We consider multi-antenna wireless systems aided by reconfigurable intelligent surfaces (RIS). RIS presents a new physical layer technology for improving coverage and energy efficiency by intelligently controlling the propagation environment. In practice however, achieving the anticipated gains of RIS requires accurate channel estimation. Recent attempts to solve this problem have considered the least-squares (LS) approach, which is simple but also sub-optimal. The optimal channel estimator, based on the minimum mean-squared-error (MMSE) criterion, is challenging to obtain and is non-linear due to the non-Gaussianity of the effective channel seen at the receiver. Here we present approaches to approximate the optimal MMSE channel estimator. As a first approach, we analytically develop the best linear estimator, the LMMSE, together with a corresponding majorization-minimization based algorithm designed to optimize the RIS phase shift matrix during the training phase. This estimator is shown to yield improved accuracy over the LS approach by exploiting second-order statistical properties of the wireless channel and the noise. To further improve performance and better approximate the globally-optimal MMSE channel estimator, we propose data-driven non-linear solutions based on deep learning. Specifically, by posing the MMSE channel estimation problem as an image denoising problem, we propose two convolutional neural network (CNN) based methods to perform the denoising and approximate the optimal MMSE channel estimation solution. Our numerical results show that these CNN-based estimators give superior performance compared with linear estimation approaches. They also have low computational complexity requirements, thereby motivating their potential use in future RIS-aided wireless communication systems.

We propose a multiple-input multiple-output (MIMO) quantum key distribution (QKD) scheme for terahertz (THz) frequency applications operating at room temperature. Motivated by classical MIMO communications, a transmitreceive beamforming scheme is proposed that converts the rankr MIMO channel between Alice and Bob into r parallel lossy quantum channels. Compared with existing single-antenna QKD schemes, we demonstrate that the MIMO QKD scheme leads to performance improvements by increasing the secret key rate and extending the transmission distance. Our simulation results show that multiple antennas are necessary to overcome the high free-space path loss at THz frequencies. We demonstrate a nonmonotonic relation between performance and frequency, and reveal that positive key rates are achievable in the 10 − 30 THz frequency range. The proposed scheme can be used for both indoor and outdoor QKD applications for beyond fifth generation ultra-secure wireless communications systems.

Large intelligent surfaces (LIS) present a promising new technology for enhancing the performance of wireless communication systems. So far, the significant performance gains brought by LIS have mainly been shown under the assumption that perfect channel state information is available. In practice however, acquiring accurate channel knowledge poses a significant challenge, and the corresponding overhead can be large.Here, we study the achievable rate of a LIS-assisted communication system accounting for such channel estimation overhead. As a main observation, we demonstrate that there exists a tradeoff between the number of LIS elements K and achievable rate. More specifically, there exists an optimal K * , beyond which, the achievable rate starts to decline, since the power gains offered by LIS are outweighed by the channel estimation overhead. We present analytical approximations for K * , based on maximizing an analytical upper bound on achievable rate that we derive.