We propose a simple initial frame timing acquisition algorithm for cellular OFDMA systems. The proposed algorithm utilizes the 9 dB boost in preamble power set by the IEEE 802.16e standard. Simulation results show that the proposed algorithm succeeds in acquiring the starting point of a frame under not only single cell but also multi-cell environments, while the conventional autocorrelation-based method fails under multi-cell environment.

In this letter, we propose a computationally efficient search space for QRM-MLD that is used for spatially multiplexed multiple antenna systems. We perform a set of computer simulations to show that the proposed method achieves a performance that is near to that of the original QRM-MLD, while its computational complexity is near to that of rank-QRM-MLD.

The performance of the ordered successive interference cancellation (OSIC) signal detection method is well known to depend on the successful detection of the first layer. In a previous work, in an effort to mitigate the error propagation effect, all the constellation points were tried as the first layer symbol, thereby achieving a better performance. In this letter, we show that the selection of the first layer impacts the error performance significantly, and based on the observation, we propose a novel signal detection method QR-LRL. In the proposed work, the least reliable layer (LRL) is chosen to be the first layer, which is shown to be the best choice in terms of noise enhancement in detecting the other layers. Also, we discuss Log Likelihood Ratio (LLR) computation when the proposed method is used. Computer simulations confirm the efficacy of the proposed method.

A simple detector named QR-LRL for MIMO systems was proposed in and it was shown that QR-LRL approached the hard-output ML performance. However, its soft-output performance is not capable of approaching the near ML performance. In this letter, we propose a novel detection method which can generate reliable soft-outputs while avoiding the empty vector set problem. The proposed detector efficiently uses the upper triangular structure in QR decomposition. Simulation results show that the proposed detector can approach the near soft-output ML performance as well as hard-output with feasible complexity.