Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Ambient Backscatter Communications over NOMA Downlink Channels

In this paper,we investigate the performance of commensal ambient backscatter communications(AmBC)that ride on a non-ortho go nal multiple access(NOMA)downlink transmission,in which a backscatter device(BD)splits part of its received signals from the base station(BS)for energy harvesting,and backscatters the remaining received signals to transmit information to a cellular user.Specifically,under the power consumption constraint at BD and the peak transmit power constraint at BS,we derive the optimal reflection coefficient at BD,the optimal total transmit power at BS,and the optimal power allocation at BS for each transmission block to maximize the ergodic capacity of the ambient backscatter transmission on the premise of preserving the outage performance of the NOMA downlink transmission.Furthermore,we consider a scenario where the BS is restricted by a maximum allowed average transmit power and the reflection coefficient at BD is fixed due to BD’s low-complexity nature.An algorithm is developed to determine the optimal total transmit power and power allocation at BS for this scenario.Also,a low-complexity algorithm is proposed for this scenario to reduce the computational complexity and the signaling overheads.Finally,the performance of the derived solutions are studied and compared via numerical simulations.