Low-complexity frequency synchronization for wireless OFDM systems

Abstract

The Orthogonal Frequency Division Multiplexing (OFDM) system provides an efficient and robust solution for communication over frequency-selective fading channels and has been adopted in many wireless communication standards. The multiple-input and multiple-out (MIMO) OFDM system further increases the data rates and robustness of the OFDM system by using multiple transmit and receive antennas. The multi-user MIMO-OFDM system is an extension of the MIMO-OFDM system to a multi-user context. It enables transmission and reception of information from multiple users at the same time and in the same frequency band. One drawback of all wireless OFDM systems is their sensitivities to frequency synchronization errors, in the form of carrier frequency offsets (CFO¿s). CFO causes inter-carrier interference, which significantly degrades the system performance. Accurate estimation and compensation of CFO is thus essential to ensure good performance of OFDM systems. To this end, many CFO estimation and compensation algorithms have been described in the literature for different wireless OFDM systems. These algorithms can be broadly divided into two categories, namely blind algorithms and training-based algorithms.

A key drawback of blind algorithms is their high computational complexity. In this thesis, we address this drawback by developing low-complexity blind CFO estimation algorithms exploiting null subcarriers in single-input single-output (SISO) OFDM systems. Null subcarriers are subcarriers at both ends of the allocated spectrum that are left empty and used as guard bands. To reduce the complexity of existing algorithms, we derive a closed-form CFO estimator by using a low-order Taylor series approximation of the original cost function. We also propose a successive algorithm to limit the performance degradation due to the Taylor series approximation. The null subcarrier placement that maximizes the signal to noise ratio (SNR) of the CFO estimation is also studied. Weshow that to maximize the SNR of CFO estimation, null subcarriers should be evenly spaced.

A key drawback of training-based algorithms is the training overhead from the transmission of training sequences, as it reduces the effective data throughput of the system. Compared to SISO-OFDM systems, the training overhead for MIMO-OFDM systems is even larger due to the use of multiple antennas. To address this drawback, in this thesis, we propose an efficient training sequence design for MIMO-OFDM systems using constant amplitude zero autocorrelation (CAZAC) sequences. We show that using the proposed training sequence, the CFO estimate can be obtained using low-complexity correlation operations and that the performance approaches the Cramer-Rao Bound (CRB). In the uplink of multi-user MIMO-OFDM systems, there are multiple CFO values between the base-station and different users. The maximum-likelihood CFO estimator is not practical here because its complexity grows exponentially with the number of users. To reduce this complexity, we propose a sub-optimal CFO estimation algorithm using CAZAC training sequences. Using the proposed algorithm, the CFO of each user can be estimated using simple correlation operations, while the computational complexity grows only linearly with the number of users. The performance approaches the single-user CRB for practical SNR values. We also find the CAZAC sequences that maximize the signal to interference ratio of the CFO estimation.