Jari Ylioinas

Iterative Joint Detection, Decoding, and Channel Estimation in Turbo-Coded MIMO-OFDM

An iterative receiver for a multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of a list detector, a turbo decoder, and a channel estimator that is based on the space-alternating generalized expectation-maximization (SAGE) algorithm. This paper proposes a way to improve the convergence of the iterative detection and decoding by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits. A new list parallel interference cancellation (PIC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal losses of performance. Furthermore, the organization of spectrally efficient decision-directed (DD) SAGE channel estimation under a constrained number of detector-decoder iterations is optimized by computer simulations, and the SAGE algorithm itself is modified for nonconstant envelope constellations. The list recalculation is shown to improve convergence. It is also shown that the list PIC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. Despite the low preamble density and fast-fading channel, the proposed iterative receiver shows robust performance.

Avoiding Matrix Inversion in DD SAGE Channel Estimation in MIMO-OFDM with M-QAM

A decision directed (DD) channel estimation is considered for a multiple-input multiple-output (MIMO) or- thogonal frequency division multiplexing (OFDM) system to improve the spectral efficiency. Unlike in the pilot based channel estimation, the least-squares (LS) channel estimator operating in the DD mode for MIMO-OFDM requires a matrix inversion. The size of the matrix to be inverted depends on the number of transmit antennas and the length of the channel impulse re- sponse. The frequency domain (FD) space-alternating generalized expectation-maximization (SAGE) channel estimator calculates the LS estimate iteratively avoiding the matrix inversion with a constant envelope modulation. The drawback with the FD- SAGE channel estimator is the required matrix inversion with a non-constant envelope modulation. The size of the matrix to be inverted depends on the length of the channel impulse response. However, it is considerably less complex than the LS channel estimator in the DD mode. In this paper, a time domain (TD) SAGE channel estimator is derived to avoid the matrix inversion in DD channel estimation for MIMO-OFDM systems when using non-constant envelope modulation. The derived TD- SAGE channel estimator is shown to offer the same performance as the FD-SAGE channel estimator with reduced complexity. antennas and the length of the channel impulse response. With constant envelope constellation, the frequency domain (FD) space-alternating expectation-maximization (SAGE) (4) channel estimator avoids this matrix inversion due to dividing the MIMO channel estimation problem into multiple SISO channel estimation problems. However, the drawback of the FD-SAGE channel estimator is that it requires a matrix inver- sion having the number of elements per one dimension equal to the length of the channel impulse response when non-constant envelope constellations are used. In this paper, we derive the DD time domain (TD) SAGE channel estimator for a MIMO-OFDM system. The SISO channel estimation is further divided into multiple single tap estimation problems by the DD-TD-SAGE estimator. The per- formance and the complexity of the DD-TD-SAGE estimator is compared to those of the DD-LS and DD-FD-SAGE estima- tors. As a benchmark, the performance of the preamble-based LS estimation with minimum mean square error (MMSE) post-processing is provided as well. The proposed estimator gives practically the same performance as the DD-FD-SAGE channel estimator without the need for matrix inversion with constant envelope constellations, and, thus, resulting in lower complexity, which is also characterized.

Space-Time Equalizers for MIMO High Speed WCDMA Downlinks

Multiple-input multiple-output (MIMO) communications based on multiple transmit and receive antennae will be applied to enhance the data rates of the third generation (3G) cellular systems or wideband code division multiple access (WCDMA) and in particular, their high speed downlink packet access (HSDPA) services. This causes both spatial multiplexing interference (SMI) and downlink multiple access interference (MAI). In this paper, we derive an improved linear minimum mean square error (LMMSE) detector approximation suitable for MIMO-WCDMA systems. We also propose a new two-stage receiver architecture to suppress the effects of both MAI and SMI in HSDPA communications. SMI often dominates MAI, since it does not benefit from spreading gain. Therefore, a spatial maximum a posteriori (MAP) detector is used to suppress SMI, and a LMMSE based channel equalizer is applied as its front- end. The performance of the resulting LMMSE-MAP receiver structure is studied via Monte Carlo computer simulations with assumptions very realistically mimicking those of the HSDPA specification. The results show that it clearly outperforms the rake receiver or the improved LMMSE equalizer approximation, which, on the other hand, is also shown to be superior to the more conventional LMMSE equalizers.

Implementation of LS, MMSE and SAGE channel estimators for mobile MIMO-OFDM

The use of decision directed (DD) channel estimation in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) downlink receiver is studied in this paper. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark. The space-alternating generalized expectation-maximization (SAGE) algorithm is used to improve the performance from that of the pilot symbol based least-squares (LS) channel estimator. The DD channel estimation improves the performance with high user velocities, where the pilot symbol density is not sufficient. It can also be used to reduce the pilot overhead without any performance degradation. Minimum mean square error (MMSE) filtering can also be used in estimating the channel in between pilot symbols. The pilot based LS, MMSE and the SAGE channel estimators are implemented and the performance-complexity trade-offs are studied.

Interference resistant receivers for WCDMA MIMO downlink

Optimal and suboptimal spatial maximum a posteriori (MAP) receivers in a concatenation of linear minimum mean square error (LMMSE) equalizer structure are presented for multiple-input-multiple-output (MIMO) wideband code division multiple access (WCDMA) systems. The performance of a list sphere detector is compared to that of the spatial MAP and LMMSE-MAP receivers. The complexity of the receivers is also discussed. Finally, the LMMSE-MAP based receivers are proposed for the frequency selective fading channels where LMMSE part mitigates the multiple access interference (MAI) and inter-antenna interference (IAI) is mitigated by the spatial MAP or its approximation. Remarkable performance gains compared to the conventional rake receiver are observed in computer simulations with realistic WCDMA system parameters.

On the Activation Ordering of Detector, Decoder, and Channel Estimator in Iterative Receiver for MIMO-OFDM

An iterative receiver for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of the a posteriori probability (APP) algorithm, the repeat-accumulate (RA) decoder, and the least-squares (LS) channel estimator. An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. This paper proposes to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and find out the optimal activation schedule for them. An analytical expression of the EXIT function is derived for the LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of channel estimate mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the RA decoder. A trellis search based algorithm is shown to find the convergence with the lowest possible complexity using the EXIT charts. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided.

An Iterative Receiver for Joint Detection, Decoding, and Channel Estimation in Turbo Coded MIMO OFDM

An iterative receiver using a list detector, a turbo decoder, and the space-alternating generalized expectation- maximization (SAGE) based channel estimation for a multiple- input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and to estimate the channel state. Different iterative processing possibilities are identified and compared to each other from the performance point of view. The performance of the list parallel interference cancellation (PIC) detection algorithm is compared to the K-best list sphere detector (LSD) while using them as a soft MIMO detector to approximate the a posteriori probability (APP) detector with reduced complexity and with minor losses in the performance. According to the results, the iterative receiver performs well with the low pilot overhead despite the fast fading channel.

Scheduling of the Activations in Iterative Detection, Decoding, and Channel Estimation for MIMO-OFDM

An iterative receiver for a multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of the a posteriori probability (APP) algorithm, the repeat-accumulate (RA) decoder, and the least-squares (LS) channel estimator. An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. This paper proposes to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and find out the optimal activation schedule for them. A semi-analytical expression of the EXIT function is derived for the decision directed LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of channel estimate mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the RA decoder. Trellis search based algorithms are shown to find the convergence with the lowest possible complexity using the EXIT charts. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided.

Decision directed channel estimation for improving performance in LTE-A

The use of decision directed (DD) channel estimation in a MIMO-OFDM downlink receiver with a LTE pilot structure is studied in this paper. The space-alternating generalized expectation-maximization (SAGE) algorithm is used to improve the receiver performance from the one obtained by using the pilot symbol based least-squares (LS) channel estimator. The DD channel estimation improves the performance with high user velocities, where the pilot symbol density is not sufficient. The DD channel estimation can also be used to reduce the pilot overhead without any performance degradation by transmitting data instead of pilot symbols. The complexity of the SAGE channel estimator is discussed as well.

Decision-Directed Channel Estimation Implementation for Spectral Efficiency Improvement in Mobile MIMO-OFDM

Channel estimation algorithms and their implementations for mobile receivers are considered in this paper. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) receiver. The decision directed (DD) space-alternating generalized expectation-maximization (SAGE) algorithm is used to improve the performance from that of the pilot symbol based least-squares (LS) channel estimator. The performance is improved with high user velocities, where the pilot symbol density is not sufficient. Minimum mean square error (MMSE) filtering is also used in estimating the channel in between pilot symbols. The pilot overhead can be reduced to a third of the LTE pilot overhead with DD channel estimation, obtaining a ten percent increase in data throughput. Complexity reduction and latency issues are considered in the architecture design. The pilot based LS, MMSE and the SAGE channel estimators are implemented with a high level synthesis tool, synthesized with the UMC 0.18 μ