Frequency synchronization and channel estimation techniques for future wireless communication systems

A reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) wireless communication system is considered. In 5th-generation wireless communication, massive MIMO, which uses a very large number of antennas, is used as a key technology to satisfy the rapidly increasing data rate demands. Additionally, another technique based on the use of an RIS has been recently proposed to improve channel capacity. An RIS is a meta-material sheet that can intentionally adjust the phase of the incident signals. Because of the various advantages that can be achieved by using massive MIMO and RISs, RIS-assisted massive MIMO systems are expected to be widely used in future wireless communication systems. However, in practice, antennas inevitably have hardware impairments, and such hardware impairments cause severe performance degradation, especially in situations where more hardware is used. The performance obtainable by the use of the RIS is sensitive to the channel estimation performance, and hardware impairments lead to a significant degradation of the channel estimation performance. This thesis investigates the effect of carrier frequency offset (CFO), which is one of representative hardware impairments, on the channel estimation performance of such systems. Motivated by the results of this investigation, this thesis proposes methods for estimating/compensating this CFO. Most of the existing synchronization techniques are designed for MIMO systems with collocated antennas, where the same CFO is experienced by the received signal at each antenna. In contrast, for massive MIMO systems with distributed antennas, the received signals at different receive antennas suffer from different CFOs. This makes frequency synchronization of such systems computationally complex, especially as the number of receive antennas grows large. Motivated by this, this thesis investigates the problem of CFO estimation for distributed massive MIMO. We evaluate the performance of distributed massive MIMO systems with both collaborative and non-collaborative CFO estimation techniques. These optimal CFO estimation results can serve as benchmarks on the achievable estimation performance of practical collaborative and non-collaborative CFO estimation techniques for such systems. The proposed estimation methods are tailored to work with periodic pilot sequences. We show how exploiting the relationships which exist between the CFOs can substantially reduce the computational load in the collaborative approach. Accurate channel estimation is essential to achieve the performance gains promised by the use of RISs. A variety of channel estimation methods have been proposed for RIS-aided wireless communication systems; however, none of the existing methods considers the effect of CFO. CFOs can significantly degrade the channel estimation performance systems. Motivated by this, we investigate the effect of CFO on channel estimation for RIS-aided OFDM-based massive MIMO systems. Furthermore, we propose a joint CFO and channel impulse response (CIR) estimation method for such systems for a single-user case and that for a multi-user case. The proposed pilot structure makes it possible to accurately estimate the CFOs and CIRs without extra overhead. For RIS phase shift optimization at the data transmission stage, we propose a projected gradient method (PGM) which achieves the same performance as a computationally demanding grid search technique while having a significantly lower computational complexity. Numerical results demonstrate that the proposed methods provide an improvement in the normalized mean-square error of channel estimation as well as in the achievable rate performance. While these methods require a significantly lower overall computational complexity than the benchmark schemes, the proposed joint CFO and CIR estimation method with the proposed PGM shows an excellent performance as well as shortening the training overhead.