This thesis presents novel signal processing techniques and performance analysis of future communication technologies for the upcoming beyond 5G (B5G) and sixth-generation (6G) wireless communication systems. Broadly speaking, the thesis is divided into two parts: the first on classical communications, the second on quantum communications. In the first part, we focus on reconfigurable intelligent surface (RIS), which is a new energy efficient physical layer technology proposed for B5G/6G systems. We propose a deep learning based channel estimation scheme for RIS-assisted communication systems. In order to reduce the high pilot overhead due to the passive RIS elements, ‘On-Off’ based and ‘Grouping’ based adaptive transmission schemes are investigated. We derive an upper bound on the achi...[ Read more ]

This thesis presents novel signal processing techniques and performance analysis of future communication technologies for the upcoming beyond 5G (B5G) and sixth-generation (6G) wireless communication systems. Broadly speaking, the thesis is divided into two parts: the first on classical communications, the second on quantum communications. In the first part, we focus on reconfigurable intelligent surface (RIS), which is a new energy efficient physical layer technology proposed for B5G/6G systems. We propose a deep learning based channel estimation scheme for RIS-assisted communication systems. In order to reduce the high pilot overhead due to the passive RIS elements, ‘On-Off’ based and ‘Grouping’ based adaptive transmission schemes are investigated. We derive an upper bound on the achievable rate, and present closed form approximations for the optimum number of RIS elements to be switched on, and the optimum group size that maximizes the achievable rate. We also analyze the performance of a RIS-assisted communication system, where we characterize the statistical properties of the received signal-to-noise ratio and derive analytical approximations for the outage probability, average achievable rate and average symbol error probability.

The second part of the thesis focuses on quantum key distribution (QKD) protocols, that can be used for ultra-secure data transmission in B5G/6G networks. We propose a multiple-input multiple-output (MIMO) transmission scheme for a continuous variable QKD (CV-QKD) system operating at terahertz (THz) frequencies and solve related channel estimation and secret key rate (SKR) analysis problems. The MIMO CV-QKD scheme helps to overcome the high path loss at THz frequencies and improves the SKR of the system. In the last part of the thesis, we propose data-driven algorithms for efficient parameter estimation of single-mode Gaussian quantum states, which are an essential component of the CV-QKD protocol for encoding the key information.