Quantum particle swarm evolutionary algorithm with application to system identification
Based on quantum evolutionary algorithm and particle swarm optimization, a quantum particle swarm evolutionary algorithm is proposed. In this algorithm, quantum angle is used to represent the qubit, a new method learning from the idea of particle swarm algorithm is presented to determine rotation angle, He gate is taken to prevent from premature convergence. Applying this algorithm to identify system parameter, and comparing with conventional genetic algorithm and quantum evolutionary algorithm, the experimental results illustrate that the proposed algorithm has better performance than that of others. Meanwhile, it can also keep high identification ability to the system with the existence of noise.
The design of the LTE physical layer (PHY) is heavily influenced by the requirements for high peak transmission rate (100 Mbps DL/50 Mbps UL), spectral efficiency and multiple channel bandwidths (1.25–20 MHz). Unfortunately, the existing WCDMA turbo interleaver does not permit an efficient high throughput implementation. That is why the 3rd Generation Partnership Project (3GPP) has proposed the quadratic polynomial permutation (QPP) interleaver to rectify this situation. In this paper, we will study the performances of QPP interleaver for LTE encoder channel for different configurations of LTE system and we study the effect of LTE channel turbo encoder for the transmission over a wireless multipath channel.