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For further complexity reduction, k=d.
For further complexity reduction, two different approximation methods are presented.
This leads to more parallel VLSI design with further complexity reduction.
Moreover, the structures of R11 and R22 enable the independent detections of real and imaginary parts of (s1,s2) and (s3,s4), which leads to further complexity reduction.
Based on the proposed SO-THP+GMI algorithm, further complexity reduction can be achieved by SO-THP+S-GMI and the complexity is about 34.4 % less than that of the conventional SO-THP algorithm.
Thus, we formulate a greedy optimisation method for computing a close approximation to the optimal subset at lower complexity, which can be implemented in a decentralised way, for further complexity reduction.
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Other approaches for further complexity reductions under such premises are also available in the literature.
However, despite the fact that these methods enrich for low-copy sequences, for economical reasons further genome complexity reduction is required to engage in comparative sequencing.
With the availability of either the full or partial channel state information (CSI) at the transmitter, we can achieve further performance gain or receiver complexity reduction.
Any computational complexity reduction mechanism can further reduce the computational cost and power consumption.
Further development on SO-THP+GMI with complexity reduction leads to a novel SO-THP+S-GMI algorithm.
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