In this paper, we propose an algorithm for estimating the carrier frequency offset (CFO) in OFDM-based multi-cell networks. In 3GPP-LTE single-cell systems, the CFO can be estimated by applying the Schmidl algorithm. However, multi-cell interference (MCI) is induced in multi-cell environments; as a result the MCI degrades estimate accuracy. One solution to mitigate MCI may be via properly designing the training sequences. In this paper, we propose a method for generating training sequences with good orthogonality in both time and frequency domain. Therefore, MCI can be effectively suppressed and CFO estimation algorithms designed for single-user or single-cell environments can be slightly modified, and applied in multi-cell environments. An example is given for showing how to modify the estimation algorithms. Consequently, the computational complexity can be dramatically reduced. Moreover, the training sequences can be applied for detecting cell identity (ID) thanks to its good orthogonality. Simulation results show that the proposed sequences and the CFO estimation algorithms outperform conventional schemes in multi-cell environments.