subsurfaces.
• To derive optimal solutions for power allocation, precoding/combining, and IRS phase shifts under the goal of maximizing spectral efficiency.
• To investigate the impact of large-scale IRS deployment on spatial multiplexing and SNR scaling in pure LoS environments.
• To minimize transmit power under rate constraints when direct LoS links are negligible, optimizing IRS configuration.

Proposed method

• The system employs a clustered statistical channel model for mmWave propagation with multiple IRS subsurfaces, each with identical numbers of passive reflecting elements.
• Asymptotic analysis is conducted under the assumption of very large numbers of transmit and receive antennas, enabling closed-form expressions for achievable rate.
• Optimal power allocation, precoding/combining, and IRS phase shifts are derived independently via convex optimization, avoiding joint design complexity.
• The analysis shows that when the number of reflecting elements per subsurface is large, the number of favorable propagation paths equals the number of subsurfaces.
• For the pure LoS scenario without direct paths, the minimum transmit power is derived under equal power allocation and optimal subsurface count selection.
• Numerical simulations validate the analytical results, including SNR scaling with reflecting elements and rate performance gains with increasing IRS size and subarray count.

Experimental results

Research questions

• RQ1 How does the achievable rate scale in a doubly-massive MIMO system enhanced by an IRS with multiple subsurfaces?
• RQ2 Can the optimal solutions for power allocation, precoding/combining, and IRS phase shifts be derived independently without joint optimization?
• RQ3 What is the relationship between the number of favorable propagation paths and the number of IRS subsurfaces when reflecting elements are large?
• RQ4 How does the received SNR scale with the number of reflecting elements per subsurface?
• RQ5 What is the minimum transmit power required to meet a target rate when direct LoS links are absent and the IRS dominates the channel?

Key findings

• The number of favorable propagation paths provided by the IRS is equal to the number of subsurfaces when each subsurface has a very large number of reflecting elements.
• The received SNR for each favorable path increases quadratically with the number of reflecting elements per subsurface.
• Optimal power allocation, precoding/combining, and IRS phase shifts can be computed independently, simplifying system design and reducing signaling overhead.
• When the total number of reflecting elements is fixed, the optimal number of subsurfaces is approximately 5 when the total number of elements is 1000, balancing path diversity and SNR gain.
• With 1000 reflecting elements and 100 subsurfaces, the achievable sum rate with IRS is about twice that of a system without IRS when $N_t = 64$ and $N_r = 36$.
• The required transmit power decreases linearly with $1/N^2$ as the number of reflecting elements per subsurface increases, with a 20 dB reduction between $N=10$ and $N=100$ for a fixed rate.