In this paper,we study the power allocation problem in energy harvesting internet of things(IoT)communication system,with the aim to maximize the total throughput while avoiding data buffer overflow or energy exhausti...In this paper,we study the power allocation problem in energy harvesting internet of things(IoT)communication system,with the aim to maximize the total throughput while avoiding data buffer overflow or energy exhausting.The IoT node has a finite battery to store the harvested energy and a limited buffer for the storage of the unsent data.The energy/-data arrives following a Markov process.Assuming the node has no prior knowledge of the energy/data process and only knows the values of the current time slot,the optimal power allocation problem is modeled as a reinforcement learning task.The state consists of the data in the buffer,the energy stored in the battery,the new coming data amount,the energy harvesting amount and the channel coefficient at time slot t.Then the action is defined as the selected transmitting power.With the growth of the state or action space,it is challenging to visit every state-action pair sufficiently and store all the state-action values,so a deep Q-learning based algorithm is proposed to solve this problem.Simulation results show the advantages of our proposed algorithms,and we also analyze the effect of different system setting parameters.展开更多
In order to establish a route supporting multi-constrained quality of service（QoS）, increase network throughput and reduce network energy consumption, an improved ant colony-based multi-constrained QoS energy-saving...In order to establish a route supporting multi-constrained quality of service（QoS）, increase network throughput and reduce network energy consumption, an improved ant colony-based multi-constrained QoS energy-saving routing algorithm（IAMQER） is proposed. The ant colony algorithm, as one of the available heuristic algorithms, is used to find the optimal route from source node to destination node. The proposed IAMQER algorithm, which is based on the analysis of local node information such as node queue length, node forwarding number of data packets and node residual energy, balances the relationship between the network throughput and the energy consumption, thus improving the performance of network in multi-constrained QoS routing. Simulation results show that this IAMQER algorithm can find the QoS route that reduce average energy consumption and improves network packet delivery ratio under the end-to-end delay and packet loss ratio constraints.展开更多
Wireless networks are developed under the fashion of wider spectrum utilization （e.g., cognitive radio） and multi-hop communication （e.g., wireless mesh networks）. In these paradigms, how to effectively allocate t...Wireless networks are developed under the fashion of wider spectrum utilization （e.g., cognitive radio） and multi-hop communication （e.g., wireless mesh networks）. In these paradigms, how to effectively allocate the spectrum to different transmission links with minimized mutual interference becomes the key concern. In this paper, we study the throughput optimization via spectrum allocation in cognitive radio networks （CRNs）. The previous studies incorporate either the conflict graph or SINR model to characterize the interference relationship. However, the former model neglects the accumulative interference effect and leads to unwanted interference and sub-optimal results, while the work based on the latter model neglects its heavy reliance on the accuracy of estimated RSS （receiving signal strength） among all potential links. Both are inadequate to characterize the complex relationship between interference and throughput. To this end, by considering the feature of CRs, like spectrum diversity and non-continuous OFDM, we propose a measurement-assisted SINR-based cross-layer throughput optimization solution. Our work concerns features in different layers： in the physical layer, we present an efficient RSS estimation algorithm to improve the accuracy of the SINR model; in the upper layer, a flow level SINR-based throughput optimization problem for WMNs is modelled as a mixed integer non-linear programming problem which is proved to be NP-hard. To solve this problem, a centralized （1 -ε）-optimal algorithm and an efficient distributed algorithm are provided. To evaluate the algorithm performance, the real-world traces are used to illustrate the effectiveness of our scheme.展开更多
In order to improve the throughput of cognitive radio(CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple ...In order to improve the throughput of cognitive radio(CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple users and one fusion center. The frame structure of cooperative spectrum sensing was divided into multiple transmission time slots and one sensing time slot consisting of local energy detection and cooperative overhead. An optimization problem was formulated to maximize the throughput of CR network, subject to the constraints of both false alarm probability and detection probability. A joint optimization algorithm of sensing time and number of users was proposed to solve this optimization problem with low time complexity. An allocation algorithm of cooperative users was proposed to preferentially allocate the users to the channels with high utilization probability. The simulation results show that the significant improvement on the throughput can be achieved through the proposed joint optimization and allocation algorithms.展开更多
Device-to-Device(D2D) communication has been proposed to facilitate cellular network with system capacity(SC) and quality of service(QoS).We consider the design of link assignment(LA),channel allocation(CA)and power c...Device-to-Device(D2D) communication has been proposed to facilitate cellular network with system capacity(SC) and quality of service(QoS).We consider the design of link assignment(LA),channel allocation(CA)and power control(PC) in D2D-aided content delivery scenario for both user fairness(UF)and system throughput(ST) under QoS requirement.Due to the complexity of the problem,we decompose it into two components:CA is formulated from graph perspective to mitigate severe co-channel interference,which turns out to be the Max K-cut problem;LA and PC are jointly optimized to utilize the gain achieved from CA for supreme performance,and specifically,genetic algorithm(GA) is adopted to optimize LA,but when deriving the fitness of each chromosome,PC optimization will be involved.Thanks to numerical results,we elucidate the efficacy of our scheme.展开更多
The beyond fifth-generation Internet of Things requires more capable channel coding schemes to achieve high-reliability,low-complexity and lowlatency communications.The theoretical analysis of error-correction perform...The beyond fifth-generation Internet of Things requires more capable channel coding schemes to achieve high-reliability,low-complexity and lowlatency communications.The theoretical analysis of error-correction performance of channel coding functions as a significant way of optimizing the transmission reliability and efficiency.In this paper,the efficient estimation methods of the block error rate(BLER)performance for rate-compatible polar codes(RCPC)are proposed under several scenarios.Firstly,the BLER performance of RCPC is generally evaluated in the additive white Gaussian noise channels.That is further extended into the Rayleigh fading channel case using an equivalent estimation method.Moreover,with respect to the powerful decoder such as successive cancellation list decoding,the performance estimation is derived analytically based on the polar weight spectrum and BLER upper bounds.Theoretical evaluation and numerical simulation results show that the estimated performance can fit well the practical simulated results of RCPC under the objective conditions,verifying the validity of our proposed performance estimation methods.Furthermore,the application designs of the reliability estimation of RCPC are explored,particularly in the advantages of the signal-to-noise(SNR)estimation and throughput efficiency optimization of polar coded hybrid automatic repeat request.展开更多
In this paper,we study an unmanned aerial vehicle(UAV)-assisted communication system,where the UAV is dispatched to implement simultaneous transmission and reception(STR)in the existence of multiple malicious jammers....In this paper,we study an unmanned aerial vehicle(UAV)-assisted communication system,where the UAV is dispatched to implement simultaneous transmission and reception(STR)in the existence of multiple malicious jammers.Two schemes are investigated,namely frequency band-division-duplex(FDD)and time-fraction(TF).Based on the FDD scheme,the UAV can transmit information by using the portion of the bandwidth and receive information within the remaining portion of the bandwidth simultaneously.To perform the STR within the whole bandwidth,the TF-based scheme is considered by using a fraction of a time slot for the downlink,while the remaining fraction of the time slot is allocated for the uplink.We aim to maximize the worst-case throughput by optimizing the UAV three-dimensional(3D)trajectory and resource allocation for each scheme.The optimization problem is non-convex and thus computationally intractable.To handle the nonlinear problem,we use the block coordinate decomposition method to disaggregate the optimization problem into four subproblems and adopt the successive convex approximation technique to tackle non-convex problems.The simulation results demonstrate the performance of the TF-based scheme over the benchmark schemes.展开更多
文摘In this paper,we study the power allocation problem in energy harvesting internet of things(IoT)communication system,with the aim to maximize the total throughput while avoiding data buffer overflow or energy exhausting.The IoT node has a finite battery to store the harvested energy and a limited buffer for the storage of the unsent data.The energy/-data arrives following a Markov process.Assuming the node has no prior knowledge of the energy/data process and only knows the values of the current time slot,the optimal power allocation problem is modeled as a reinforcement learning task.The state consists of the data in the buffer,the energy stored in the battery,the new coming data amount,the energy harvesting amount and the channel coefficient at time slot t.Then the action is defined as the selected transmitting power.With the growth of the state or action space,it is challenging to visit every state-action pair sufficiently and store all the state-action values,so a deep Q-learning based algorithm is proposed to solve this problem.Simulation results show the advantages of our proposed algorithms,and we also analyze the effect of different system setting parameters.
基金supported by the National Natural Science Foundation of China(61101107)the Beijing Higher Education Young Elite Teacher Project
文摘In order to establish a route supporting multi-constrained quality of service（QoS）, increase network throughput and reduce network energy consumption, an improved ant colony-based multi-constrained QoS energy-saving routing algorithm（IAMQER） is proposed. The ant colony algorithm, as one of the available heuristic algorithms, is used to find the optimal route from source node to destination node. The proposed IAMQER algorithm, which is based on the analysis of local node information such as node queue length, node forwarding number of data packets and node residual energy, balances the relationship between the network throughput and the energy consumption, thus improving the performance of network in multi-constrained QoS routing. Simulation results show that this IAMQER algorithm can find the QoS route that reduce average energy consumption and improves network packet delivery ratio under the end-to-end delay and packet loss ratio constraints.
基金This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61373128, 91218302, 61321491, the Fundamental Research Funds for the Central Universities of China under Grant No. 20620140509, the EU FP7 IRSES MobileCloud Project under Grant No. 612212, and the Collaborative Innovation Center of Novel Software Technology and Industrialization of China.
文摘Wireless networks are developed under the fashion of wider spectrum utilization （e.g., cognitive radio） and multi-hop communication （e.g., wireless mesh networks）. In these paradigms, how to effectively allocate the spectrum to different transmission links with minimized mutual interference becomes the key concern. In this paper, we study the throughput optimization via spectrum allocation in cognitive radio networks （CRNs）. The previous studies incorporate either the conflict graph or SINR model to characterize the interference relationship. However, the former model neglects the accumulative interference effect and leads to unwanted interference and sub-optimal results, while the work based on the latter model neglects its heavy reliance on the accuracy of estimated RSS （receiving signal strength） among all potential links. Both are inadequate to characterize the complex relationship between interference and throughput. To this end, by considering the feature of CRs, like spectrum diversity and non-continuous OFDM, we propose a measurement-assisted SINR-based cross-layer throughput optimization solution. Our work concerns features in different layers： in the physical layer, we present an efficient RSS estimation algorithm to improve the accuracy of the SINR model; in the upper layer, a flow level SINR-based throughput optimization problem for WMNs is modelled as a mixed integer non-linear programming problem which is proved to be NP-hard. To solve this problem, a centralized （1 -ε）-optimal algorithm and an efficient distributed algorithm are provided. To evaluate the algorithm performance, the real-world traces are used to illustrate the effectiveness of our scheme.
基金Project(61471194)supported by the National Natural Science Foundation of ChinaProject(BK20140828)supported by the Natural Science Foundation of Jiangsu Province,ChinaProject supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars,Ministry of Education,China
文摘In order to improve the throughput of cognitive radio(CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple users and one fusion center. The frame structure of cooperative spectrum sensing was divided into multiple transmission time slots and one sensing time slot consisting of local energy detection and cooperative overhead. An optimization problem was formulated to maximize the throughput of CR network, subject to the constraints of both false alarm probability and detection probability. A joint optimization algorithm of sensing time and number of users was proposed to solve this optimization problem with low time complexity. An allocation algorithm of cooperative users was proposed to preferentially allocate the users to the channels with high utilization probability. The simulation results show that the significant improvement on the throughput can be achieved through the proposed joint optimization and allocation algorithms.
基金supported by the National 863 projects of China(2014AA01A706)
文摘Device-to-Device(D2D) communication has been proposed to facilitate cellular network with system capacity(SC) and quality of service(QoS).We consider the design of link assignment(LA),channel allocation(CA)and power control(PC) in D2D-aided content delivery scenario for both user fairness(UF)and system throughput(ST) under QoS requirement.Due to the complexity of the problem,we decompose it into two components:CA is formulated from graph perspective to mitigate severe co-channel interference,which turns out to be the Max K-cut problem;LA and PC are jointly optimized to utilize the gain achieved from CA for supreme performance,and specifically,genetic algorithm(GA) is adopted to optimize LA,but when deriving the fitness of each chromosome,PC optimization will be involved.Thanks to numerical results,we elucidate the efficacy of our scheme.
基金supported by National Natural Science Foundation of China(No.62201596)Research Planning Project of National University of Defense Technology(ZK22-45).
文摘The beyond fifth-generation Internet of Things requires more capable channel coding schemes to achieve high-reliability,low-complexity and lowlatency communications.The theoretical analysis of error-correction performance of channel coding functions as a significant way of optimizing the transmission reliability and efficiency.In this paper,the efficient estimation methods of the block error rate(BLER)performance for rate-compatible polar codes(RCPC)are proposed under several scenarios.Firstly,the BLER performance of RCPC is generally evaluated in the additive white Gaussian noise channels.That is further extended into the Rayleigh fading channel case using an equivalent estimation method.Moreover,with respect to the powerful decoder such as successive cancellation list decoding,the performance estimation is derived analytically based on the polar weight spectrum and BLER upper bounds.Theoretical evaluation and numerical simulation results show that the estimated performance can fit well the practical simulated results of RCPC under the objective conditions,verifying the validity of our proposed performance estimation methods.Furthermore,the application designs of the reliability estimation of RCPC are explored,particularly in the advantages of the signal-to-noise(SNR)estimation and throughput efficiency optimization of polar coded hybrid automatic repeat request.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 61901254in part by the Aeronautical Science Foundation of China under Grant 2020Z0660S6001
文摘In this paper,we study an unmanned aerial vehicle(UAV)-assisted communication system,where the UAV is dispatched to implement simultaneous transmission and reception(STR)in the existence of multiple malicious jammers.Two schemes are investigated,namely frequency band-division-duplex(FDD)and time-fraction(TF).Based on the FDD scheme,the UAV can transmit information by using the portion of the bandwidth and receive information within the remaining portion of the bandwidth simultaneously.To perform the STR within the whole bandwidth,the TF-based scheme is considered by using a fraction of a time slot for the downlink,while the remaining fraction of the time slot is allocated for the uplink.We aim to maximize the worst-case throughput by optimizing the UAV three-dimensional(3D)trajectory and resource allocation for each scheme.The optimization problem is non-convex and thus computationally intractable.To handle the nonlinear problem,we use the block coordinate decomposition method to disaggregate the optimization problem into four subproblems and adopt the successive convex approximation technique to tackle non-convex problems.The simulation results demonstrate the performance of the TF-based scheme over the benchmark schemes.
