Uplink Channel Estimation and Signal Extraction Under Malicious Attack in Massive MIMO System release_as4cr5nk7bablfzl3irs77mzxq

Abstract

This paper investigates correlative attack for the massive MIMO uplink. Malicious users (MUs) send jamming data sequences that are correlative to the data sequences of legitimate users (LUs) to a base station (BS). We consider the problem of channel estimation and signal extraction in the presence of correlative attacks. The right singular matrix of received signal at the BS is a function of the correlation between the legitimate and jamming data in large-scale antenna regime. As a result, correlative attacks degrade the performance of traditional channel estimation methods that base on eigenvalue decomposition (EVD) of the received signals. Then, we propose a signal extraction and channel estimation method to combat against correlative attacks. More precisely, geometric arguments, such as convex hull of extracted signals, are utilized for providing signal extraction and channel estimation criteria. For the optimization of these criteria, we develop an extractor which is able to capture convex hull of desired signals from noisy signals. Based on the proposed extractor, we formulate two optimization problems, whose global minima are solved to perform signal extraction and channel estimation. Experimental results show that when correlation coefficient is 0.6, the proposed method outperforms the EVD-based method more than 5 dB in the sense of normalized mean square error (NMSE).
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