Iterative receivers for space-time block-coded OFDM systems in dispersive fading channels

Abstract

We consider the design of iterative receivers for space-time block-coded orthogonal frequency-division multiplexing (STBC-OFDM) systems in unknown wireless dispersive fading channels, with or without outer channel coding. First, we propose a maximum-likelihood (ML) receiver for STBC-OFDM systems based on the expectation-maximization (EM) algorithm. By assuming that the fading processes remain constant over the duration of one STBC code word and by exploiting the orthogonality property of the STBC as well as the OFDM modulation, we show that the EM-based receiver has a very low computational complexity and that the initialization of the EM receiver is based on the linear minimum mean square error (MMSE) channel estimate for both the pilot and the data transmission. Since the actual fading processes may vary within one STBC code word, we also analyze the effect of a modeling mismatch on the receiver performance and show both analytically and through simulations that the performance degradation due to such a mismatch is negligible for practical Doppler frequencies. We further propose a Turbo receiver based on the maximum a posteriori-EM algorithm for STBC-OFDM systems with outer channel coding. Compared with the previous noniterative receiver employing a decision-directed linear channel estimator, the iterative receivers proposed here significantly improve the receiver performance and can approach the ML performance in typical wireless channels with very fast fading, at a reasonable computational complexity well suited for real-time implementations.Index Terms-Expectation-maximization, fast fading, minimum mean square error, orthogonal frequency-division multiplexing, space-time block codes, turbo principle, wireless dispersive channels.

Receiver design for MIMO OFDM transmission over time variant channels

“…We compare our algorithm with an EM-based iterative MMSE receiver such as the one proposed in [7] and [16]. In contrast to our work, the authors in [7] and [16] take a data-centric approach, treating the transmitted signal as the desired parameter and the channel as the unobserved data.…”

“…In contrast to our work, the authors in [7] and [16] take a data-centric approach, treating the transmitted signal as the desired parameter and the channel as the unobserved data. This algorithm further confines its pilots to the first ST block.…”

“…EM A refers to the iterative MMSE scheme while EM B refers to the Kalman filter based scheme proposed in this chapter. Our algorithm (EMB) outperforms EM A of [7] in both pilot scenarios. One reason for this performance improvement is that our algorithm incorporates the time correlation information and the most recent channel estimate in every iteration of the EM algorithm.…”

“…The pilots are used to produce an initial channel estimate for the first ST block. This estimate is in turn used to predict the initial channel estimate for the subsequent ST blocks by employing a time correlation filter [7]. These initial estimates are used to kick-start the EM algorithm.…”

“…The channel constraints include the finite impulse response and time and frequency correlation [4]. The channel estimate can also be improved iteratively in a data-aided fashion [5] or more rigourously by the expectation maximization (EM) approach [6], [7], [8], [9]. This paper considers receiver design for OSTBC-OFDM transmission over a frequency selective, time-variant channel.…”