Cell discontinuous transmission(Cell DTx)is a key technology to mitigate inter-cell interference(ICI)in ultra-dense networks(UDNs).The aim of this work is to understand the impact of Cell DTx on physical-layer sum rat...Cell discontinuous transmission(Cell DTx)is a key technology to mitigate inter-cell interference(ICI)in ultra-dense networks(UDNs).The aim of this work is to understand the impact of Cell DTx on physical-layer sum rates of SBSs and link-layer quality-of-service(QoS)performance in multiuser UDNs.In this work,we develop a cross-layer framework for capacity analysis in multiuser UDNs with Cell DTx.In particular,we first extend the traditional one-dimensional effective capacity model to a new multidimensional effective capacity model to derive the sum rate and the effective capacity.Moreover,we propose a new iterative bisection search algorithm that is capable of approximating QoS performance.The convergence of this new algorithm to a unique QoS exponent vector is later proved.Finally,we apply this framework to the round-robin and the max-C/I scheduling policies.Simulation results show that our framework is accurate in approximating 1)queue length distribution,2)delay distribution and 3)sum rates under the above two scheduling policies,and further show that with the Cell DTx,systems have approximately 30% higher sum rate and 35% smaller average delay than those in full-buffer scenarios.展开更多
With the ever-growing number of base stations(BSs)and user equipments(UEs)in ultra-dense networks(UDN),reusing the same pilot sequences among the cells is inevitable.With pilot reuse scheme,the channel estimation obta...With the ever-growing number of base stations(BSs)and user equipments(UEs)in ultra-dense networks(UDN),reusing the same pilot sequences among the cells is inevitable.With pilot reuse scheme,the channel estimation obtained at a BS contains not only the desired channel-state information(CSI)but also interference from neighboring cells,which can severely degrade CSI estimation performance and adversely affect communication performance.In this paper we consider a pilot contamination avoidance based on pilot pattern design for UDN where the pilot reuse employed and the interfering users from neighboring cells may be not at lower power levels at the BS compared to the in-cell users.We present a novel statistical interference model of sub-carriers to describe the non-deterministic interference from neighboring cells.Then,we provide a pilot pattern design model with non-uniform pilot distribution.Based on this,a pilot contamination avoidance based on pilot pattern design is proposed where pilot reuse scheme and the non-deterministic interference from neighboring cells are taken into consideration.Unlike existing interference mitigation approaches,the proposed method eliminates interference through the method of interference avoidance and can be applied to different kinds of channel estimation algorithms.Simulation results showed that the proposed approach can effectively avoid the interference and ensure the accuracy of channel estimation.展开更多
Mobile-edge computing(MEC),enabling to offload computing tasks on mobile devices towards edge servers,can reduce the terminals cost.However,a single MEC sever usually has limited computing capabilities,which can not m...Mobile-edge computing(MEC),enabling to offload computing tasks on mobile devices towards edge servers,can reduce the terminals cost.However,a single MEC sever usually has limited computing capabilities,which can not meet a large number of terminals’requirements.In this paper,we consider an ultra-dense networks(UDN)scenario where the macro base stations(MBSs)are assisted by MEC severs.In particular,we first construct system model for MEC assisted UDN,and build the system overhead minimization.Next,in order to solve the problem,we transform the problem into three sub-problems,i.e.,offloading strategies subproblem,channel assignments subproblem,and power allocation subproblem.Then,employing joint offloading and resource allocation algorithms,we obtain the optimal joint strategy for the MEC assisted UDNs.Finally,simulations are conducted to evaluate the performance of our proposed algorithms.Numerical results show that obtained algorithms can effectively reduce the energy consumption of the system and improve the overall performance of the system.展开更多
Pushing popular contents to the edge of the network can meet the growing demand for data traffic, reduce latency and relieve the pressure of the backhaul. However, considering the limited storage space of the base sta...Pushing popular contents to the edge of the network can meet the growing demand for data traffic, reduce latency and relieve the pressure of the backhaul. However, considering the limited storage space of the base stations, it is impossible to cache all the contents, especially in ultra-dense network(UDN). Furthermore, the uneven distribution of mobile users results in load imbalance among small base stations(SBSs) in both time and space, which also affects the caching strategy. To overcome these shortcoming, the impact of the changing load imbalance in UDN was investigated, and then a dynamic hierarchical collaborative caching(DHCC) scheme was proposed to optimize latency and caching hit rate. The storage of the SBS is logically divided into the independent caching layer and the collaborative caching layer. The independent caching layer caches the most popular contents for local users’ interest, and the collaborative caching layer caches contents as much as possible for the benefit of content diversity in the region. Different SBSs have respective storage space layer division ratios, according to their real-time traffic load. For SBSs with heavy load, the independent caching layers are allocated with more space. Otherwise, the collaborative caching layers could store more contents with larger space. The simulation results show that, DHCC improved both transmission latency and hit rate compared with existing caching schemes.展开更多
To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks(UDNs)and mobile edge computing(MEC)are considered as important solutions.In the...To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks(UDNs)and mobile edge computing(MEC)are considered as important solutions.In the MEC enabled UDNs,one of the most important issues is computation offloading.Although a number of work have been done toward this issue,the problem of dynamic computation offloading in time-varying environment,especially the dynamic computation offloading problem for multi-user,has not been fully considered.Therefore,in order to fill this gap,the dynamic computation offloading problem in time-varying environment for multi-user is considered in this paper.By considering the dynamic changes of channel state and users’queue state,the dynamic computation offloading problem for multi-user is formulated as a stochastic game,which aims to optimize the delay and packet loss rate of users.To find the optimal solution of the formulated optimization problem,Nash Q-learning(NQLN)algorithm is proposed which can be quickly converged to a Nash equilibrium solution.Finally,extensive simulation results are presented to demonstrate the superiority of NQLN algorithm.It is shown that NQLN algorithm has better optimization performance than the benchmark schemes.展开更多
In order to meet the emerging requirements for high computational complexity, low delay and energy consumption of the 5 th generation wireless systems(5 G) network, ultra-dense networks(UDNs) combined with multi-acces...In order to meet the emerging requirements for high computational complexity, low delay and energy consumption of the 5 th generation wireless systems(5 G) network, ultra-dense networks(UDNs) combined with multi-access edge computing(MEC) can further improve network capacity and computing capability. In addition, the integration of green energy can effectively reduce the on-grid energy consumption of system and realize green computation. This paper studies the joint optimization of user association(UA) and resource allocation(RA) in MEC enabled UDNs under the green energy supply pattern, users need to perceive the green energy status of base stations(BSs) and choose the one with abundant resources to associate. To minimize the computation cost for all users, the optimization problem is formulated as a mixed integer nonlinear programming(MINLP) which is NP-hard. In order to solve the problem, a deep reinforcement learning(DRL)-based association and optimized allocation(DAOA) scheme is designed to solve it in two stages. The simulation results show that the proposed scheme has good performance in terms of computation cost and time out ratio, as well achieve load balancing potentially.展开更多
文摘Cell discontinuous transmission(Cell DTx)is a key technology to mitigate inter-cell interference(ICI)in ultra-dense networks(UDNs).The aim of this work is to understand the impact of Cell DTx on physical-layer sum rates of SBSs and link-layer quality-of-service(QoS)performance in multiuser UDNs.In this work,we develop a cross-layer framework for capacity analysis in multiuser UDNs with Cell DTx.In particular,we first extend the traditional one-dimensional effective capacity model to a new multidimensional effective capacity model to derive the sum rate and the effective capacity.Moreover,we propose a new iterative bisection search algorithm that is capable of approximating QoS performance.The convergence of this new algorithm to a unique QoS exponent vector is later proved.Finally,we apply this framework to the round-robin and the max-C/I scheduling policies.Simulation results show that our framework is accurate in approximating 1)queue length distribution,2)delay distribution and 3)sum rates under the above two scheduling policies,and further show that with the Cell DTx,systems have approximately 30% higher sum rate and 35% smaller average delay than those in full-buffer scenarios.
基金This work was supported in part by the Chongqing Research Program of Basic Research and Frontier Technology under Grant cstc2019jcyj-msxmX0233in part by Science and Technology Research Program of Chongqing Education Commission of China under Grant KJQN201901125,Grant KJQN201901103in part by the Scientific Research Foundation of Chongqing University of Technology under Grant 2019ZD42,Grant 2019ZD63.
文摘With the ever-growing number of base stations(BSs)and user equipments(UEs)in ultra-dense networks(UDN),reusing the same pilot sequences among the cells is inevitable.With pilot reuse scheme,the channel estimation obtained at a BS contains not only the desired channel-state information(CSI)but also interference from neighboring cells,which can severely degrade CSI estimation performance and adversely affect communication performance.In this paper we consider a pilot contamination avoidance based on pilot pattern design for UDN where the pilot reuse employed and the interfering users from neighboring cells may be not at lower power levels at the BS compared to the in-cell users.We present a novel statistical interference model of sub-carriers to describe the non-deterministic interference from neighboring cells.Then,we provide a pilot pattern design model with non-uniform pilot distribution.Based on this,a pilot contamination avoidance based on pilot pattern design is proposed where pilot reuse scheme and the non-deterministic interference from neighboring cells are taken into consideration.Unlike existing interference mitigation approaches,the proposed method eliminates interference through the method of interference avoidance and can be applied to different kinds of channel estimation algorithms.Simulation results showed that the proposed approach can effectively avoid the interference and ensure the accuracy of channel estimation.
基金the Natural Science Foundation of Henan(202300410292)the Key Scientific Projects of Henan Higher Education Institutions(19A510018)+5 种基金the Key Scientific Projects of Henan Higher Education Institutions(20A510008)the Key Scientific Projects of Henan Higher Education Institutions(21A510008)the Key Scientific and Technological Projects(202102210120)the Key Scientific and Technological Projects(212102210553)the Foundation for Young Backbone Teachers in Higher Education Institutions(2018GGJS126)Henan key Laboratory for Big Data Processing and Analytics of Electronic Commerce(2020-KF-6)。
文摘Mobile-edge computing(MEC),enabling to offload computing tasks on mobile devices towards edge servers,can reduce the terminals cost.However,a single MEC sever usually has limited computing capabilities,which can not meet a large number of terminals’requirements.In this paper,we consider an ultra-dense networks(UDN)scenario where the macro base stations(MBSs)are assisted by MEC severs.In particular,we first construct system model for MEC assisted UDN,and build the system overhead minimization.Next,in order to solve the problem,we transform the problem into three sub-problems,i.e.,offloading strategies subproblem,channel assignments subproblem,and power allocation subproblem.Then,employing joint offloading and resource allocation algorithms,we obtain the optimal joint strategy for the MEC assisted UDNs.Finally,simulations are conducted to evaluate the performance of our proposed algorithms.Numerical results show that obtained algorithms can effectively reduce the energy consumption of the system and improve the overall performance of the system.
基金supported by the National Natural Science Foundation of China (61771070 and 61671088)。
文摘Pushing popular contents to the edge of the network can meet the growing demand for data traffic, reduce latency and relieve the pressure of the backhaul. However, considering the limited storage space of the base stations, it is impossible to cache all the contents, especially in ultra-dense network(UDN). Furthermore, the uneven distribution of mobile users results in load imbalance among small base stations(SBSs) in both time and space, which also affects the caching strategy. To overcome these shortcoming, the impact of the changing load imbalance in UDN was investigated, and then a dynamic hierarchical collaborative caching(DHCC) scheme was proposed to optimize latency and caching hit rate. The storage of the SBS is logically divided into the independent caching layer and the collaborative caching layer. The independent caching layer caches the most popular contents for local users’ interest, and the collaborative caching layer caches contents as much as possible for the benefit of content diversity in the region. Different SBSs have respective storage space layer division ratios, according to their real-time traffic load. For SBSs with heavy load, the independent caching layers are allocated with more space. Otherwise, the collaborative caching layers could store more contents with larger space. The simulation results show that, DHCC improved both transmission latency and hit rate compared with existing caching schemes.
基金supported by the National Key Research and Development Program of China(2019YFB1804403)。
文摘To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks(UDNs)and mobile edge computing(MEC)are considered as important solutions.In the MEC enabled UDNs,one of the most important issues is computation offloading.Although a number of work have been done toward this issue,the problem of dynamic computation offloading in time-varying environment,especially the dynamic computation offloading problem for multi-user,has not been fully considered.Therefore,in order to fill this gap,the dynamic computation offloading problem in time-varying environment for multi-user is considered in this paper.By considering the dynamic changes of channel state and users’queue state,the dynamic computation offloading problem for multi-user is formulated as a stochastic game,which aims to optimize the delay and packet loss rate of users.To find the optimal solution of the formulated optimization problem,Nash Q-learning(NQLN)algorithm is proposed which can be quickly converged to a Nash equilibrium solution.Finally,extensive simulation results are presented to demonstrate the superiority of NQLN algorithm.It is shown that NQLN algorithm has better optimization performance than the benchmark schemes.
基金supported by the National Natural Science Foundation of China (61871058)。
文摘In order to meet the emerging requirements for high computational complexity, low delay and energy consumption of the 5 th generation wireless systems(5 G) network, ultra-dense networks(UDNs) combined with multi-access edge computing(MEC) can further improve network capacity and computing capability. In addition, the integration of green energy can effectively reduce the on-grid energy consumption of system and realize green computation. This paper studies the joint optimization of user association(UA) and resource allocation(RA) in MEC enabled UDNs under the green energy supply pattern, users need to perceive the green energy status of base stations(BSs) and choose the one with abundant resources to associate. To minimize the computation cost for all users, the optimization problem is formulated as a mixed integer nonlinear programming(MINLP) which is NP-hard. In order to solve the problem, a deep reinforcement learning(DRL)-based association and optimized allocation(DAOA) scheme is designed to solve it in two stages. The simulation results show that the proposed scheme has good performance in terms of computation cost and time out ratio, as well achieve load balancing potentially.
