We propose an iterative tuning process of blind/group-blind detector based on constant modulus property of digitally modulated signal. By adopting the iterative tuning process after applying blind/group-blind detectors, significant gains are attained compared to the original blind/group-blind detectors. The signal to interference and noise ratio (SINR) approaches to ideal MMSE detector even with reasonably small number of data samples.

Motivated by the result in [12] we present a new block iterative multiuser detection algorithm utilizing the constant modulus (CM) property of data symbols. The proposed iterative detector is developed based on a constrained least square (LS) cost function and CM projection. It can be seen as an extension of least squares constant modulus algorithm [1] in a sense that it minimizes the CM and the LS cost functions iteratively. Simulation study shows that even for a reasonably small number of symbols, say around 100 received symbols, the proposed iterative detector approaches the performance of ideal MMSE detector and considerably outperforms the conventional blind detectors, such as subspace blind method [7] or MOE detector [5].

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.

The minimum mean square error (MMSE) multiple antenna transmission scheme for a code division multiple access (CDMA) system was recently developed by Choi and Perreau [1]. In this paper, we first show that the zero-forcing (ZF) transmit beamformer for multiple antenna CDMA system has the same form as the MMSE beamformer given by Choi and Perreau. We then develop an analytical method to obtain a closed-form expression of the bit error rate (BER) of the ZF transmit beamformer when there are channel estimation errors. The analytical and simulation results show good agreement, and confirm the importance of accurate channel state information (CSI) at the transmitter when using the ZF transmit beamformer.

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.

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.