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Near-Optimal Contraction of Voronoi Regions for Pruning of Blind Decoding Results

In Long-Term Evolution (LTE) downlink control channel, a large number of blind decoding attempts are made, while the number of valid codewords is limited. The blind decoding results are then verified using a 16-bit cyclic redundancy check (CRC). However, even with the 16-bit CRC, the false alarm (FA) rate of such blind decoding is inevitably high. This paper investigates the problem of pruning of blind decoding results for reduction of the FA rate. To the best of our knowledge, the approach using a soft correlation metric (SCM) shows the best FA reduction performance among existing schemes. However, following the Bayes principle, we propose novel likelihood-based pruning that provides systematic balancing between the FA rate and the miss (MS) rate. Moreover, the simulation results show that the signal-to-noise ratio (SNR) gain of our proposed scheme is unbounded, with respect to the SCM-based scheme, in the independent and identically distributed (i.i.d.) Rayleigh fading channel. Moreover, the proposed scheme is shown to be less complex than the existing scheme. Finally, it is proved that, as SNR increases, the proposed approach has the decision error probability that approaches the minimum value yielding near-optimal contraction of Voronoi regions for pruning of blind decoding results.