In this paper,we concentrate on a reconfigurable intelligent surface(RIS)-aided mobile edge computing(MEC)system to improve the offload efficiency with moving user equipments(UEs).We aim to minimize the energy consump...In this paper,we concentrate on a reconfigurable intelligent surface(RIS)-aided mobile edge computing(MEC)system to improve the offload efficiency with moving user equipments(UEs).We aim to minimize the energy consumption of all UEs by jointly optimizing the discrete phase shift of RIS,UEs’transmitting power,computing resources allocation,and the UEs’task offloading strategies for local computing and offloading.The formulated problem is a sequential decision making across multiple coherent time slots.Furthermore,the mobility of UEs brings uncertainties into the decision-making process.To cope with this challenging problem,the deep reinforcement learning-based Soft Actor-Critic(SAC)algorithm is first proposed to effectively optimize the discrete phase of RIS and the UEs’task offloading strategies.Then,the transmitting power and computing resource allocation can be determined based on the action.Numerical results demonstrate that the proposed algorithm can be trained more stably and perform approximately 14%lower than the deep deterministic policy gradient benchmark in terms of energy consumption.展开更多
The tremendous performance gain of heterogeneous networks(Het Nets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for Het Nets becomes much more challengi...The tremendous performance gain of heterogeneous networks(Het Nets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for Het Nets becomes much more challenging and this is the focus of this paper. In this paper, the eavesdropper is hidden from the macro base stations. To relax the unpractical assumption on the channel state information on eavesdropper, a localization based algorithm is first given. Then a joint resource allocation algorithm is proposed in our work, which simultaneously considers physical layer security, cross-tier interference and joint optimization of power and subcarriers under fairness requirements. It is revealed in our work that the considered optimization problem can be efficiently solved relying on convex optimization theory and the Lagrangian dual decomposition method is exploited to solve the considered problem effectively. Moreover, in each iteration the closed-form optimal resource allocation solutions can be obtained based on the Karush-Kuhn-Tucker(KKT) conditions. Finally, the simulation results are given to show the performance advantages of the proposed algorithm.展开更多
In this paper, we focus on energy-efficient transceiver and relay beamforming design for multi-pair two-way relay system. The multi-antenna users and the multi-antenna relay are considered in this work. Different from...In this paper, we focus on energy-efficient transceiver and relay beamforming design for multi-pair two-way relay system. The multi-antenna users and the multi-antenna relay are considered in this work. Different from the existing works, the proposed algorithm is energy-efficient which is more applicable to the future green network. It considers both the sum-MSE problem and the power consumption problem for the users under the relay power constraint. Based on the optimal condition decomposition(OCD) method, the energy-efficient precoders at the users can be designed separately with limited information exchanged. The proposed relay beamforming algorithm is based on the alternative direction method of multipliers(ADMM) which has simpler iterative solution and enjoys good convergence. Simulation results demonstrate the performance of the proposed algorithms in terms of power consumption and MSE performance.展开更多
In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation pr...In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation problems are jointly optimized.In order to make the resource allocation suitable for large scale networks,the optimization problem is decomposed first based on an effective decomposition algorithm named optimal condition decomposition(OCD) algorithm.Furthermore,aiming at reducing implementation complexity,the subcarriers are divided into chunks and are allocated chunk by chunk.The simulation results show that the proposed algorithm achieves more superior performance than uniform power allocation scheme and Lagrange relaxation method,and then the proposed algorithm can strike a balance between the complexity and performance of the multi-carrier Ultra-Dense Networks.展开更多
Ultra-dense networking is widely accepted as a promising enabling technology to realize high power and spectrum efficient communications in future 5G communication systems. Although joint resource allocation schemes p...Ultra-dense networking is widely accepted as a promising enabling technology to realize high power and spectrum efficient communications in future 5G communication systems. Although joint resource allocation schemes promise huge performance improvement at the cost of cooperation among base stations,the large numbers of user equipment and base station make jointly optimizing the available resource very challenging and even prohibitive. How to decompose the resource allocation problem is a critical issue. In this paper,we exploit factor graphs to design a distributed resource allocation algorithm for ultra dense networks,which consists of power allocation,subcarrier allocation and cell association. The proposed factor graph based distributed algorithm can decompose the joint optimization problem of resource allocation into a series of low complexity subproblems with much lower dimensionality,and the original optimization problem can be efficiently solved via solving these subproblems iteratively. In addition,based on the proposed algorithm the amounts of exchanging information overhead between the resulting subprob-lems are also reduced. The proposed distributed algorithm can be understood as solving largely dimensional optimization problem in a soft manner,which is much preferred in practical scenarios. Finally,the performance of the proposed low complexity distributed algorithm is evaluated by several numerical results.展开更多
With great increase of mobile service in recent years,high quality of experience(QoE) is becoming a comprehensive and major goal for service provider.To unify evaluations of different services,mean opinion score(MOS) ...With great increase of mobile service in recent years,high quality of experience(QoE) is becoming a comprehensive and major goal for service provider.To unify evaluations of different services,mean opinion score(MOS) as a subjective assessment is usually adopted for accurate and convincing reflection of user perceived quality.In this paper,we consider the effect of the burst transmission of best effort(BE) traffic on the uses with real time video traffic in the same cell.We extend the rate scaling process which was initially used to shape burstiness of BE users as interference to handle the scenario that BE users act as resource competitors with video users.A power reallocation strategy between the two types of users is presented and an algorithm further improving the fairness of BE users is proposed.The simulation results demonstrate that the proposed algorithm can not only promote the QoE of both types of users,but also guarantee the fairness among users.展开更多
基金supported by the National Natural Science Foundation of China(No.62101277 and No.U20B2039)the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu(No.BK20212001)。
文摘In this paper,we concentrate on a reconfigurable intelligent surface(RIS)-aided mobile edge computing(MEC)system to improve the offload efficiency with moving user equipments(UEs).We aim to minimize the energy consumption of all UEs by jointly optimizing the discrete phase shift of RIS,UEs’transmitting power,computing resources allocation,and the UEs’task offloading strategies for local computing and offloading.The formulated problem is a sequential decision making across multiple coherent time slots.Furthermore,the mobility of UEs brings uncertainties into the decision-making process.To cope with this challenging problem,the deep reinforcement learning-based Soft Actor-Critic(SAC)algorithm is first proposed to effectively optimize the discrete phase of RIS and the UEs’task offloading strategies.Then,the transmitting power and computing resource allocation can be determined based on the action.Numerical results demonstrate that the proposed algorithm can be trained more stably and perform approximately 14%lower than the deep deterministic policy gradient benchmark in terms of energy consumption.
基金supported by the National Natural Science Foundation of China under Grant No.61371075the 863 project SS2015AA011306
文摘The tremendous performance gain of heterogeneous networks(Het Nets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for Het Nets becomes much more challenging and this is the focus of this paper. In this paper, the eavesdropper is hidden from the macro base stations. To relax the unpractical assumption on the channel state information on eavesdropper, a localization based algorithm is first given. Then a joint resource allocation algorithm is proposed in our work, which simultaneously considers physical layer security, cross-tier interference and joint optimization of power and subcarriers under fairness requirements. It is revealed in our work that the considered optimization problem can be efficiently solved relying on convex optimization theory and the Lagrangian dual decomposition method is exploited to solve the considered problem effectively. Moreover, in each iteration the closed-form optimal resource allocation solutions can be obtained based on the Karush-Kuhn-Tucker(KKT) conditions. Finally, the simulation results are given to show the performance advantages of the proposed algorithm.
基金supported by China National S&T Major Project 2013ZX03003002-003National Natural Science Foundation of China under Grant No. 61176027, No.61421001111 Project of China under Grant B14010
文摘In this paper, we focus on energy-efficient transceiver and relay beamforming design for multi-pair two-way relay system. The multi-antenna users and the multi-antenna relay are considered in this work. Different from the existing works, the proposed algorithm is energy-efficient which is more applicable to the future green network. It considers both the sum-MSE problem and the power consumption problem for the users under the relay power constraint. Based on the optimal condition decomposition(OCD) method, the energy-efficient precoders at the users can be designed separately with limited information exchanged. The proposed relay beamforming algorithm is based on the alternative direction method of multipliers(ADMM) which has simpler iterative solution and enjoys good convergence. Simulation results demonstrate the performance of the proposed algorithms in terms of power consumption and MSE performance.
基金supported in part by Beijing Natural Science Foundation(4152047)the 863 project No.2014AA01A701+1 种基金111 Project of China under Grant B14010China Mobile Research Institute under grant[2014]451
文摘In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation problems are jointly optimized.In order to make the resource allocation suitable for large scale networks,the optimization problem is decomposed first based on an effective decomposition algorithm named optimal condition decomposition(OCD) algorithm.Furthermore,aiming at reducing implementation complexity,the subcarriers are divided into chunks and are allocated chunk by chunk.The simulation results show that the proposed algorithm achieves more superior performance than uniform power allocation scheme and Lagrange relaxation method,and then the proposed algorithm can strike a balance between the complexity and performance of the multi-carrier Ultra-Dense Networks.
基金supported by China Mobile Research Institute under grant [2014] 451National Natural Science Foundation of China under Grant No. 61176027+2 种基金Beijing Natural Science Foundation(4152047)the 863 project No.2014AA01A701111 Project of China under Grant B14010
文摘Ultra-dense networking is widely accepted as a promising enabling technology to realize high power and spectrum efficient communications in future 5G communication systems. Although joint resource allocation schemes promise huge performance improvement at the cost of cooperation among base stations,the large numbers of user equipment and base station make jointly optimizing the available resource very challenging and even prohibitive. How to decompose the resource allocation problem is a critical issue. In this paper,we exploit factor graphs to design a distributed resource allocation algorithm for ultra dense networks,which consists of power allocation,subcarrier allocation and cell association. The proposed factor graph based distributed algorithm can decompose the joint optimization problem of resource allocation into a series of low complexity subproblems with much lower dimensionality,and the original optimization problem can be efficiently solved via solving these subproblems iteratively. In addition,based on the proposed algorithm the amounts of exchanging information overhead between the resulting subprob-lems are also reduced. The proposed distributed algorithm can be understood as solving largely dimensional optimization problem in a soft manner,which is much preferred in practical scenarios. Finally,the performance of the proposed low complexity distributed algorithm is evaluated by several numerical results.
基金supported by China National S&T Major Project 2013ZX03003002003Beijing Natural Science Foundation No.4152047+1 种基金the 863 project No.2014AA01A701111 Project of China under Grant B14010
文摘With great increase of mobile service in recent years,high quality of experience(QoE) is becoming a comprehensive and major goal for service provider.To unify evaluations of different services,mean opinion score(MOS) as a subjective assessment is usually adopted for accurate and convincing reflection of user perceived quality.In this paper,we consider the effect of the burst transmission of best effort(BE) traffic on the uses with real time video traffic in the same cell.We extend the rate scaling process which was initially used to shape burstiness of BE users as interference to handle the scenario that BE users act as resource competitors with video users.A power reallocation strategy between the two types of users is presented and an algorithm further improving the fairness of BE users is proposed.The simulation results demonstrate that the proposed algorithm can not only promote the QoE of both types of users,but also guarantee the fairness among users.
