Holger Claussen

Improved max-log-map turbo decoding by maximization of mutual information transfer

The demand for low-cost and low-power decoder chips has resulted in renewed interest in low-complexity decoding algorithms. In this paper, a novel theoretical framework for improving the performance of turbo decoding schemes that use the max-log-MAP algorithm is proposed. This framework is based on the concept of maximizing the transfer of mutual information between the component decoders. The improvements in performance can be achieved by using optimized iteration-dependent correction weights to scale the a priori information at the input of each component decoder. A method for the offline computation of the correction weights is derived. It is shown that a performance which approaches that of a turbo decoder using the optimum MAP algorithm can be achieved, while maintaining the advantages of low complexity and insensitivity to input scaling inherent in the max-log-MAP algorithm. The resulting improvements in convergence of the turbo decoding process and the expedited transfer of mutual information between the component decoders are illustrated via extrinsic information transfer (EXIT) charts.

Improved max-log map turbo decoding using maximum mutual information combining

The demand for low-cost and low-power decoder chips has resulted in renewed interest in low-complexity decoding algorithms. In this paper a novel modification of the Max-Log-MAP algorithm is proposed for use in a turbo decoding process. This is achieved by scaling the a priori information by correction weights at each iteration, in order to maximize the exchange of mutual information between the component decoders. It is shown that the proposed technique results in a performance which approaches that of a turbo decoder using the optimum MAP algorithm, while maintaining the advantages of low complexity and insensitivity to input scaling inherent in the Max-Log-MAP algorithm. A second contribution of this paper is a method for off-line computation of the optimum weight values. The convergence behaviour of the proposed decoder is analysed via extrinsic information transfer (EXIT) charts.

High-performance MIMO receivers based on multi-stage partial parallel interference cancellation

Turbo-encoded multiple-input multiple-output (MIMO) radio links have recently been proposed for the support of high-speed downlink packet access (HSDPA) in UMTS, where the re-use of spreading codes across the transmitter antennas results in high levels of interference. The state-of-the-art receiver chain for such a link incorporates space-time channel equalization, de-spreading, pre-whitening and finally a posteriori probability (APP) detection. A multi-stage partial parallel interference canceller (MS-PPIC) is considered as a low complexity alternative to the APP detector and its max-log variant. Nonlinear cancellation metrics are derived for the MS-PPIC and its performance is compared with the APP detector for flat and dispersive channels. It is shown that the MS-PPIC can provide similar performance compared to APP and, for low coding rates, superior performance compared to max-log-APP, at a substantially lower computational complexity.

A Low Complexity Iterative Receiver Based on Successive Cancellation for MIMO

Turbo-encoded multiple-input multiple-output (MIMO) systems have recently been proposed for the support of high-speed downlink packet access (HSDPA) in UMTS, where the re-use of spreading codes across the transmitter antennas results in high levels of interference. The state of the art receiver for this system incorporates a channel equalizer, followed by an a posteriori probability (APP) detector and a turbo decoder. However, the complexity of APP detection can become prohibitive since it grows exponentially with the number of transmitter antennas and the modulation order. In this paper, a MIMO receiver is proposed which replaces the optimum but complex APP detector by successive interference cancellation (SIC) incorporating sub-optimal matched filter detection. Using convolutional encoding at the transmitter, the receiver performance is sustained via iterations between the simplified detector and the convolutional decoder. In combination with a proposed novel soft-output combining scheme, it is shown that the new receiver can outperform the APP-based receiver at a much lower complexity and with no need for channel equalization.

Impact of modeling errors on the performance of MIMO receivers with APP and PIC detection

Turbo-encoded multiple-input multiple-output (MIMO) radio links have been recently proposed for high-speed downlink packet access (HSDPA) in UMTS, where the reuse of spreading codes across the transmitter antennas results in high levels of interference. A state-of-the-art receiver chain for such a link incorporates space-time channel equalization, despreading, prewhitening, and detection. In this paper, the impact of modeling errors at the equalizer output on the receiver performance is investigated. Both a precise model and a more pragmatic approximate model are considered. Degradations resulting from imperfect knowledge of the channel state information at the receiver are also evaluated. The a posteriori probability (APP) detector and its max-log variant as well as the multistage partial parallel interference canceller (MS-PPIC) are examined as detection candidates in 1/3-rate and 1/2-rate coded 4/spl times/4 MIMO links. It is shown that, surprisingly, the MS-PPIC can provide superior performance compared to max-log-APP.