Vital sign monitoring via a phased array radar transmitting continuous-wave (CW) or linear frequency-modulated continuous-wave (FMCW) offers contactless, low-cost, continuous monitoring of vital signs, i.e., human heart rate and breathing rate. In this paper, we demonstrate that, besides the authorized receiver of radar echoes, a passive eavesdropper could process the reflections of the CW or linear FMCW waveforms off the patient being monitored and decipher their vital signals, thus violating their privacy. We then propose an approach to protect vital sign privacy. Specifically, we propose the use of a nonlinear FMCW waveform that distorts the radar echoes, making it more difficult for the eavesdropper to extract the vital signs without knowledge of the specific parameters of the non-linear FMCW waveform. At the same time, the authorized receiver, with knowledge of those parameters, can easily extract the vital signs. Therefore, the proposed waveform can simultaneously implement vital sign monitoring and user privacy protection. Numerical experiments demonstrate the effectiveness of our proposed scheme.

Time-modulated arrays (TMAs) transmitting information bearing orthogonal frequency division multiplexing (OFDM) signals can achieve directional modulation. By turning its antennas on and off in a periodic fashion, the TMA can be configured to transmit the OFDM signal undistorted in the direction of a legitimate receiver and scrambled everywhere else. This capability has been proposed as means of securing the transmitted information from unauthorized users. In this paper, we investigate how secure the TMA OFDM system is, by looking at the transmitted signal from an eavesdropper’s point of view. We demonstrate that the symbols observed by the eavesdropper across the OFDM subcarriers are linear combinations of the source symbols, with mixing coefficients that are unknown to the eavesdropper. We propose the use of independent component analysis (ICA) theory to obtain the mixing matrix and provide methods to resolve the column permutation and scaling ambiguities, which are inherent in the ICA problem, by leveraging the structure of the mixing matrix and assuming knowledge of the characteristics of the TMA OFDM system. In general, resolving the ambiguities and recovering the symbols requires long data. Specifically for the case of the constant modulus symbols, we propose a modified ICA approach, namely the constant-modulus ICA (CMICA), that provides a good estimate of the mixing matrix using a small number of received samples. We also propose countermeasures which the TMA could undertake in order to defend the scrambling. Simulation results are presented to demonstrate the effectiveness, efficiency and robustness of our scrambling defying and defending schemes.

Time-modulated arrays (TMA) transmitting orthogonal frequency division multiplexing (OFDM) waveforms achieve physical layer security by allowing the signal to reach the legitimate destination undistorted, while making the signal appear scrambled in all other directions. In this paper, we examine how secure the TMA OFDM system is, and show that it is actually possible for the eavesdropper to defy the scrambling. In particular, we show that, based on the scrambled signal, the eavesdropper can formulate a blind source separation problem and recover data symbols and TMA parameters via independent component analysis (ICA) techniques. We show how the scaling and permutation ambiguities arising in ICA can be resolved by exploiting the Toeplitz structure of the corresponding mixing matrix, and knowledge of data constellation, OFDM specifics, and the rules for choosing TMA parameters. We also introduce a novel TMA implementation to defend the scrambling against the eavesdropper.

In frequency-division-duplex (FDD) massive MIMO systems, channel state information (CSI) feedback waiting phase does not get fully exploited since base station needs to wait for the CSI feedback before transmitting downlink data. The proportion of the CSI feedback waiting phase during the downlink transmission would be high as the MIMO system scales up, which sacrifices downlink rates of the FDD massive MIMO systems significantly. In this paper, we first present a channel prediction-aided FDD scheme to utilize the idle waiting time efficiently. Then we propose a novel channel prediction method based on Bayesian neural network (BNN), which can handle the uncertainty in a natural manner and learn regularization from data without painstaking manual pre-tuning of network hyperparameters. Numerical results show that our proposed channel prediction-aided FDD scheme can achieve remarkable performance gains in terms of either achievable downlink rates or bit error rate. Moreover, our proposed BNN-based channel predictor is much more effective and robust in contrast to the state-of-the-art channel prediction techniques such as autoregressive model and recurrent neural network.

In massive multiple-input-multiple-output (MIMO) systems, wireless channels are estimated at a fixed and short time interval for all users, which causes redundancy since most of users in practice might have a considerably larger coherence time than the prescribed time interval of estimation, and consequently wastes considerable signaling resources on channel acquisition. In this paper, we propose a novel channel estimation scheme for time-division-duplex massive MIMO systems, which can fully exploit the redundancy by exploring the temporal channel correlation underlying the channel aging effect. We also derive a rigorous lower bound on the achievable spectral efficiency, and maximize the lower bound to determine the optimal time interval of channel estimation. Numerical results show that the proposed estimation scheme can offer great spectral efficiency gains over the conventional one, and provide insights on how to put into practice the proposed scheme.

In this paper, we consider a massive multiple-input- multiple-output (MIMO) system, where the base station (BS) is equipped with a large number of antennas while serving a much smaller number of users simultaneously. Though massive MIMO systems can provide significant spectral and energy efficiency via simple signal processing, the required channel state information (CSI) overhead is still a huge challenge, especially for the FDD mode. The basic frame structure of the FDD massive MIMO does not fully exploit the CSI feedback waiting phase since the BS needs to wait some time for the CSI feedback sent by users and then transmit data in downlink with the estimated CSI. The proportion of the CSI feedback waiting phase in the downlink transmission would be high as the MIMO scaling up, which reduces downlink rates for the FDD massive MIMO systems to some extent. In this paper, we propose two novel downlink precoding and transmission (DPT) schemes for FDD systems by exploiting the CSI feedback waiting phase. The corresponding performance comparisons between our proposed DPT methods, the contemporary DPT scheme and the ideal DPT one are also provided based on the COST 2100 outdoor channel model. Numerical results show that one of our proposed DPT schemes can achieve higher downlink rates than the contemporary scheme in relative low-mobility scenarios. The other proposed DPT scheme performs much better and is robust to user mobility.