摘要
As the traffic distribution in China mainland is far from uniform, a new traffic model in China mainland is presented on the basis of per-capita Gross Domestic Product (GDP) and density of population. Based on this characteristic traffic model, a new Traffic Dependent Dynamic Channel Allocation and Reservation (TDDCAR) technique is proposed, the simulation model is built, and the strategies' performance is evaluated through computer simulation. The simulation results show that, compared to the conventional Fixed Channel Allocation (FCA), TDDCAR estimates the traffic conditions in every spot beam and frequently adjusts the traffic according to current traffic conditions. It has achieved a significant improvement in new call blocking probability, handover blocking probability, and fair index, particularly, in heavy traffic conditions. The building of traffic model in China mainland and the analysis of the simulation results has been a key foundation for the study of resource allocation schemes in the future.
As the traffic distribution in China mainland is far from uniform, a new traffic model in China mainland is presented on the basis of per-capita Gross Domestic Product （GDP） and density of population. Based on this characteristic traffic model, a new Traffic Dependent Dynamic Channel Allocation and Reservation （TDDCAR） technique is proposed, the simulation model is built, and the strategies＇ performance is evaluated through computer simulation. The simulation results show that, compared to the conventional Fixed Channel Allocation （FCA）, TDDCAR estimates the traffic con- ditions in every spot beam and frequently adjusts the traffic according to current traffic conditions. It has achieved a significant improvement in new call blocking probability, handover blocking probability, and fair index, particularly, in heavy traffic conditions. The building of traffic model in China mainland and the analysis of the simulation results has been a key foundation for the study of resource allocation schemes in the future.
