Stemming Downlink Leakage from Training Sequences in Multi-User MIMO Networks

Multi-User MIMO has attracted much attention due to its significant advantage of increasing the utilization ratio of wireless channels. Recently a serious eavesdropping attack, which exploits the CSI feedback of the FDD system, is discovered in MU-MIMO networks. In this paper, we firstly show a similar eavesdropping attack for the TDD system is also possible by proposing a novel, feasible attack approach. Following it, a malicious user can eavesdrop on other users' downloads by transforming training sequences. To prevent this attack, we propose a secure CSI estimation scheme for instantaneous CSI. Furthermore, we extend this scheme to achieve adaptive security when CSI is relatively statistical. We have implemented our scheme for both uplink and downlink of MU-MIMO and performed a series of experiments. Results show that our secure CSI estimation scheme is highly effective in preventing downlink leakage against malicious users.