Xuemei Ouyang

Optimal antenna diversity combining for IEEE 802.11a system

IEEE 802.11a/g devices promise to accommodate the increasing consumer demands for high data rate wireless communications. A novel optimal decoding method for receiver antenna diversity combining for a COFDM system is discussed in this paper to provide robust wireless connections for applications such as wireless multimedia communications. The proposed optimal diversity combining integrates the channel equalization, demodulation (bit metrics calculation) and diversity combining in the decoder, which is the maximum likelihood decoding method for the diversity combining for the COFDM system. The proposed method can have about 4-6 dB gain over a single antenna receiver and 2-4 dB gain over a traditional symbol level diversity combining receiver without increasing the implementation complexity.

Reduced-complexity ML detection for coded MIMO systems using an absolute-value search

Spatial multiplexing of data streams on multiple transmit antennae is a well known means of increasing the data-rate in a bandwidth constrained system. At the receiver, maximum likelihood (ML) detection offers the best performance. However, for large constellations and/or large number of antennae the complexity of the decoder grows exponentially. Sphere-decoding is one suboptimal decoding method that has been proposed to reduce complexity by reducing the size of the search space for determining the optimum transmitted symbol vector. In this paper, we present an alternative approach, using an absolute-value based distance for searching, that keeps the size of the search space the same, but reduces the complexity by reducing the number of multiplications required to evaluate the quantities being compared in the search space. This method has vastly reduced complexity compared to ML. Simulation results are presented with a bit-interleaved coded modulation (BICM) system to show that this method has less than 0.5 dB performance loss as compared to ML detection.