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Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation
Signal Processing · Submitted 12 Nov 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Simulation results verify the effectiveness of the proposed algorithm.
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The abstract, sentence by sentence
Intelligent Reflecting Surface (IRS) has been a promising solution to enhance wireless networks both spectral-efficiently and energy-efficiently. This paper considers an IRS-assisted the Internet of Things network for massive connectivity. We aim to solve the IRS-related activity detection and channel estimation problem which has not been studied before. In this paper, we formulate the IRS-related activity detection and channel estimation problem as sparse matrix factorization, matrix completion and Multiple Measurement Vector problem and, we propose a three-stage framework based on the approximate message passing. Simulation results verify the effectiveness of the proposed algorithm.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Intelligent Reflecting Surface (IRS) has been a promising solution to enhance wireless networks both spectral-efficiently and energy-efficiently. | background 1.00 | 0.04 | 0.02 | asserted 0.72 |
| s1 | This paper considers an IRS-assisted the Internet of Things network for massive connectivity. | background 0.64 | 0.36 | 0.02 | asserted 0.89 |
| s2 | We aim to solve the IRS-related activity detection and channel estimation problem which has not been studied before. | gap 0.90 | 0.30 | 0.04 | asserted 0.93 |
| s3 | In this paper, we formulate the IRS-related activity detection and channel estimation problem as sparse matrix factorization, matrix completion and Multiple Measurement Vector problem and, we propose a three-stage framework based on the approximate message passing. | method 0.79 | 0.95 | 0.12 | asserted 0.99 |
| s4 | Simulation results verify the effectiveness of the proposed algorithm. | result 0.98 | 0.91 | 0.68 | asserted 1.00 |
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The paper
Original abstract
Intelligent Reflecting Surface (IRS) has been a promising solution to enhance wireless networks both spectral-efficiently and energy-efficiently. This paper considers an IRS-assisted the Internet of Things network for massive connectivity. We aim to solve the IRS-related activity detection and channel estimation problem which has not been studied before. In this paper, we formulate the IRS-related activity detection and channel estimation problem as sparse matrix factorization, matrix completion and Multiple Measurement Vector problem and, we propose a three-stage framework based on the approximate message passing. Simulation results verify the effectiveness of the proposed algorithm.
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How the literature uses it
3 arXiv papers cite this in 4 sentences
Papers citing it · 3 sentences without a confident label
Cites it as background 1 paper
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In [ref] , the authors consider the uplink of a network with massive connectivity and investigate the joint problem of channel estimation and activity detection.
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead · Channel Estimation · 0.83
What it cites · 4 sentences without a confident label
Uses the method or tool of 1 paper
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As for the MMV problem of recovering $Θ$ from the estimated matrix $$ and the known signature matrix $$, we adopt the Vector AMP algorithm proposed in [ref] , which operates a vector denoiser on each column vector of the matching filter output. initializing with $Θ^0 = 0$ and $^0=$, the iterations of the vector AMP algorithm are defined as [equation] where $θ^t_k$ is the $k$-th column vector of the estimated matrix $Θ^t$ at iteration t, $^t ∈ℂ^N×L$ is the corresponding residual, $η_t,k(·): ℂ^N×1→ℂ^N×1$ is a vector denoiser that operates on the $k$-th column vector of $(^t)^* + Θ^t$, and $η'_t,k(·)$ is the first-order derivative of $η_t,k(·)$.
Belief propagation for joint sparse recovery · MMV problem via Vector AMP · 1.00
Uses data or values from 1 paper
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In our setting, $d_0 = 1$, $l_0 = -30$dB, and the path loss component $α$ for device-to-IRS link and IRS-to-BS link are set as 2 and 2.8, respectively [ref] .
Over-the-Air Computation via Intelligent Reflecting Surfaces · Simulation · 0.80
Cites as background 3 papers
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Intelligent Reflecting Surface (IRS) has recently emerged as a promising new technology for enhancing wireless communications both spectral-efficiently and energy-efficiently by controlling the propagation environment [ref] .
Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network · Introduction · 0.97
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In particular, the IRS-related channel capacity characterization is studied in [ref] .
Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication · Introduction · 0.95
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Furthermore, IRS has been jointly designed with other existing technologies, e.g., non-orthogonal multiple access (NOMA) [ref] and over-the-air computation [ref] .
Over-the-Air Computation via Intelligent Reflecting Surfaces · Introduction · 0.86
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