Mobility And Interference Management In 5G Heterogeneous Cellular Networks

To cope with exponential increase in mobile data demand,heterogeneous cellular net-works are proposed as a basic architecture for 5G networks.One of the characteristics of 5G heterogeneous cellular networks is the multi-tier structure.Deploying a set of small cells in the Macrocell network to form multi-tier cellular networks is a cost-efficient way to increase the data rate and spectrum efficiency.In addition,the other characteristic of 5G heterogeneous cellular networks is that mobile users have multiple communication meth-ods:Device-to-Device(D2D)communication method and cellular communication method.D2D communication,which allows devices to communicate directly under the control of base stations,is a promising heterogeneous technology to improve the system throughput.Because of the massive deployment of small cells,mobile users move between close-by cells increases considerably,hence cross-tier handover and mobility management play cru-cial roles to provision seamless coverage and ensure service continuity.On the other hand,interference management is a key research challenge with the co-existence of D2D commu-nications and multi-tier cellular networks because the interference situation is more com-plicated.Based on the above,this dissertation mainly focuses on investigating the inbound handover confusion in the 5G heterogeneous cellular networks with the help of mobility prediction,and the interference management strategy in the Macro-small cell networks en-abling D2D communications.The main achievements and results of this dissertation are summarized as follows:1.The inbound handover confusion in the two-tier Macrocell-small cell networks with help of mobility prediction is investigate.Instead of studying the mobile user's movement,an analytical model for the activity status of small cells is propose.That model exploits the statistical property of inbound handover events that would happen in small cells.In order to obtain the optimal prediction outcome of the next status for the small cells,the optimal cell status prediction(CSP)algorithm is proposed.On avoiding the handover confusion,a dy-namic allocation approach of physical cell identifier(DA-PCI)according to the prediction results is developed.Designing two PCI allocation strategies:i)the cell status prediction(CSP)based strategy,by which the dedicated PCIs will be assigned to the small cells with busy activity status while the other small cells share the public PCIs;ii)an integrated strat-egy in order to fully exploit the usage of PCI.Formulating the preference relation for small cells via reference signal received quality(RSRQ)relation integrated with status prediction information using Bayesian average method.Simulation results reveal that the proposed al-gorithms yield a higher accuracy than the conventional methods;in the meantime,handover confusions can be reduced significantly during the inbound handover.2.The two-tier Macro-small cell networks enabling D2D communication is modelled.To avoid the complicated interference for cross-tier D2D,a mode selection scheme with a dedicated resource sharing strategy is proposed.For co-tier D2D,a joint optimization problem of power control and resource reuse with the aim of maximizing the overall outage capacity is formulated.To solve this non-convex optimization problem,a heuristic algo-rithm is devised to obtain a suboptimal solution and reduce the computational complexity.System-level simulations demonstrate the effectiveness of the proposed method,which can provide enhanced system performance and guarantee the quality-of-service(QoS)of all de-vices in two-tier D2D Macro-small cell networks.In addition,the study reveals the high potential of introducing cross-and co-tier D2D in small cell networks:i)cross-tier D2D obtains better performance at low and medium small cell densities than co-tier D2D,and ii)co-tier D2D achieves a steady performance improvement with the increase of small cell density.