Deployment,Planning And And Resource Management In Heterogeneous Cellular Networks

With the extensively use of wireless smart devices and rapid development of mobile applications,the current infrastructures are expected to enhance network capacity and reduce energy consumption at the same time.Heterogeneous cellular network(HCN),which is built by deploying small cells in traditional macro cellular networks,can enhance the coverage for users and improve the capacity of the total network.However,due to the frequency reuse,complex co-channel interference exists among macrocells and small cells,which is a great challenge to improve the spectrum efficiency and energy efficiency of HCNs.This thesis has conducted numerous research on cell planning,deployment,user association,resource management to optimize spectrum efficiency and energy efficiency.The main work and contributions are summarized as follows.Considering the dynamic feature of space traffic in real scenario,we model vari-ous traffic patterns using stochastic geometry approach and propose an energy-efficient scheme to deploy small cells in traditional macrocell networks.According to the proposed traffic pattern model,small cells are planned in the HCN according to various traffic pat-terns,so as to minimize the number of active small cell base stations(s-BSs)without compromising on the quality of service requirements.Firstly,we assume a number of candidate locations have been selected for s-BSs initially.Secondly,for each traffic pat-tern,we have designed an iterative algorithm to update BS states and user associations to switch off redundant BSs until the number of s-BSs does not decrease.The final deployed set of s-BSs is the union of active s-BSs under all traffic patterns.The traffic distributions are different in each Monto Carlo simulation.Thus,we may obtain several feasible so-lutions of BS states for each traff-ic pattern.At last,we achieve an optimal solution for each traffic pattern to minimize the deployed s-BSs under all traffic patterns.At the same time we can obtain the final set of s-BSs for deployment.When traffic pattern changes,we can control the BS state according to the optimal BS state under that traffic pattern(switch to active or sleep state),so as to meet the traffic demands under different traffic patterns.Numerical results prove that our scheme can reduce the deployment cost of HCN efficiently and enhance the energy efficiency of the system while guaranteeing the quality of service.Considering users still tend to associate with macrocells and the load imbalance prob-lem followed after the deployment of HCNs,we propose a user association scheme based on joint scheduling and a user association scheme based on biasing to adjust the load in HCNs.The scheme based on joint scheduling adopt an iterative algorithm to realize op-timal user association.Firstly,we model the problem of user association guaranteeing load balancing as a logarithmic based utility maximization problem,which is an NP-hard combinatorial optimization problem.To simplify the problem,we relax the problem as a joint scheduling or fractional user association problem assuming that a user can associate with more than one BS,which can be considered as a basis to achieve the upper bound of the objective function.Secondly,we prove that equal resource allocation is capable to achieve practical optimal performance over a long enough time window.This conclusion further simplifies the problem,which transforms the difficult combinational problem into a linear programming problem.Thirdly,we present a new efficient and low-complexity distributed iterative algorithm which guarantees to converge to the optimal solution with the maximal step.Finally,we study the load balancing problem using bias methods,which include SINR bias and rate bias.By setting the bias factor rationally,they can effectively balance the load between the macrocell and small cells.Numerical results show that using these two methods can effectively achieve load balancing between cells.Besides,using a simple biasing method can also obtain near-optimal performance.Considering co-channel interference problem in the signal transmitting stage in HCNs,we propose a graph-based interference coordination and resource allocation algorithm,which can effectively suppress interference by rationally allocating subchannels and power dynamically,thereby enhancing the spectral efficiency of the whole network.Firstly,we determine the neighbor relationship between each cell pair and divide all cells into cell-clusters.We take independent interference coordination and resource allocation for each cell-cluster,in order to reduce the complexity of the problem.Secondly,we introduce a metric called relative interference and adopt a user clustering algorithm in each cell-cluster to minimize the relative interference in each cell-cluster.Finally,we take a proportional fair subchannel allocation approach among user-clusters in each cell-cluster.On this ba-sis,we use water-filling method to allocate transmit power in each cell.In order to verify the performance of the algorithm,we propose a resource allocation algorithm based on channel-sharing,to obtain the optimal solution of the problem as a baseline.In the simu-lations,we compare the graph-based algorithm,the channel-sharing based algorithm and the distributed algorithm.Numerical results show that the proposed graph-based algo-rithm can acheive more than 95%of the optimal performance considering network spec-trum efficiency,and overwhelm the existing algorithms including the algorithm without considering interference coordination.In addition,the proposed algorithm has low com-plexity,which is more suitable for real-time applications.Considering the scenarios of energy-constrained HCNs,we propose a resource al-location algorithm guaranteeing energy efficiency by optimizing subchannel and power allocation to further enhance the energy efficiency of HCNs from the perspective of re-source management.Firstly,the problem is formulated as a fraction programming prob-lem and the objective function is expressed as the energy efficiency taking the quality of service into consideration.To solve this problem,we transform the fractional program-ming problem into a parametric programming problem by taking energy efficiency as the parameter through Dinkelbach method,and use an iterative method to update the pa-rameter.Secondly,the problem can be viewed as a combinatorial optimization problem in each iteration for a fixed value of energy efficiency,the problem is transformed into the dual form and sub-gradient search method is used to obtain optimal subchannel and power allocation.Finally,the energy efficiency utility parameter is updated according to the optimal subchannel and power allocation results and implement the updated en-ergy efficiency parameter in the next iteration until it converges.Experiments show that the proposed algorithm can effectively improve the energy efficiency of the system,the parameter updating process and subgradient search can converge in a few iterations.