| Περίληψη: | The increasing number of portable devices, such as Smart phones, laptops, tablets etc, has certainly pushed up the market of wireless communication systems. These high performance devices need to comply with the strict requirements in terms of energy consumption. Battery capacity progress is relatively slower compared with the technological evolution of devices. Therefore, energy consumption issues can be solved when innovations are introduced in the processor design and architecture side.
This thesis focuses on the word-length optimization for algorithms that run on wireless receivers with multiple antennas (MIMO). The study was made for the signal’s quantization. Earlier experiments have shown that the signal conversion from floating–point to fixed–point representation requires unaffordable Bit-Error-Rate (BER) simulations that may last for hours or even for days.
During this thesis, a new divide-and-conquer method has been developed. It faces the ‘quantization problem’ hierarchically by grouping signals and exploiting the correlation between quantization error and Gaussian additive noise. BER simulations were limited by involving noise simulations. This method was successfully applied to a highly adaptive digital signal processing (DSP) algorithm, improving the total simulation time up to 84%.
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