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Configuring Intelligent Reflecting Surface with Performance Guarantees: Blind Beamforming
Information Theory · Submitted 4 Dec 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Field tests demonstrate the significant advantages of the proposed blind beamforming algorithm over the benchmark algorithms in enhancing wireless transmission.
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The abstract, sentence by sentence
This work gives a blind beamforming strategy for intelligent reflecting surface (IRS), aiming to boost the received signal-to-noise ratio (SNR) by coordinating phase shifts across reflective elements in the absence of channel information. While the existing methods of IRS beamforming typically first estimate channels and then optimize phase shifts, we propose a conditional sample mean based statistical approach that explores the wireless environment via random sampling without performing any channel estimation. Remarkably, the new method just requires a polynomial number of random samples to yield an SNR boost that is quadratic in the number of reflective elements, whereas the standard random-max sampling algorithm can only achieve a linear boost under the same condition. Moreover, we gain additional insight into blind beamforming by interpreting it as a least squares problem. Field tests demonstrate the significant advantages of the proposed blind beamforming algorithm over the benchmark algorithms in enhancing wireless transmission.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | This work gives a blind beamforming strategy for intelligent reflecting surface (IRS), aiming to boost the received signal-to-noise ratio (SNR) by coordinating phase shifts across reflective elements in the absence of channel information. | contribution 1.00 | 0.91 | 0.19 | asserted 0.98 |
| s1 | While the existing methods of IRS beamforming typically first estimate channels and then optimize phase shifts, we propose a conditional sample mean based statistical approach that explores the wireless environment via random sampling without performing any channel estimation. | contribution 0.70 | 0.71 | 0.09 | asserted 1.00 |
| s2 | Remarkably, the new method just requires a polynomial number of random samples to yield an SNR boost that is quadratic in the number of reflective elements, whereas the standard random-max sampling algorithm can only achieve a linear boost under the same condition. | result 0.98 | 0.90 | 0.62 | asserted 1.00 |
| s3 | Moreover, we gain additional insight into blind beamforming by interpreting it as a least squares problem. | interpretation 0.96 | 0.91 | 0.05 | asserted 0.99 |
| s4 | Field tests demonstrate the significant advantages of the proposed blind beamforming algorithm over the benchmark algorithms in enhancing wireless transmission. | result 0.99 | 0.93 | 0.31 | asserted 1.00 |
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The paper
Original abstract
This work gives a blind beamforming strategy for intelligent reflecting surface (IRS), aiming to boost the received signal-to-noise ratio (SNR) by coordinating phase shifts across reflective elements in the absence of channel information. While the existing methods of IRS beamforming typically first estimate channels and then optimize phase shifts, we propose a conditional sample mean based statistical approach that explores the wireless environment via random sampling without performing any channel estimation. Remarkably, the new method just requires a polynomial number of random samples to yield an SNR boost that is quadratic in the number of reflective elements, whereas the standard random-max sampling algorithm can only achieve a linear boost under the same condition. Moreover, we gain additional insight into blind beamforming by interpreting it as a least squares problem. Field tests demonstrate the significant advantages of the proposed blind beamforming algorithm over the benchmark algorithms in enhancing wireless transmission.
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How the literature uses it
1 arXiv paper cite this in 4 sentences
Papers citing it · 2 sentences without a confident label
Contests it 1 paper
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It is worth noting that the CSM-based methods in [ref] did not fully exploit the power measurements to obtain partial CSI of IRS-cascaded channels, thus resulting in their high overhead for power measurements and low IRS beamforming gain.
Channel Autocorrelation Estimation for IRS-Aided Wireless Communications Based on Power Measurements · INTRODUCTION · contest 0.73
Compares with it 1 paper
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For comparison, the benchmark schemes for IRS reflection design based on power measurements are listed as follows: $1)$Upper bound: The upper bound on the effective channel gain is obtained by optimizing the IRS reflection vector based on the perfect CSI $h$ with the algorithm proposed in [ref] ; $2)$RMS (random-max sampling): A large number of random IRS reflection vectors are applied with $u_n$ uniformly distributed in $Φ_b$ for $∀n$ and the one achieving the largest received signal power is used; $3)$CSM (conditional sample mean): This is the method proposed in [ref] , where a large number of random IRS reflection vectors are applied, and the empirical expectation of the received signal power is calculated conditioned on $u_n$ fixed at every possible value for $∀n$.
Channel Autocorrelation Estimation for IRS-Aided Wireless Communications Based on Power Measurements · IRS Reflection Design with Estimated Channel · 0.91
What it cites
Compares with 1 paper
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Notice that this work does not assume coordination between base-station and IRS, while some existing works [ref] consider optimizing base-station and IRS jointly.
Active RIS vs. Passive RIS: Which Will Prevail in 6G? · System Model · 0.90
Cites as background 2 papers
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As summarized in [ref] , semidefinite relaxation (SDR) [ref] and fractional programming (FP) [ref] constitute two popular optimization tools in this area, e.g., [ref] find SDR useful because of the quadratic form of the IRS beamforming problem, while [ref] rely on the FP technique to deal with the signal-to-noise-plus-interference ratio (SINR) terms.
Active RIS vs. Passive RIS: Which Will Prevail in 6G? · Introduction · 0.93
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Moreover, [ref] view the channel estimation for IRS as a compressed sensing task.
Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems · Introduction · 0.85
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