Synchronization and Channel Estimation in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is a strong candidate for the fifth generation (5G) communications system for its high data rate and link reliability. Combining the massive MIMO with orthogonal frequency division multiplexing (OFDM) technique will increase the robustness of the system against the delay spread in the multipath channel. However, OFDM systems are sensitive to frequency synchronization errors, which degrade the system performance significantly. In addition, OFDM systems also suffer from high peak to average power ratio (PAPR). Hence we also consider single carrier system (SC), which has a much lower PAPR. Specifically, in this thesis, we studied symbol synchronization for correcting time delays for massive MIMO systems with SC. We implemented symbol synchronization for the MIMO downlink case (each user performs symbol synchronization). It is found that the Gardner’s algorithm can be readily applied for the massive MIMO system for symbol synchronization. For OFDM-based massive MIMO systems, both channel estimation and frequency synchronization are considered. For feasible channel estimation for the massive MIMO system, the time-division duplex (TDD) is assumed, in which case, the standard least-square (LS) channel estimation is applied in the uplink (UL), and the estimated channel is then used for MIMO precoding in the downlink (DL). The OFDM system is sensitive to the carrier frequency offset (CFO). We use pilot-based CFO estimation (instead of blind frequency synchronization) to ensure good performance of the frequency synchronization. To avoid the high complexity of joint estimation of the CFOs of all the users at the base station, we assume that each user estimates its CFO during the DL and adjusts its transmission accordingly in the UL. It is shown that the CFO estimation of the MIMO system has similar performance as that of the single-input single-output system.