Quantum computing (QC)-assisted algorithms promise exponential increase in the computational efficiency, enabling instant solution of large systems of equations. Massive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) systems pose extraordinary demands in computational complexity, due to the usage of huge antenna arrays, massive number of users, and ultra low-latency requirements. This work provides an initial discussion on how QC-based algorithms for solving linear systems of equations could be utilized for assisting the basic operations of the transceivers physical-layer, such as the channel estimation. We identify the connections between the amplitude encoding in the quantum domain and the recovery of the channel information.Quantum computing (QC)-assisted algorithms promise exponential increase in the computational efficiency, enabling instant solution of large systems of equations. Massive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) systems pose extraordinary demands in computational complexity, due to the usage of huge antenna arrays, massive number of users, and ultra low-latency requirements. This work provides an initial discussion on how QC-based algorithms for solving linear systems of equations could be utilized for assisting the basic operations of the transceivers physical-layer, such as the channel estimation. We identify the connections between the amplitude encoding in the quantum domain and the recovery of the channel information.

IFIP/IEEE International Conference on Very Large Scale IntegrationOct 3, 20227 citations

A QUANTUM MIMO ARCHITECTURE FOR ANTENNA WIRELESS DIGITAL COMMUNICATIONS

Said M. Mikki

Progress In Electromagnetics Research CJan 1, 201919 citations

A Lightweight Preprocessing Scheme for Secret Key Generation from MmWave Massive MIMO Channel Measurements

Lijun Yang, Xinchao Ge, Qianyi Zhu +1 more

Due to the new channel characteristics, previous preprocessing methods for physical layer key generation cannot be directly applied in mmWave massive MIMO systems. Actually, due to the intolerable high computation complexity and time consumption caused by large dimension of massive MIMO channel, even the previous optimal preprocessing method principal component analysis (PCA) is not suitable. To address the problem, in this paper, we propose a lightweight preprocessing approach called beamspace refinement, which is suitable for mmWave massive MIMO channel by exploiting its channel characteristics namely sparsity. The proposed scheme brings the following advantages. 1) It is lightweight. The computational complexity is quiet low. Unlike the PCA scheme, the proposed scheme does not involve the exponential calculation of the number of antennas, which makes it very suitable for mmWave massive MIMO channel. 2) It is robust against noise. Thus, it can be used to generate secret key with quiet high-entropy and high bit agreement ratio even in low SNR regimes. Numerical results show that the proposed approach outperforms the previous method DCT, WT and PCA in terms of bit agreement ratio, and is on par with PCA in terms of key capacity. Besides, the increase of the antennas number can improve key agreement ratio in our scheme, which is beneficial for massive MIMO system.Due to the new channel characteristics, previous preprocessing methods for physical layer key generation cannot be directly applied in mmWave massive MIMO systems. Actually, due to the intolerable high computation complexity and time consumption caused by large dimension of massive MIMO channel, even the previous optimal preprocessing method principal component analysis (PCA) is not suitable. To address the problem, in this paper, we propose a lightweight preprocessing approach called beamspace refinement, which is suitable for mmWave massive MIMO channel by exploiting its channel characteristics namely sparsity. The proposed scheme brings the following advantages. 1) It is lightweight. The computational complexity is quiet low. Unlike the PCA scheme, the proposed scheme does not involve the exponential calculation of the number of antennas, which makes it very suitable for mmWave massive MIMO channel. 2) It is robust against noise. Thus, it can be used to generate secret key with quiet high-entropy and high bit agreement ratio even in low SNR regimes. Numerical results show that the proposed approach outperforms the previous method DCT, WT and PCA in terms of bit agreement ratio, and is on par with PCA in terms of key capacity. Besides, the increase of the antennas number can improve key agreement ratio in our scheme, which is beneficial for massive MIMO system.

IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)May 20, 20230 citations

Quantum-Enhanced Channel Estimation for Terahertz Cell-Free Massive MIMO: Identifying the Favorable SNR Regime

Aqiel Niama Almamori, Mohammed Adil Abbas, Hasan Kahtan +1 more

Abstract The paper explores the field of quantum-enhanced channel estimation of terahertz (THz) cell-free massive multiple-input multiple-output (MIMO) systems. Although quantum sensing has been suggested to be used in wireless communications, its practical benefit over classical techniques is not well known. We identify systematically the signal-to-noise ratio (SNR) regime in which quantum sensing is able to offer an advantage. The results demonstrate that THz systems operating in the low pilot SNR regime ( $\sim$ ∼ 3 dB) achieve 14–103.1% sum-rate improvement with quantum-enhanced estimation, compablack to only 2–5% at millimeter-wave (mmWave) frequencies where pilot SNR exceeds 13 dB. The 6–33 $\times$ × greater quantum advantage at THz frequencies stems from the fundamental difference in operating regimes: THz systems are noise-limited due to severe path loss ( $\sim$ ∼ 100 dB at 10 m), while mmWave systems are interference-limited with near-perfect classical estimation. Our results identify THz cell-free multiple-input multiple-output (MIMO) as a compelling application for quantum-enhanced wireless communications and provide design guidelines for practical deployment in future sixth-generation (6G) networks.Abstract The paper explores the field of quantum-enhanced channel estimation of terahertz (THz) cell-free massive multiple-input multiple-output (MIMO) systems. Although quantum sensing has been suggested to be used in wireless communications, its practical benefit over classical techniques is not well known. We identify systematically the signal-to-noise ratio (SNR) regime in which quantum sensing is able to offer an advantage. The results demonstrate that THz systems operating in the low pilot SNR regime ( $$\sim $$ ∼ 3 dB) achieve 14–103.1% sum-rate improvement with quantum-enhanced estimation, compablack to only 2–5% at millimeter-wave (mmWave) frequencies where pilot SNR exceeds 13 dB. The 6–33 $$\times $$ × greater quantum advantage at THz frequencies stems from the fundamental difference in operating regimes: THz systems are noise-limited due to severe path loss ( $$\sim $$ ∼ 100 dB at 10 m), while mmWave systems are interference-limited with near-perfect classical estimation. Our results identify THz cell-free multiple-input multiple-output (MIMO) as a compelling application for quantum-enhanced wireless communications and provide design guidelines for practical deployment in future sixth-generation (6G) networks.

Journal of Infrared Millimeter and Terahertz WavesJul 1, 20260 citations

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∼ 3 dB) achieve 14–103.1% sum-rate improvement with quantum-enhanced estimation, compablack to only 2–5% at millimeter-wave (mmWave) frequencies where pilot SNR exceeds 13 dB. The 6–33

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× greater quantum advantage at THz frequencies stems from the fundamental difference in operating regimes: THz systems are noise-limited due to severe path loss (

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∼ 100 dB at 10 m), while mmWave systems are interference-limited with near-perfect classical estimation. Our results identify THz cell-free multiple-input multiple-output (MIMO) as a compelling application for quantum-enhanced wireless communications and provide design guidelines for practical deployment in future sixth-generation (6G) networks.