Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy lo...Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations.Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.展开更多
Due to irregular deployment of small base stations （SBSs）, the interference in cognitive heterogeneous networks （CHNs） becomes even more complex; in particular, the uncertainty of spectrum mobility aggravates the ...Due to irregular deployment of small base stations （SBSs）, the interference in cognitive heterogeneous networks （CHNs） becomes even more complex; in particular, the uncertainty of spectrum mobility aggravates the interference context. In this case, how to analyze system capacity to obtain a closed-form expression becomes a crucial problem. In this paper we employ stochastic methods to formulate the capacity of CHNs and achieve a closed-form expression. By using discrete-time Markov chains （DTMCs）, the spectrum mobility with respect to the arrival and departure of macro base station （MBS） users is modeled. Then an integral method is proposed to derive the interference based on stochastic geometry （SG）. Also, the effect of sensing accuracy on network capacity is discussed by concerning false-alarm and miss-detection events. Simulation results are illustrated to show that the proposed capacity analysis method for CHNs can approximate the conventional sum methods without rigorous requirement for channel station information （CSI）. Therefore, it turns out to be a feasible and efficient way to capture the network capacity in CHNs.展开更多
Mobility prediction is one of the promising technologies for improving quality of service(QoS) and network resource utilization. In future heterogeneous networks(HetNets), the network topology will become extremely co...Mobility prediction is one of the promising technologies for improving quality of service(QoS) and network resource utilization. In future heterogeneous networks(HetNets), the network topology will become extremely complicated due to the widespread deployment of different types of small-cell base stations(SBSs). For this complex network topology, traditional mobility prediction methods may cost unacceptable overhead to maintain high prediction accuracy. This problem is studied in this paper, and the hierarchical mobility prediction scheme(HMPS) is proposed for the future HetNets. By dividing the entire process into two prediction stages with different granularity, the tradeoff between prediction accuracy and computational complexity is investigated. Before performing prediction of user mobility, some frequently visited locations are identified from the user’s trajectory, and each location represents an important geographic area(IGA). In the coarse-grained prediction phase, the next most possible location to be visited is predicted at the level of the possible geographic areas by using a second-order Markov chain with fallback. Then, the fine-grained prediction of user position is performed based on hidden Markov model(HMM) from temporal and spacial dimensions. Simulation results demonstrate that, compared with the existing prediction methods, the proposed HMPS can achieve a good compromise between prediction accuracy and complexity.展开更多
基金supported by the National High-Tech R&D Program (863 Program) under grant No. 2015AA01A705Beijing Municipal Science and Technology Commission research fund project under grant No. D151100000115002+1 种基金China Scholarship Council under grant No. 201406470038BUPT youth scientific research innovation program under grant No. 500401238
文摘Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations.Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.
基金Project supported by the National Basic Research Program （973） of China （No. 2012CB315801）, the National Natural Science Foundation of China （Nos. 61302089 and 61302081）, and the State Major Science and Technology Special Projects （No. 2013ZX03001025-002）
文摘Due to irregular deployment of small base stations （SBSs）, the interference in cognitive heterogeneous networks （CHNs） becomes even more complex; in particular, the uncertainty of spectrum mobility aggravates the interference context. In this case, how to analyze system capacity to obtain a closed-form expression becomes a crucial problem. In this paper we employ stochastic methods to formulate the capacity of CHNs and achieve a closed-form expression. By using discrete-time Markov chains （DTMCs）, the spectrum mobility with respect to the arrival and departure of macro base station （MBS） users is modeled. Then an integral method is proposed to derive the interference based on stochastic geometry （SG）. Also, the effect of sensing accuracy on network capacity is discussed by concerning false-alarm and miss-detection events. Simulation results are illustrated to show that the proposed capacity analysis method for CHNs can approximate the conventional sum methods without rigorous requirement for channel station information （CSI）. Therefore, it turns out to be a feasible and efficient way to capture the network capacity in CHNs.
基金supported by the National Science and Technology Major Project of China (2017ZX03001014)the National Natural Science Foundation of China (61771070)
文摘Mobility prediction is one of the promising technologies for improving quality of service(QoS) and network resource utilization. In future heterogeneous networks(HetNets), the network topology will become extremely complicated due to the widespread deployment of different types of small-cell base stations(SBSs). For this complex network topology, traditional mobility prediction methods may cost unacceptable overhead to maintain high prediction accuracy. This problem is studied in this paper, and the hierarchical mobility prediction scheme(HMPS) is proposed for the future HetNets. By dividing the entire process into two prediction stages with different granularity, the tradeoff between prediction accuracy and computational complexity is investigated. Before performing prediction of user mobility, some frequently visited locations are identified from the user’s trajectory, and each location represents an important geographic area(IGA). In the coarse-grained prediction phase, the next most possible location to be visited is predicted at the level of the possible geographic areas by using a second-order Markov chain with fallback. Then, the fine-grained prediction of user position is performed based on hidden Markov model(HMM) from temporal and spacial dimensions. Simulation results demonstrate that, compared with the existing prediction methods, the proposed HMPS can achieve a good compromise between prediction accuracy and complexity.
