Ran-Hong Yan

Iterative channel estimation using soft decision feedback

We present an iterative channel estimation scheme with soft decision feedback. In a block-by-block transmission system with a soft-output equalizer and/or decoder it is possible to feed back the decisions to the channel estimator to improve its performance. We propose to feed back soft decisions which will reduce error propagation. We also derive a suboptimal estimator with substantially lower complexity and present simulations for a GSM-like system. The improvement of the bit-error rate over non-iterative schemes with this low complexity estimator is about 0.8 dB (equalizer feedback) and 1.5 dB (decoder feedback).

Advanced signal-processing algorithms for energy-efficient wireless communications

Substantial progress has been made in the receiver signal-processing algorithms for wireless communications to minimize the requirements on signal-to-noise (and/or interference) power ratio and computational complexities for the same quality of service. In cellular infrastructure systems, one of the key system design objectives in the base stations is to maximize the receiver sensitivity, so that the required signal level from the mobile stations can be minimized. The use of advance signal-processing algorithms, based on maximum a posteriori (MAP) estimation, iterative (turbo) channel estimation, equalization, and decoding, allows for a reduction of the required transmitter power by one-third to one-half. Lower computational complexities in the terminals, which implies a reduced power drain on the digital circuits, can be achieved by using techniques that adapt the state complexity of the receiver to the propagation channel. We give an in-depth review of these algorithms, and discuss their performance and implementation requirements.