Authentication in Millimeter-Wave Body-Centric Networks Through Wireless Channel Characterization

Advent of 5G technologies has ensued in massive growth of body-centric communications (BCCs), especially at millimeter-wave (mm-wave) frequencies. As a result, the portable/handheld terminals are becoming more and more “intelligent” but not without the cost of being less secure. Improved authentication measures need to be explored, as effective identity authentication is the first level of security in these devices. This paper presents a novel keyless authentication method exploiting wireless channel characteristics. Human palm has distinct transmission coefficient (S21) for each of the users and is used for in vivo fingerprint identification in this paper. A detailed channel modeling using data acquisition from real environment and empirical approach is adopted to evaluate the usability of this method. The results show that this method can provide a secure operation for the mm-wave 5G BCCs.