Pilot-Free Channel Change Detection for mmWave Massive MIMO System

Wu, Yue; Jiao, Yuchen; Gao, Feifei; Gu, Yuantao

doi:10.1109/globecom38437.2019.9014219

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Specifically, the on-line change detection technique was applied in cognitive network to detect the moment when the primary user changes from the active state to the inactive state such that the secondary user can start to transmit when the primary user becomes inactive [38]. More relevant to our work, [39] and [40] studied the on-line change detection schemes to identify the change in the millimeter wave (mmWave) MIMO channel caused by the movement of the obstacles, scatters, etc. However, these change detectors are not designed based on the LLR criterion and not optimal for the trade-off between detection delay and false alarm.…”

Section: B Prior Workmentioning

confidence: 99%

Detecting Abrupt Change in Channel Covariance Matrix for MIMO Communication

Liu

et al. 2023

IEEE Trans. Wireless Commun.

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The acquisition of the channel covariance matrix is of paramount importance to many strategies in multipleinput-multiple-output (MIMO) communications, such as the minimum mean-square error (MMSE) channel estimation. Therefore, plenty of efficient channel covariance matrix estimation schemes have been proposed in the literature. However, an abrupt change in the channel covariance matrix may happen occasionally in practice due to the change in the scattering environment and the user location. Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time. Specifically, this paper first considers the technique of on-line change detection (also known as quickest/sequential change detection), where we need to detect whether a change in the channel covariance matrix occurs at each channel coherence time interval. Next, because the complexity of detecting the change in a high-dimension covariance matrix at each coherence time interval is too high, we devise a low-complexity off-line strategy in massive MIMO systems, where change detection is merely performed at the last channel coherence time interval of a given time period. Numerical results show that our proposed on-line and off-line schemes can detect the channel covariance change with a small delay and a low false alarm rate. Therefore, our paper theoretically and numerically verifies the feasibility of detecting the channel covariance change accurately and quickly in practice.

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Section: B Prior Workmentioning

confidence: 99%

Detecting Abrupt Change in Channel Covariance Matrix for MIMO Communication

Liu

et al. 2023

IEEE Trans. Wireless Commun.

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Section: Introductionmentioning

confidence: 99%

“…Transmission design in a changing environment is studied in [7], [8]. By utilizing the angle domain sparse structure, a significance test of the subspace correlation is proposed in [8] to detect channel changes in massive MIMO systems. To further maximize the transmission rate in a changing channel, a joint channel change and link rate selection algorithm is proposed in [7].…”

Section: Introductionmentioning

confidence: 99%

Age Optimal Sampling Under Unknown Delay Statistics

Tang

Chen

Wang

et al. 2023

IEEE Trans. Inform. Theory

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In this work, we consider a status update system with a sensor and a receiver. The status update information is sampled by the sensor and then forwarded to the receiver through a channel with non-stationary delay distribution. The data freshness at the receiver is quantified by the Age-of-Information (AoI). The goal is to design an online sampling strategy that can minimize the average AoI when the non-stationary delay distribution is unknown. Assuming that channel delay distribution may change over time, to minimize the average AoI, we propose a joint stochastic approximation and non-parametric change point detection algorithm that can: (1) learn the optimum update threshold when the delay distribution remains static; (2) detect the change in transmission delay distribution quickly and then restart the learning process. Simulation results show that the proposed algorithm can quickly detect the delay changes, and the average AoI obtained by the proposed policy converges to the minimum AoI.

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“…Employing change-point detection algorithms to design efficient transmission schemes for block-fading and quasi-static channels has recently been considered in [14]. The key is to estimate channel change points accurately from the high dimensional received signals, and then re-estimate the new CSI and re-design beamforming strategies for channel throughput maximization.…”

Section: Introductionmentioning

confidence: 99%

Joint Link Rate Selection and Channel State Change Detection in Block-Fading Channels

Tang¹,

Hou²,

Wang³

et al. 2021

Preprint

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In this work, we consider the problem of transmission rate selection for a discrete time point-to-point block fading wireless communication link. The wireless channel remains constant within the channel coherence time but can change rapidly across blocks. The goal is to design a link rate selection strategy that can identify the best transmission rate quickly and adaptively in quasistatic channels. This problem can be cast into the stochastic bandit framework, and the unawareness of time-stamps where channel changes necessitates running change-point detection simultaneously with stochastic bandit algorithms to improve adaptivity. We present a joint channel change-point detection and link rate selection algorithm based on Thompson Sampling (CD-TS) and show it can achieve a sublinear regret with respect to the number of time steps T when the channel coherence time is larger than a threshold. We then improve the CD-TS algorithm by considering the fact that higher transmission rate has higher packet-loss probability. Finally, we validate the performance of the proposed algorithms through numerical simulations.

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Pilot-Free Channel Change Detection for mmWave Massive MIMO System

Cited by 7 publications

References 19 publications

Detecting Abrupt Change in Channel Covariance Matrix for MIMO Communication

Detecting Abrupt Change in Channel Covariance Matrix for MIMO Communication

Age Optimal Sampling Under Unknown Delay Statistics

Joint Link Rate Selection and Channel State Change Detection in Block-Fading Channels

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