This study proposes a soft ordered successive interference cancellation (OSIC) receiver accompanied by a simplified log-likelihood ratio calculation method for a dual-polarised multiple-input multiple-output (MIMO) di...
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This study proposes a soft ordered successive interference cancellation (OSIC) receiver accompanied by a simplified log-likelihood ratio calculation method for a dual-polarised multiple-input multiple-output (MIMO) digital video broadcasting-second generation terrestrial system. The future terrestrial transmission systems will be required to provide high data rates for ultra-high definition television service. For this purpose, dual-polarised MIMO techniques with a little cross-antenna interference and high-order modulation schemes have been considered. As the cross-antenna interference needs to be removed, a soft OSIC receiver can be used effectively. However, the receiver has to endure high computational complexity for calculating soft values. To reduce it, the authors simplified the calculation based on the decision threshold (DT) method. The main contribution of this study is to extend the conventional DT method, which is restricted to only low-order modulations, to an algorithmic process applicable to any modulation order. The proposed soft OSIC receiver has about 1 dB degradation from the soft OSIC receiver with the maximum-likelihood calculation of soft values. The degradation can be fully rewarded by the significantly reduced computational complexity. Furthermore, this study presents an additional possibility of improving the decoding performance by modelling the OSIC equaliser as a biased estimator.
In Darwin's Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett's analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algori...
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In Darwin's Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett's analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented as algorithms by physical processes. For that to be true, the processes have to be part of computational systems. The basic difference between mere simulation and real computing is having proper causal structure. I will show what kind of requirements this poses for natural evolutionary processes if they are to be computational.
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