Systematic pruning of blind decoding results

In 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 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 to reduce 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 SNR gain of our likelihood-based pruning is shown to be around 1 dB with respect to SCM-based pruning in AWGN channel. The proposed likelihood-based approach can be applied to any error-correction/detection systems whose decoders make many blind decoding attempts.