Research On Interference Management And Resource Allocation Algorithms In Heterogeneous Networks

Heterogeneous networks (HetNets) technique has been widely considered to be accepted by3rd Generation Partnership Project (3GPP) as one of the key candidate technologies of LTE-Advanced standards. HetNets consist of several types of base stations, such as macrocell, microcell, picocell, femtocell, relay and remote radio head (RRH). HetNets deploy multi-type of low power base stations in the coverage area of traditional cellular system, in order to eliminate the areas of both blind-zone and hot-zone, which will tremendously increase the network capacity. Compared with traditional macro cellular base stations, low power base stations, such as picocell, femtocell and relay, are a more economical approach to enhance the network capacity. However, a phenomenon cannot be avoided that the coverage areas of different base stations deployed in the same area simultaneously would be overlapping. Therefore, the complicated inter-cell interference is required to be tackled.The inter-cell interference coordination algorithm, the resource optimization algorithms to minimize the total power consumption and the network interference power level, are researched in the thesis. The inter-cell interference coordination algorithm could effectively decrease the inter-cell interference power level and improve the channel qualities of cell-edge users, which would further enhance the network capacity and users’performance. Resource optimization algorithms reduce the power consumption and suppress the total interference power level of the network, while satisfy the users demands. The main research work and contributions are summarized as follows:A novel algorithm to coordinate the inter-cell interference in HetNets is firstly proposed. On the basis of the interference relationship of users being served and neighbor base stations, an interference graph is constructed by the nodes and edges, which represent the base stations and the interference relationships respectively. An orthogonal spectrum allocation algorithm is studied, which is based on the coloring algorithm according to the vertex degree to assign the adjacent base stations in the graph with non-overlapping frequency bandwidth. The algorithm eliminates the strong interference from adjacent base stations is eliminated. The orthogonal frequency bandwidth is assigned to the base stations, which effectively controls the interference power level in the bandwidth and satisfies the signal to interference plus noise ratio (SINR) requirements of cell-edge users.To further improve the spectrum utilization ratio than the study above, an adaptive frequency resource allocation algorithm is proposed to explore the variations of the users’channel qualities. Based on the comparison results of the SINR threshold and the channel quality information from both the users being served and nearby users of the adjacent base stations, the adaptive frequency allocation algorithm is consisted of three consecutive steps as’request-reply-decision’, in which each base station independently determines whether the spectrum could be shared according to the feedback spectrum sharing information. The adaptive frequency resource allocation algorithm only needs to exchange limited information including the bandwidth request and feedback spectrum sharing information, and quickly assigns the frequency resource to improve the spectrum utilization ratio.The resource allocation algorithms are well researched in the thesis, which optimize the power allocation in HetNets scenario and guarantee the users’ minimum transmission rate. The power optimization algorithms consist of the centralized algorithm and the distributed algorithm. Different from other optimization methods, several factors including the load of base stations, users’ dissatisfactory ratio, scheduling priority and the downlink transmission power, are jointly considered to design the centralized algorithm. The scheduling priorities of base stations and users are determined according to the utility values in the centralized algorithm. But the base stations in the restricted matrix are forbidden to share the same bandwidth to eliminate the strong co-channel interference received by the users of the adjacent base stations. The transmission power levels of all base stations, which are selected as the values of the utility matrix in ascending order, within the same bandwidth are jointly calculated. The power and frequency resource allocation of all the base stations are coordinated to minimize the total power consumption of all the base station in the centralized algorithm. However, the total bandwidth is classified into the orthogonal bandwidth and non-orthogonal bandwidth according to users’ types in the distributed algorithm. The cell-edge users are permitted to occupy the orthogonal bandwidth with higher priority to eliminate the interference from neighboring base stations, while the cell-center users could satisfy the SINR threshold requirements in the whole bandwidth. With the power control algorithm and user bandwidth extension, the downlink transmission power consumption of each base station is further reduced to a lower level. The channel quality and interference information, which is used to perform the deployment optimization of both the frequency resource and power allocation, is just required to exchange among the adjacent base stations in the distributed algorithm.A distributed resource allocation algorithm is proposed in the last section, which is designed to suppress the interference power level. With the application of adaptive modulation and coding (AMC) technique, the modulation and coding scheme (MCS) of the users are determined by the serving base stations according to the channel qualities. However, the MCS levels of users have great influence on the power consumption of base stations and the total interference power level of the network. The distributed algorithm firstly constructs the MCS search space according to all the possible combinations of the MCS levels of all users. Then, the distributed algorithm determines the frequency and power resource allocation results depending on the selected MCS combination of users, which is in accordance with the rule of the minimum increment of total network interference power. Finally, the optimal assignment results of the MCS levels of all users, frequency resource and power level are determined according to the criterion of minimizing interference power level of the HetNets.