Achraf Dhayni

In Spatial Multiplexing MIMO systems, many powerful non-linear detection techniques as sphere decoding have emerged to overcome the performance limitations of linear detection techniques. However, these non-linear techniques suffer from high complexity that increases dramatically with the number of antennas and the modulation order. Hence, they cannot be implemented on highly parallel hardware architecture and are thus not suitable for real-time high data rate transmission. In this paper, a new detection technique is proposed to approach the optimal performance obtained by Maximum Likelihood (ML) detector without increasing the complexity significantly. This detector is denoted by OSIC-ML since it combines two techniques: the Ordered Successive Interference Cancellation (OSIC) and the ML. The proposed OSIC-ML detector shows a near-optimal performance at very low complexity even with large scale MIMO and imperfect channel estimation, where this complexity can be efficiently controlled to achieve the desired complexity-performance tradeoff.

In this paper, we propose a new algorithm for orthogonal frequency-division-multiplexing (OFDM) systems. The algorithm is based on particle fitering (PF) for joint carrier recovery and channel estimation in slow Rayleigh fading channel. The algorithm has been designed to work with both the parametric L-path channel model (with known path delays) and the equivalent discrete-time channel model. PF is a powerful tool for estimating and tracking parameters in non-linear model. In the algorithm, we use a combination of Kalman filtering and Sequential Monte Carlo Sampling to estimate the channel fading coefficients and the posterior probability density of the unknown carrier frequency offset (CFO). Afterwards, the channel matrix is thus easily computed, and the data symbol is estimated with free intercarrier interference (ICI) by using MMSE equalizer. It is shown that our algorithm has a good performance in terms of MSE and BER and approaches the BER of the ideal case for which the channel response and CFO are known.