In ultra-dense networks(UDN),multiple association can be regarded as a user-centric pattern in which a user can be served by multiple base stations(BSs).The data rate and quality of service can be improved.However,BSs...In ultra-dense networks(UDN),multiple association can be regarded as a user-centric pattern in which a user can be served by multiple base stations(BSs).The data rate and quality of service can be improved.However,BSs in user-centric paradigm are required to serve more users due to this multiple association scheme.The improvement of system performance may be limited by the improving load of BSs.In this letter,we develope an analytical framework for the load distribution of BSs in heterogeneous user-centric UDN.Based on open loop power control(OLPC),a user-centric scheme is considered in which the clustered serving BSs can provide given signal to interference plus noise ratio(SINR)for any typical user.As for any BS in different tiers,by leveraging stochastic geometry,we derive the Probability Mass Function(PMF)of the number of the served users,the Cumulative Distribution Function(CDF)of total power consumption,and the CDF bounds of downlink sum data rate.The accuracy of the theoretical analysis is validated by numerical simulations,and the effect the system parameters on the load of BSs is also presented.展开更多
Cell-free network is a promising architecture with numerous merits in energy efficiency and macro diversity,which is easy and flexible to integrate with other communication technologies.However,its current network top...Cell-free network is a promising architecture with numerous merits in energy efficiency and macro diversity,which is easy and flexible to integrate with other communication technologies.However,its current network topology where access points(APs)are connected to a central processing unit(CPU)to jointly serve the users,causes huge burden to the fronthaul network.To deal with this problem,in this paper,we first combine thoughts in user-centric(UC)network where users are served by selected subset of APs.Then,we propose a successful transmission probability(STP)based AP clustering scheme to reduce the fronthaul capacity requirement(FCR).By using stochastic geometry and proper approximation methods,the approximated STP calculation expression is derived.Numerical simulations demonstrate that the obtained STP expression can provide a tight approximation compared to Monte Carlo simulation results under different system parameters while keeping the computation tractable.Furthermore,the relationship between the FCR and the STP threshold is formulated as a clustering optimization problem,which gives insights on clustering design in UC-CF network systems.We show by simulation results that the proposed scheme requires less fronthaul capacity than the original CF approach while ensuring the STP performance.展开更多
Network densification is a promising solution to fulfill network capacity requirement and transmission rate for beyond 5G and 6G wireless communications.Ultra-dense network(UDN)integrates heterogeneous network resourc...Network densification is a promising solution to fulfill network capacity requirement and transmission rate for beyond 5G and 6G wireless communications.Ultra-dense network(UDN)integrates heterogeneous network resources and coordinates technologies on quality of service controlling,to provide users with flexible service.However,dense deployment reduces coverage radius of the cell,resulting in an increase on handover frequency,which makes a serious impact on service continuity.In this paper,we propose a proactive selection method for dynamic access points grouping(DAPGing)in accordance with“user-centric”philosophy,which selects target Access Points(AP)and reduces handover times to ensure communication continuity.This method includes two criteria:1)the user’s sojourn time,which is determined by analyzing the AP coverage area;2)neighbor relationship between APs,which is determined by coverage area and signal strength characteristics between neighboring APs.Therefore,candidate APs become the proactive selected ones to update the AP group.Stochastic geometry is used to build system model and performance metrics are analyzed,including AP group coverage probability and average update frequency.Experimental analysis shows that the proposed proactive selection method brings similar coverage probability to traditional handover method,while average update frequency is reduced more than 20%selection criteria.展开更多
The mobile data traffic has been exponentially growing during the last several decades.This was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UD...The mobile data traffic has been exponentially growing during the last several decades.This was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service variations.Cell-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this issue.In such systems,each User Equipment(UE)is served by a preferred set of surrounding APs cooperatively.In this paper,we provide a survey of the state-of-the-art literature on CF mMIMO.As a starting point,the significance and the basic properties of CF mMIMO are highlighted.We then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and algorithms.After that,we discuss the practical issues in implementing CF mMIMO and point out the potential future directions.Finally,we conclude this paper with a summary of the key lessons learned in this field.展开更多
基金supported by National Natural Science Foundation of China (No. 61971161)Foundation of Science and Technology on Communication Networks Laboratory (No.6142104190410)Heilongjiang Touyan Team(No. HITTY20190009)
文摘In ultra-dense networks(UDN),multiple association can be regarded as a user-centric pattern in which a user can be served by multiple base stations(BSs).The data rate and quality of service can be improved.However,BSs in user-centric paradigm are required to serve more users due to this multiple association scheme.The improvement of system performance may be limited by the improving load of BSs.In this letter,we develope an analytical framework for the load distribution of BSs in heterogeneous user-centric UDN.Based on open loop power control(OLPC),a user-centric scheme is considered in which the clustered serving BSs can provide given signal to interference plus noise ratio(SINR)for any typical user.As for any BS in different tiers,by leveraging stochastic geometry,we derive the Probability Mass Function(PMF)of the number of the served users,the Cumulative Distribution Function(CDF)of total power consumption,and the CDF bounds of downlink sum data rate.The accuracy of the theoretical analysis is validated by numerical simulations,and the effect the system parameters on the load of BSs is also presented.
文摘Cell-free network is a promising architecture with numerous merits in energy efficiency and macro diversity,which is easy and flexible to integrate with other communication technologies.However,its current network topology where access points(APs)are connected to a central processing unit(CPU)to jointly serve the users,causes huge burden to the fronthaul network.To deal with this problem,in this paper,we first combine thoughts in user-centric(UC)network where users are served by selected subset of APs.Then,we propose a successful transmission probability(STP)based AP clustering scheme to reduce the fronthaul capacity requirement(FCR).By using stochastic geometry and proper approximation methods,the approximated STP calculation expression is derived.Numerical simulations demonstrate that the obtained STP expression can provide a tight approximation compared to Monte Carlo simulation results under different system parameters while keeping the computation tractable.Furthermore,the relationship between the FCR and the STP threshold is formulated as a clustering optimization problem,which gives insights on clustering design in UC-CF network systems.We show by simulation results that the proposed scheme requires less fronthaul capacity than the original CF approach while ensuring the STP performance.
基金This work was supported by the National Natural Science Foundation of China(NSFC)under Grant 61931005.
文摘Network densification is a promising solution to fulfill network capacity requirement and transmission rate for beyond 5G and 6G wireless communications.Ultra-dense network(UDN)integrates heterogeneous network resources and coordinates technologies on quality of service controlling,to provide users with flexible service.However,dense deployment reduces coverage radius of the cell,resulting in an increase on handover frequency,which makes a serious impact on service continuity.In this paper,we propose a proactive selection method for dynamic access points grouping(DAPGing)in accordance with“user-centric”philosophy,which selects target Access Points(AP)and reduces handover times to ensure communication continuity.This method includes two criteria:1)the user’s sojourn time,which is determined by analyzing the AP coverage area;2)neighbor relationship between APs,which is determined by coverage area and signal strength characteristics between neighboring APs.Therefore,candidate APs become the proactive selected ones to update the AP group.Stochastic geometry is used to build system model and performance metrics are analyzed,including AP group coverage probability and average update frequency.Experimental analysis shows that the proposed proactive selection method brings similar coverage probability to traditional handover method,while average update frequency is reduced more than 20%selection criteria.
基金This work was supported in part by National Key R&D Program of China under Grant 2020YFB1807201in part by National Natural Science Foundation of China under Grants 61971027,U1834210,and 61961130391+2 种基金in part by Beijing Natural Science Foundation under Grant L202013in part by Frontiers Science Center for Smart High-speed Railway System under Grant 2020JBZD005in part by the Royal Society Newton Advanced Fellowship under Grant NA191006.E.Björnson was supported by the Grant 2019-05068 from the Swedish Research Council.
文摘The mobile data traffic has been exponentially growing during the last several decades.This was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service variations.Cell-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this issue.In such systems,each User Equipment(UE)is served by a preferred set of surrounding APs cooperatively.In this paper,we provide a survey of the state-of-the-art literature on CF mMIMO.As a starting point,the significance and the basic properties of CF mMIMO are highlighted.We then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and algorithms.After that,we discuss the practical issues in implementing CF mMIMO and point out the potential future directions.Finally,we conclude this paper with a summary of the key lessons learned in this field.
