Aiming at the problem of tremendous escalation of energy consumption caused by the dense deployment of micro base stations in heterogeneous cellular networks, the energy efficiency of two-tier heterogeneous cellular networks was analyzed and a new method for maximizing network energy efficiency by adjusting the micro base station transmit power was proposed. Firstly, the heterogeneous cellular network was modeled by using homogeneous Poisson point process, and the coverage probability of base stations at each tier was derived. Secondly, according to the definition of energy efficiency, the total power consumption and total throughput of network were derived respectively, and the closed-form expression of energy efficiency was given. Finally, the impact of the micro base station transmission power on the energy efficiency of network was analyzed, and a micro base station power optimization algorithm was proposed to maximize energy efficiency. The simulation results show that, the transmission power of micro base station has a significant impact on the energy efficiency of heterogeneous cellular network. Furthermore, the energy efficiency of heterogeneous cellular network can be effectively improved by optimizing the transmission power of micro base stations.

According to the poor applicability and poor prediction accuracy fluctuation of the existing Software Reliability Growth Model (SRGM), this paper proposed a model based on Fuzzy Neural Network (FNN) which was connected with self-Adaptive Step Cuckoo Search (ASCS) algorithm, the weights and thresholds of the FNN were optimized by ASCS algorithm, then the FNN was used to establish SRGM. Software defect data were used in the FNNs training process, the weights and thresholds of FNN were adjusted by ASCS, the accuracy of prediction process was improved correspondingly, at the same time, in order to reduce the fluctuation of prediction by FNN, averaging method was used to deal with predicted results. Based on those, SRGM was established by self-Adaptive Step Cuckoo Search algorithm—Fuzzy Neural Network (ASCS-FNN). According to 3 groups of software defect data, taking Average Error (AE) and Sum of Squared Error (SSE) as measurements, the SRGMs one-step forward predictive ability established by ASCS-FNN was compared with the SRGMs one-step forward predictive ability established by Simulated Annealing—Dynamic Fuzzy Neural Network (SA-DFNN), FNN and Back Propagation Network (BPN). The simulation results confirm that, the SRGM based on ASCS-FNN relative to the SRGM based on SA-DFNN, FNN and BPN, the mean of Relative Increase (RI) of prediction accuracy rate for RI (AE) is -1.48%, 54.8%, 33.8%, and the mean of Relative Increase (RI) of prediction accuracy rate for RI (SSE) is 14.4%, 76%, 35.9%. The prediction of SRGM established by ASCS-FNN is more steadily than the prediction of SRGM established by FNN and BPN, and the net structure of ASCS-FNN is much simpler than the net structure of SA-DFNN, so the SRGM established by ASCS-FNN has high prediction accuracy, prediction stability, and some adaptability.