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Next generation wireless communications systems are pushing the limits of both energy efficiency and spectral efficiency. This presents a challenge at the receiver when it comes to accomplishing tasks such as synchronization, channel estimation, and equalization and has motivated the development of code-aided iterative receiver algorithms in the technical literature. In this paper, we focus on the task of frame synchronization. While previous work has predominately assumed an additive white Gaussian noise channel, we develop code-aided frame synchronization algorithms for multipath channels. An iterative receiver is presented which integrates frame synchronization with iterative channel estimation, equalization, demodulation, and decoding. The receiver design includes a novel frame pre-processing stage to reduce the complexity of the proposed receiver. The complexity and performance of the proposed receiver is compared with that of a receiver based on conventional synchronization. The results demonstrate that the proposed receiver is capable of achieving a gain of up to 3 dB while increasing complexity by only 20%.

Detection of co-channel signals is important as wireless communication systems become increasingly dense. A particularly challenging case is single antenna reception in both the presence of inter-symbol interference and co-channel interference. Optimal joint maximum a posteriori probability (MAP) detection of the co-channel signals is prohibitively complex. Therefore, in this paper, we propose a factor graph-based iterative receiver which approximates joint MAP detection. In the receiver, noise is modeled with a Gaussian mixture distribution since studies have shown that the noise affecting wireless communication systems is often impulsive. Furthermore, the parameters of the factor graph model (channel and noise parameters) are iteratively estimated within the receiver. The proposed receiver is shown to the outperform state-of-the-art receiver algorithms while having a lower complexity in both Gaussian and impulsive noise. We also show significant gains from iterative parameter estimation, especially in non-Gaussian noise.