In 5G systems, massive multiple-input multiple-output (MIMO) has been adopted in base stations (BSs) to improve spectral efficiency and coverage. The traditional conductive performance test techniques are challenging ...In 5G systems, massive multiple-input multiple-output (MIMO) has been adopted in base stations (BSs) to improve spectral efficiency and coverage. The traditional conductive performance test techniques are challenging due to the unaffordable cost and high complexity when testing a large number of antennas. To solve this problem, the over-the-air (OTA) test has been presented, in which probe selection is the key to reduce the number of channel emulators and probes. In this paper, a novel artificial bee colony (ABC) algorithm is introduced to enhance the efficiency and accuracy of probe selection procedure. A sectoring- based multi-probe anechoic chamber (MPAC) is built to evaluate the throughput performance of massive MIMO equipped in 5G BS. In addition, link level simulation is carried out to evaluate the proposal’s performance gain under the commercial network assumptions, where the average throughput of three velocity is given with different SNR region. The results suggest that OTA chamber and multi-probe wall are available not only for 5G BSs, but also for user equipments (UEs) with end-to-end communication.展开更多
Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the node...Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).展开更多
基于蜂窝网的室内定位由于与通信网络共用基础设施,因此具有覆盖范围广、无需基础设施再投资等突出优点,已成为电信运营商级室内定位的首选,是5G通信领域的研究热点之一。在蜂窝网室内定位场景中,基站的布局将直接影响接收首径的数量、...基于蜂窝网的室内定位由于与通信网络共用基础设施,因此具有覆盖范围广、无需基础设施再投资等突出优点,已成为电信运营商级室内定位的首选,是5G通信领域的研究热点之一。在蜂窝网室内定位场景中,基站的布局将直接影响接收首径的数量、到达时间TOA(Time of Arrivaling)和测量误差等要素,从而影响定位精度。据此,文中提出一种面向室内定位的基站选择优化方法,以减小由于基站布局引入的误差。首先,引入TOA信息去除TDOA定位的虚定位点;其次,针对不同基站选择方案得到的定位结果,利用二次聚类的思想去除孤立点,并根据聚类结果中样本节点数量最多的类确定定位点的位置。实验结果表明,与其他优化方法相比,所提方法的室内定位平均误差降低了15.49%。展开更多
基金supported by the State Major Science and Technology Special Projects under Grant No. 2018ZX03001028-003
文摘In 5G systems, massive multiple-input multiple-output (MIMO) has been adopted in base stations (BSs) to improve spectral efficiency and coverage. The traditional conductive performance test techniques are challenging due to the unaffordable cost and high complexity when testing a large number of antennas. To solve this problem, the over-the-air (OTA) test has been presented, in which probe selection is the key to reduce the number of channel emulators and probes. In this paper, a novel artificial bee colony (ABC) algorithm is introduced to enhance the efficiency and accuracy of probe selection procedure. A sectoring- based multi-probe anechoic chamber (MPAC) is built to evaluate the throughput performance of massive MIMO equipped in 5G BS. In addition, link level simulation is carried out to evaluate the proposal’s performance gain under the commercial network assumptions, where the average throughput of three velocity is given with different SNR region. The results suggest that OTA chamber and multi-probe wall are available not only for 5G BSs, but also for user equipments (UEs) with end-to-end communication.
文摘Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).
文摘基于蜂窝网的室内定位由于与通信网络共用基础设施,因此具有覆盖范围广、无需基础设施再投资等突出优点,已成为电信运营商级室内定位的首选,是5G通信领域的研究热点之一。在蜂窝网室内定位场景中,基站的布局将直接影响接收首径的数量、到达时间TOA(Time of Arrivaling)和测量误差等要素,从而影响定位精度。据此,文中提出一种面向室内定位的基站选择优化方法,以减小由于基站布局引入的误差。首先,引入TOA信息去除TDOA定位的虚定位点;其次,针对不同基站选择方案得到的定位结果,利用二次聚类的思想去除孤立点,并根据聚类结果中样本节点数量最多的类确定定位点的位置。实验结果表明,与其他优化方法相比,所提方法的室内定位平均误差降低了15.49%。
