Analysis of the state of the art of Soft Computing Techniques applied to network planning problems in 5G.

En el presente artículo se realizó una revisión del estado del arte de la aplicación de las técnicas de Soft Computing en la resolución de problemas de planificación de redes 5G, para lo cual se clasificó las diferentes técnicas de soft computing existentes (redes neuronales, lógica difusa, algoritmos evolutivos) y de los trabajos e investigaciones realizados sobre el tema según sus autores, los modelos planteados y los métodos de solución. Adicionalmente se describieron las investigaciones más relevantes en donde se especifican técnicas para dar solución a los problemas de arquitecturas y funcionalidades cruciales en el desarrollo de esta tecnología, entre los cuales se resalta: encontrar una posición óptima para una Estación Base (BS) en un área de interés determinada, operar en las bandas de frecuencias múltiples deseadas mientras se mantiene una alta ganancia, limitar el consumo de energía en las infraestructuras de red 5G y tratar de incrementar la calidad del servicio al disminuir la probabilidad de bloqueo de llamadas. Finalmente se concluyó que las técnicas de soft computing más aplicadas a la solución de problemas de planificación de las redes 5G son lógica difusa, para limitar el consumo de energía en las infraestructuras de red 5G, además de las redes neuronales artificiales y algoritmos genéticos para la admisión de llamadas en redes 5G con la finalidad de incrementar la calidad del servicio al disminuir las interferencias.The current article developed a review of the state of the art about the application of Soft Computing techniques in solving planning problems of 5G networks. To achieve this, the different existing Soft Computing techniques were classified (neural networks, fuzzy logic, evolutionary algorithms) and models proposed and methods of solution were considered from works and research developed about the subject according to their authors. Additionally, the most relevant investigations described techniques which are specified to solve problems of architectures and crucial functionalities in the development of this technology, highlighting the following: finding an optimal position for a Base Station (BS) in an area of particular interest, operating in the desired multiple frequency bands while maintaining high gain, limiting power consumption in 5G network infrastructures, and trying to increase quality of service by decreasing the probability of call blocking. To conclude, the most applied Soft Computing techniques in solving planning problems of 5G networks are fuzzy logic, in order to limit the energy consumption in 5G network infrastructures, in addition to artificial neural networks and genetic algorithms for admission of calls in 5G networks, increasing quality of service by reducing interference

An Artificial Intelligence Framework for Slice Deployment and Orchestration in 5G Networks

© 2015 IEEE. Network slicing is a key enabler to successfully support 5G services with specific requirements and priorities. Due to the diversity of these services, slice deployment and orchestration are essential to guarantee service performance in a cost-effective way. Here, we propose an Artificial Intelligence framework for cross-slice admission and congestion control that simultaneously considers communication, computing, and storage resources to maximize resources utilization and operator revenue. First, we propose a smart feature extraction solution to analyze the characteristics of incoming requests together with the already deployed slices, and then automatically evaluates the request requirements to make appropriate decisions. Second, we design an online algorithm that controls the slice admission based on their priorities, the arrival and departure characteristics, and the available resources. To mitigate system overloading, our framework dynamically adjusts resources allocated to low priority slices, thereby reducing the dropping probability of new slice requests. The proposed algorithm offers outstanding advantages over traditional static approaches by automatically adapting the controller decisions to the system changes. Simulation results show that our framework significantly improves the resource utilization and reduces the slice request dropping probabilities up to 44% as compared to the baseline schemes

An intelligent call admission control algorithm for load balancing in 5G-satellite networks

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Cellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different RATs integrated. In addition to 5G, the user’s device (UD) will be able to connect to the network via LTE, WiMAX, Wi-Fi, Satellite, and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. Satellite networks are among the most sophisticated communication technologies which offer specific benefits in geographically dispersed and dynamic networks. Utilising their inherent advantages in broadcasting capabilities, global coverage, decreased dependency on terrestrial infrastructure, and high security, they offer highly efficient, effective, and rapid network deployments. Satellites are more suited for large-scale communications than terrestrial communication networks. Due to their extensive service coverage and strong multilink transmission capabilities, satellites offer global high-speed connectivity and adaptable access systems. The convergence of 5G technology and satellite networks therefore marks a significant milestone in the evolution of global connectivity. However, this integration introduces a complex problem related to resource management, particularly in Satellite – Terrestrial Integrated Networks (STINs). The key issue at hand is the efficient allocation of resources in STINs to enhance Quality of Service (QoS) for users. The root cause of this issue originates from a vast quantity of users sharing these resources, the dynamic nature of generated traffic, the scarcity of wireless spectrum resources, and the random allocation of wireless channels. Hence, resource allocation is critical to ensure user satisfaction, fair traffic distribution, maximised throughput, and minimised congestion. Achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This research endeavours to address this challenge through the development and evaluation of an intelligent call admission control (CAC) algorithm based on Enhanced Particle Swarm Optimization (EPSO). The primary aim of this research is to design an EPSO-based CAC algorithm tailored specifically for 5G-satellite heterogeneous wireless networks. The algorithm's objectives include maximising the number of admitted calls while maintaining Quality of Service (QoS) for existing users, improving network resource utilization, reducing congestion, ensuring fairness, and enhancing user satisfaction. To achieve these objectives, a detailed research methodology is outlined, encompassing algorithm development, numerical simulations, and comparative analysis. The proposed EPSO algorithm is benchmarked against alternative artificial intelligence and machine learning algorithms, including the Artificial Bee Colony algorithm, Simulated Annealing algorithm, and Q-Learning algorithm. Performance metrics such as throughput, call blocking rates, and fairness are employed to evaluate the algorithms' efficacy in achieving load-balancing objectives. The experimental findings yield insights into the performance of the EPSO-based CAC algorithm and its comparative advantages over alternative techniques. Through rigorous analysis, this research elucidates the EPSO algorithm's strengths in dynamically adapting to changing network conditions, optimising resource allocation, and ensuring equitable distribution of traffic among different RATs. The result shows the EPSO algorithm outperforms the other 3 algorithms in all the scenarios. The contributions of this thesis extend beyond academic research, with potential societal implications including enhanced connectivity, efficiency, and user experiences in 5G-Satellite heterogeneous wireless networks. By advancing intelligent resource management techniques, this research paves the way for improved network performance and reliability in the evolving landscape of wireless communication

A Hybrid Approach to Call Admission Control in 5G Networks

Artificial intelligence is employed for solving complex scientific, technical, and practical problems. Such artificial intelligence techniques as neural networks, fuzzy systems, and genetic and evolutionary algorithms are widely used for communication systems management, optimization, and prediction. Artificial intelligence approach provides optimized results in a challenging task of call admission control, handover, routing, and traffic prediction in cellular networks. 5G mobile communications are designed as heterogeneous networks, whose important requirement is accommodating great numbers of users and the quality of service satisfaction. Call admission control plays a significant role in providing the desired quality of service. An effective call admission control algorithm is needed for optimizing the cellular network system. Many call admission control schemes have been proposed. The paper proposes a methodology for developing a genetic neurofuzzy controller for call admission in 5G networks. Performance of the proposed admission control is evaluated through computer simulation