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
Physical-layer security issues in wireless systems have attracted great attention.In this paper,we investigate the spectrum anti-jamming(AJ)problem for data transmissions between devices.Considering fast-changing phys...Physical-layer security issues in wireless systems have attracted great attention.In this paper,we investigate the spectrum anti-jamming(AJ)problem for data transmissions between devices.Considering fast-changing physical-layer jamming attacks in the time/frequency domain,frequency resources have to be configured for devices in advance with unknown jamming patterns(i.e.the time-frequency distribution of the jamming signals)to avoid jamming signals emitted by malicious devices.This process can be formulated as a Markov decision process and solved by reinforcement learning(RL).Unfortunately,stateof-the-art RL methods may put pressure on the system which has limited computing resources.As a result,we propose a novel RL,by integrating the asynchronous advantage actor-critic(A3C)approach with the kernel method to learn a flexible frequency pre-configuration policy.Moreover,in the presence of time-varying jamming patterns,the traditional AJ strategy can not adapt to the dynamic interference strategy.To handle this issue,we design a kernelbased feature transfer learning method to adjust the structure of the policy function online.Simulation results reveal that our proposed approach can significantly outperform various baselines,in terms of the average normalized throughput and the convergence speed of policy learning.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.
基金partially supported by the National Natural Science Foundation of China under Grant U2001210,61901216,61827801the Natural Science Foundation of Jiangsu Province under Grant BK20190400。
文摘Physical-layer security issues in wireless systems have attracted great attention.In this paper,we investigate the spectrum anti-jamming(AJ)problem for data transmissions between devices.Considering fast-changing physical-layer jamming attacks in the time/frequency domain,frequency resources have to be configured for devices in advance with unknown jamming patterns(i.e.the time-frequency distribution of the jamming signals)to avoid jamming signals emitted by malicious devices.This process can be formulated as a Markov decision process and solved by reinforcement learning(RL).Unfortunately,stateof-the-art RL methods may put pressure on the system which has limited computing resources.As a result,we propose a novel RL,by integrating the asynchronous advantage actor-critic(A3C)approach with the kernel method to learn a flexible frequency pre-configuration policy.Moreover,in the presence of time-varying jamming patterns,the traditional AJ strategy can not adapt to the dynamic interference strategy.To handle this issue,we design a kernelbased feature transfer learning method to adjust the structure of the policy function online.Simulation results reveal that our proposed approach can significantly outperform various baselines,in terms of the average normalized throughput and the convergence speed of policy learning.
