Du, P., Zhang, C., Jing, Y., Fang, C., Zhang, Z., & Huang, Y. (2024). Jamming Detection and Channel Estimation for Spatially Correlated Beamspace Massive MIMO. arXiv preprint arXiv:2410.14215.
This paper addresses the challenge of jamming attacks in beamspace massive MIMO systems, aiming to develop effective techniques for jamming detection and accurate channel estimation for both legitimate users and jammers under spatially correlated channel conditions.
The authors propose a channel-statistics-assisted jamming detection scheme based on the locally most powerful test (LMPT), leveraging the statistical properties of the received signals projected onto unused pilot sequences. For channel estimation, they introduce a two-step minimum mean square error (MMSE) based approach. This involves estimating the inner-products of jamming and system pilots, followed by estimating the jamming and user channels using projected observation vectors and the estimated inner-products.
The research highlights the importance of considering spatial channel correlation in beamspace massive MIMO systems for robust jamming detection and channel estimation. The proposed techniques offer practical solutions to enhance the security and reliability of these systems under potential jamming attacks.
This work contributes significantly to the field of wireless communication security by providing effective countermeasures against jamming attacks in next-generation massive MIMO systems. The proposed techniques can be applied to enhance the robustness and resilience of future wireless networks.
The research primarily focuses on single RF chain architecture and assumes a fixed jammer precoder during training. Future research could explore the extension of these techniques to multi-RF chain architectures and investigate scenarios with dynamic jammer behavior. Additionally, exploring the integration of the proposed methods with existing anti-jamming techniques like beamforming and resource allocation could further enhance system performance.
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by Pengguang Du... om arxiv.org 10-21-2024
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