The dense deployment of small cell networks is a key feature of next generation mobile networks aimed at providing the necessary capacity increase. Wireless heterogeneous networks are created by combining several radio access technologies, each with its own potentials, capabilities and limitations. In these networks, providing real-time services with quality assurance is essential. For effective use of radio resources, the Radio Resource Management method was introduced which its performance and efficiency is better than the control of independent radio resources in any radio access technology. In this paper, we introduced a novel approach to select the most effective radio access technologies by taking into account some performance parameters like the type of service, users’ distribution pattern and the cost of the services. It also optimizes the handover relations between macrolayer and small cells. The proposed approach is a self-optimizing model can be employed to control resources and improve performance indices associated with mobile networks without human interference by only relying on network intelligence. In order to maximize the network performance, we applied the dynamic backhauling technique to analyze the uplink signaling data which increased the validity level of the decision-making process. Based on the extracted semantic information, the network decision-making engine is able to adjust the network parameters and efficiently allocate the resources. The numerical results exhibit considerable power saving for different traffic models in addition to reduce the rate of vertical handovers. The results also show increase the network throughput by up to 30%.
The dense deployment of small cell networks is a key feature of next generation mobile networks aimed at providing the necessary capacity increase. In order to reach an acceptable performance in such ultra-dense networks, real-time resource management is of great importance. Therefore, self-optimization networking is proposed as the only viable solution to increase the networks’ utility. This paper proposed a self-optimizing model to enhance network performance and guarantee the users’ QoS requirements by considering limited resources and using effective user association, carrier scheduling and handover optimization algorithms. In order to maximize the network performance, we applied the smart backhauling technique in order to analyze the signaling to increase the validity of the decision making process. Based on the semantic information extracted from the access layer, the network decision-making center is able to adjust the network parameters and resource allocation effectively. The goal function is defined as maximizing the total energy efficiency by considering the transmission power, energy harvesting capability and the user QoS constraints so that the idle small cells are considered turned off temporarily to boost the power efficiency. Although the optimization problem is non-convex, a quadratic mixed-integer function is solved to obtain a global optimal solution. Since the actual implementation of the real-time algorithm has high computational complexity, two algorithms with different complexity levels are proposed. These algorithms use the carrier matching feature and optimal transmission power for problem-solving. The simulation results prove that, despite the increased computational complexity, effective resource allocation and optimal HO relations made the proposed approach capable to increase performance indices such as network throughput by up to 30%.
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