Blind Beamforming for Coverage Enhancement with Intelligent Reflecting Surface

TL;DR

This work tackles IRS-based coverage enhancement without channel state information by introducing MV-CSM, a blind beamforming method that aggregates per-position conditional sample means via majority voting. The authors prove achievability results showing per-position SNR scales as $\mathrm{SNR}_u=\frac{4Pc_u^2}{\sigma^2\pi^2}\,\Omega\big(\frac{N^2}{U}\big)$ under mild assumptions, and establish a matching converse upper bound for good algorithms, implying near-optimality within a wide regime. Through theoretical analysis and extensive field tests at 2.6 GHz, MV-CSM demonstrates substantial minimum-SNR gains (e.g., up to 18.22 dB in practice) compared with CSI-based and other model-free benchmarks, while avoiding CSI acquisition overhead and protocol changes. The method relies on long-term received-power statistics, is implementable with current receiver capabilities, and exhibits quadratic scaling with the number of IRS elements under realistic conditions, offering a practical path to IRS-enabled coverage enhancement.

Abstract

Conventional policy for configuring an intelligent reflecting surface (IRS) typically requires channel state information (CSI), thus incurring substantial overhead costs and facing incompatibility with the current network protocols. This paper proposes a blind beamforming strategy in the absence of CSI, aiming to boost the minimum signal-to-noise ratio (SNR) among all the receiver positions, namely the coverage enhancement. Although some existing works already consider the IRS-assisted coverage enhancement without CSI, they assume certain position-channel models through which the channels can be recovered from the geographic locations. In contrast, our approach solely relies on the received signal power data, not assuming any position-channel model. We examine the achievability and converse of the proposed blind beamforming method. If the IRS has $N$ reflective elements and there are $U$ receiver positions, then our method guarantees the minimum SNR of $Ω(N^2/U)$ -- which is fairly close to the upper bound $O(N+N^2\sqrt{\ln (NU)}/\sqrt[4]{U})$. Aside from the simulation results, we justify the practical use of blind beamforming in a field test at 2.6 GHz. According to the real-world experiment, the proposed blind beamforming method boosts the minimum SNR across seven random positions in a conference room by 18.22 dB, while the position-based method yields a boost of 12.08 dB.

Figures (6)

• Figure 1: IRS-assisted coverage enhancement.
• Figure 2: Our field test at 2.6 GHz in a large-size conference room. Figure (a) is a photo of the test site. Figure (b) is the layout drawing where the position coordinates are in meters.
• Figure 3: Network topology in our simulation. The position coordinates are all in meters. The IRS is placed on the $y$-$z$ plane.
• Figure 4: Minimum SNR versus $N$ when $U=10$. We remark that the minimum SNR is in a linear scale here so as to show the quadratic growth of MV-CSM.
• Figure 5: Minimum SNR versus $U$ when $N=200$.

Theorems & Definitions (18)

• Proposition 1: Theorem 2 and Remark 1 in blind_beamforming_twc
• Example 1
• Remark 1: CSM vs. Beam Training
• Remark 2: Training Cost of CSM
• Theorem 1
• Corollary 1
• Remark 3: Why Binary Random Sampling?
• Remark 4: Why Fixed Area for Each RE?
• Remark 5: Extension for Active IRS
• Definition 1