To gain improved performance in multiple issue superscalar processors, the increment in instruction fetch and issue rate is pretty necessary. Evasion of control hazard is a primary source to get peak instruction level parallelism in superscalar processors. Conditional branch prediction can help in improving the performance of processors only when these predictors are equipped with algorithms to give higher accuracy. The Increment in single miss-prediction rate can cause wastage of more than 20% of the instructions cycles, which leads us to an exploration of new techniques and algorithms that increase the accuracy of branch prediction. Alloying is a way to exploit the local and global history of different predictors in the same structure and sometimes also called hybrid branch prediction. In this paper, we aim to design a more accurate and robust two-level alloyed predictor, whose behavior is more dynamic on changing branch direction.

Spectrum sensing is an important task in cognitive radio (CR). Matched filter technique one of the techniques employed for spectrum sensing in cognitive radio which faces the challenge of low frequency offset tolerance in very low SNR environments. Hybrid matched filter architecture is used to improve this frequency offset tolerance. But, overall even such kind of architecture results in coarse detection. In very low SNR environments, where primary user is highly mobile, the multipath profile results in unknown phase of signal. Such kind of signal cannot even be detected by Hybrid Matched Filter. In this paper we propose combination of Hybrid Matched Filter and Single Cycle Cyclostationary Feature Detector to enhance the detection of such architecture. This results in both high frequency offset tolerance as well as fine detection of signal with unknown phase in very low SNR environments. Simulation results show significant improvement in the probability of detection and false alarm of the proposed scheme over Hybrid Matched Filter.