Joint Low-Rank Factor and Sparsity for Detecting Access Jamming in Massive MTC Networks

Due to the weak security protection capabilities of the low-cost and low-complexity massive access of machine-type devices, massive machine-type communications (mMTC) networks are extremely vulnerable to the access jamming, which can affect the correctness of activity and data detection of legitimate devices and even leads to the paralysis of the mission-critical mMTC applications. This paper studies detection problem of the access jamming in the uplink of mMTC (AJ-UM), and we propose to exploit the characteristics of the joint low-rank factor and sparsity (JLFS) to detect the AJ-UM. Our detection method is motivated by the fact that the JLFS-based feature will be significantly impacted if the AJ-UM happens. We first extract the JLFS-based feature by solving a low-rank maximum likelihood factor analysis problem with sparsity constraint, and then perform the AJ-UM detection in a sequential manner. Moreover, the proposed JLFS-based method does not need to know the accurate prior information of the JLFS-based feature in the presence or absence of the AJ-UM, which can determine the AJ-UM exists as long as there is an abrupt change in the JLFS-based feature. Numerical results are finally presented to confirm the effectiveness of the proposed JLFS-based method.