Interference management and power scheduling in femtocell networks with the optimized power scheduling BiLSTM

Abstract

With the proliferation of femtocell networks, seamless hand-off, and efficient power management are crucial for ensuring undisturbed connectivity and the optimal utilization of resources. Certain limitations of the existing methods include limited adaptability to dynamic network conditions, suboptimal hand-off decision-making, and inefficient power allocation. To tackle the addressed drawbacks of the existing interference management, the Vocal Hunt Optimization-based Power Scheduling BiLSTM (VHO-based PS-BiLSTM) is introduced in the research. This model aims at achieving a seamless transition during the hand-off events while optimizing the power consumption, ultimately enhancing the energy efficiency of the network and maximizing the end-user quality of service. The optimized Bidirectional Long Short-term Memory (BiLSTM) achieves superior performance by acquiring the sequential dependencies in energy consumption data, effectively capturing underlying patterns, and enabling the storage and utilization of data from both past and future time steps. The BiLSTM classifier is optimized with the Vocal Hunt Optimization (VHO) to improve the efficiency of the detection process and in turn, the power scheduling is carried out by the VHO individually based on the detected hand-off. The efficiency of the research is measured in terms of accuracy, sensitivity, specificity, delay, energy, Quality of service (QoS), and throughput which achieve 96.8 %, 95.76 %, 96.25 %, 109.38 ms, 0.999 J, 0.513, and 0.487 respectively.