Conventional approach of dealing with more users per coverage area in cellular networks implies densifying the amount of(Access Point)AP which will eventually result in a larger carbon footprint.In this paper,we propo...Conventional approach of dealing with more users per coverage area in cellular networks implies densifying the amount of(Access Point)AP which will eventually result in a larger carbon footprint.In this paper,we propose a base station off-loading and cell range extension(CRE)scheme based on multi-hop device-to-device(MHD2D)path selection between transmitter and receiver node.The paper also provides derivations of upper and lower bounds for energy efficiency,capacity,and transmit power.The proposed path selection scheme is inspired by the foraging behavior of honey bees.We present the algorithm as a modified variant of the artificial bee colony algorithm(MVABC).The proposed optimization problem is modeled as a minimization problem where we optimize the Energy Efficiency(EE).The proposed path selection MVABC is compared with the Genetic Algorithm(GA)and also with classical artificial bee colony(ABC)through simulations and statistical analysis.The student’s t-test,p-value,and standard error of means(SEM)clearly show that MVABC based path selection out-performs the GA and classical ABC schemes.MVABC based approach is 66%more efficient when compared with classic ABC and about 62%efficient when compared with GA based scheme.展开更多
文摘Conventional approach of dealing with more users per coverage area in cellular networks implies densifying the amount of(Access Point)AP which will eventually result in a larger carbon footprint.In this paper,we propose a base station off-loading and cell range extension(CRE)scheme based on multi-hop device-to-device(MHD2D)path selection between transmitter and receiver node.The paper also provides derivations of upper and lower bounds for energy efficiency,capacity,and transmit power.The proposed path selection scheme is inspired by the foraging behavior of honey bees.We present the algorithm as a modified variant of the artificial bee colony algorithm(MVABC).The proposed optimization problem is modeled as a minimization problem where we optimize the Energy Efficiency(EE).The proposed path selection MVABC is compared with the Genetic Algorithm(GA)and also with classical artificial bee colony(ABC)through simulations and statistical analysis.The student’s t-test,p-value,and standard error of means(SEM)clearly show that MVABC based path selection out-performs the GA and classical ABC schemes.MVABC based approach is 66%more efficient when compared with classic ABC and about 62%efficient when compared with GA based scheme.
