In this paper, the bit error rate (BER) and the outage probability are presented for a maximal ratio combining (MRC) two-dimensional (2D)-RAKE receiver operating in a correlated frequency-selective Nakagami-m fading environment with multiple access interference. A simple approximated probability distribution function of the signal-to-interference-plus-noise ratio (SINR) is derived for the receiver with multiple correlated antennas and RAKE branches in arbitrary fading environments. The combined effects of spatial and temporal diversity order, average received signal-to-noise ratio, the number of multiple access interference, angular spread, antennae spacing and multi-path Nakagami-m fading environment on the system performance are illustrated. Numerical results indicate that the performance of the 2D-RAKE receiver depends highly on the operating environment and antenna array configuration. The performance can be improved by increasing the spatio-temporal diversity gains and antenna spacing.

In order to satisfy the very high traffic demand in crowded hotspot areas and realize adequate security in future fifth-generation networks, this paper studies physical-layer security in the downlink of a two-tier ultra dense heterogeneous network, where a ubiquitous array formed by ultra dense deployed small-cells surrounds a macrocell base station. In this paper, the locations of legitimate users and eavesdroppers are drawn from Poisson point processes. Then, the cumulative distribution functions of the receive signal-to-interference-plus-noise ratio for legitimate users and eavesdroppers are derived. Further, the average secrecy rate and secrecy coverage probability for each tier as well as for the whole network are investigated. Finally, we analyze the influences on secrecy performance caused by eavesdropper density, transmit power allocation ratio, antenna number allocation ratio, and association area radius.

In this paper, a deep learning-based secret key generation scheme is proposed for FDD multiple-input and multiple-output (MIMO) systems. We built an encoder-decoder based convolutional neural network to characterize the wireless environment to learn the mapping relationship between the uplink and downlink channel. The designed neural network can accurately predict the downlink channel state information based on the estimated uplink channel state information without any information feedback. Random secret keys can be generated from downlink channel responses predicted by the neural network. Simulation results show that deep learning based SKG scheme can achieve significant performance improvement in terms of the key agreement ratio and achievable secret key rate.