In order to support massive Machine Type Communication(mMTC) applications in future Fifth Generation(5G) systems,a key technical challenge is to design a highly effective multiple access protocol for massive connectio...In order to support massive Machine Type Communication(mMTC) applications in future Fifth Generation(5G) systems,a key technical challenge is to design a highly effective multiple access protocol for massive connection requests and huge traffic load from all kinds of smart devices,e.g.bike,watch,phone,ring,glasses,shoes,etc..To solve this hard problem in distributed scenarios with massive competing devices,this paper proposes and evaluates a Neighbor-Aware Multiple Access(NAMA) protocol,which is scalable and adaptive to different connectivity size and traffic load.By exploiting acknowledgement signals broadcasted from the neighboring devices with successful packet transmissions,NAMA is able to turn itself from a contention-based random access protocol to become a contention-free deterministic access protocol with particular transmission schedules for all neighboring devices after a short transition period.The performance of NAMA is fully evaluated from random state to deterministic state through extensive computer simulations under different network sizes and Contention Window(CW)settings.Compared with traditional IEEE802.11 Distributed Coordination Function(DCF),for a crowded network with 50 devices,NAMA can greatly improve system throughput and energy efficiency by more than 110%and210%,respectively,while reducing average access delay by 53%in the deterministic state.展开更多
利用认知无线电非正交多址接入(cognitive radio non-orthogonal multiple access,CR-NOMA)技术可缓解频谱资源短缺问题,提升传感设备的吞吐量。传感设备的能效问题一直制约着传感设备的应用。为此,针对CR-NOMA中的传感设备,提出基于深...利用认知无线电非正交多址接入(cognitive radio non-orthogonal multiple access,CR-NOMA)技术可缓解频谱资源短缺问题,提升传感设备的吞吐量。传感设备的能效问题一直制约着传感设备的应用。为此,针对CR-NOMA中的传感设备,提出基于深度确定策略梯度的能效优化(deep deterministic policy gradientbased energy efficiency optimization,DPEE)算法。DPEE算法通过联合优化传感设备的传输功率和时隙分裂系数,提升传感设备的能效。将能效优化问题建模成马尔可夫决策过程,再利用深度确定策略梯度法求解。最后,通过仿真分析了电路功耗、时隙时长和主设备数对传感能效的影响。仿真结果表明,能效随传感设备电路功耗的增加而下降。此外,相比于基准算法,提出的DPEE算法提升了能效。展开更多
基金funded by the National Natural Science Foundation of China (Grant No.61231009)the National HighTech R&D Program of China(863)(Grant No.2014AA01A701)+5 种基金the National Science and Technology Major Project(Grant No. 2015ZX03001033-003)Ministry of Science and Technology International Cooperation Project(Grant No.2014DFE10160)the Science and Technology Commission of Shanghai Municipality(Grant No.14ZR1439600)the EU H2020 5G Wireless project(Grant No.641985)the EU FP7 QUICK project(Grant No. PIRSES-GA-2013-612652)the EPSRC TOUCAN project(Grant No.EP/L020009/1)
文摘In order to support massive Machine Type Communication(mMTC) applications in future Fifth Generation(5G) systems,a key technical challenge is to design a highly effective multiple access protocol for massive connection requests and huge traffic load from all kinds of smart devices,e.g.bike,watch,phone,ring,glasses,shoes,etc..To solve this hard problem in distributed scenarios with massive competing devices,this paper proposes and evaluates a Neighbor-Aware Multiple Access(NAMA) protocol,which is scalable and adaptive to different connectivity size and traffic load.By exploiting acknowledgement signals broadcasted from the neighboring devices with successful packet transmissions,NAMA is able to turn itself from a contention-based random access protocol to become a contention-free deterministic access protocol with particular transmission schedules for all neighboring devices after a short transition period.The performance of NAMA is fully evaluated from random state to deterministic state through extensive computer simulations under different network sizes and Contention Window(CW)settings.Compared with traditional IEEE802.11 Distributed Coordination Function(DCF),for a crowded network with 50 devices,NAMA can greatly improve system throughput and energy efficiency by more than 110%and210%,respectively,while reducing average access delay by 53%in the deterministic state.
