A Research Of Energy Efficiency/Spectral Efficiency Based Wireless Access Technologies In Heterogeneous Network

Traditional heterogeneous network consists of various types of base station, corresponding to different maximize transmit power, coverage, carrier frequency and other parameters, by way of overlapping provides seamless network access and high-speed data transmission. Today, the concept of heterogeneous network has been extended to the combination of a variety of new architectures, including cloud radio access network, control and signaling separation architecture, multi radio access network, etc. In the next generation wireless communication network, energy efficiency is a widely concerned technical indicator. Research on the energy efficiency in heterogeneous network, especially the heterogeneous network based on the cloud radio access network and the control data separation architecture, has been widely concerned by the academia and industry.To improve the energy efficiency of wireless network, the most important research area includes two aspects: energy efficient network resource management and network energy saving technology. This paper mainly studies the following aspects: firstly, energy efficient resource allocation method for control data separation architecture based heterogeneous cloud access network; secondly, dynamic energy consumption saving strategy designed for heterogeneous cloud access network; thirdly, sum-utility maximization method for mixed multi-traffic in heterogeneous network.The first chapter mainly summarizes the current problems and research focus, the content and research results of this paper, and finally the framework of this paper.The second chapter mainly studies the energy efficient resource allocation algorithm for control and signaling separation architecture based heterogeneous cloud access network. First of all, we compare existing cloud access network architecture models of energy consumption, take the power consumption of cloud-based platform and fronthaul links into consideration, and propose a modified network power consumption model. Secondly, we consider heterogeneous fronthaul links, such as wired or wireless link, and formulate the network energy efficiency optimization problem. Then, using the method of fractional programming and convex approximation, we transform the optimization problem into a solvable problem, and then we obtain the optimal solution using the Lagrange dual decomposition method. Finally, the simulation results show that our proposed algorithm has 10% EE gain compared to the static algorithm, and the CDSA-based H-CRAN network can achieve up to 14% EE gain compared to the conventional network even under strict fronthaul capacity limit. Different from previous research work which mainly focuses on transmit power of network, the contribution of this chapter is reflected in the comprehensive consideration of the energy consumption of the whole network and the dynamic sleep strategy of the base stations.The third chapter mainly studies the network energy saving problem for heterogeneous cloud access network. First, we consider the energy consumption of cloud-based platform, fronthaul links, and transmit links, and puts forward a model of energy consumption of the whole network. Secondly, we study the different architecture of network energy consumption model considering the cloud virtual machine system with limited computing resources, and formulate the total energy consumption minimization problem. Then, utilize its characteristics and the convex approximation method, the optimization problem is transformed into second-order cone programming problem, and solved by iterative convex optimization toolbox. Finally, it can be seen that the proposed algorithm has a better energy performance compared with traditional algorithm. Different from the previous research focuses on the power consumption of base stations, the contribution of this chapter is that we consider the whole network energy consumption, and minimize network energy consumption.The fourth chapter mainly studies the downlink resource allocation problem for multi-traffic heterogeneous network, aiming to maximize sum-utility and to optimize the network performance from the traffic satisfaction perspective. Firstly, characteristics of the future network traffic are discussed, and we put forward a unified utility function for different types of traffic. Secondly, considering the different minimum data rate requirements, the maximum interference tolerance constraints, we model the heterogeneous network multi-traffic sum-utility optimization problem. Thirdly, we use the properties of convex utility function, and utilize the iterative WMMSE algorithm to solve the optimization problem. Finally, the system level simulation results show that the proposed algorithm achieves higher system performance compared with the traditional rate maximization algorithm. Different from the previous sum-utility maximization research mostly focuses on the traditional cellular network, this chapter’s contribution is that we consider heterogeneous network cooperative beamforming, and solve the sum-utility optimization problem.The fifth chapter summarizes the content of the full text, and gives the future research direction and the corresponding recommendations.