Location Optimization for Antennas by Asynchronous Particle Swarm Optimization

[[abstract]]A novel optimisation procedure for the location of the transmitter in 3 × 3 multiple input multiple output wireless local area network wireless communication systems is presented. The optimal antenna location for maximising the channel capacity is searched by particle swarm optimiser (PSO) and asynchronous particle swarm optimisation (APSO). There are two different receiver locations considered in the simulation. These two cases are: (i) the transmitter is mobile in the whole indoor environment and the receivers are located on the tables spaced in intervals uniformly distributed (ii) the transmitter is mobile and the receivers are space in uniformly distributed intervals in the whole indoor environment. Numerical results have shown that the proposed PSO and APSO methods are transmit antenna location is optimised to increase channel capacity. The APSO has better optimisation results compared with the PSO and numerical results also show that the APSO outperforms the PSO in convergence speed.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Enhancement and performance analysis for 3D beamforming systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis is about the researching for 5th generation (5G) communication system, which focus on the improvement of 3D beamforming technology in the antenna array using in the Full Dimension Multiple-Input Multiple-Output (FD-MIMO) system and Millimeter-wave (mm-wave) system. When the 3D beamforming technology has been used in 5G communication system, the beam needs a weighting matrix to direct the beam to cover the UEs, but some compromises should be considered. If the narrow beams are used to transmit signals, then more energy is focused in the desired direction, but this has a restricted coverage area to a single or few User Equipments (UEs). If the BS covers multiple UEs, then multiple beams need to be steered towards more groups of UEs, but there is more interference between these beams from their side lobes when they are transmitted at same time. These challenges are waiting to be solved, which are about interference between each beam when the 3D beamforming technology is used. Therefore, there needs to be one method to decrease the generated interference between each beam through directing the side lobe beams and nulls to minimize interference in the 3D beamforming system. Simultaneously, energy needs to be directed towards the desired direction. If it has been decided that one beam should covera cluster of UEs, then there will be a range of received Signal to Interference plus Noise Ratio (SINR) depending on the location of the UEs relative to the direction of the main beam. If the beam is directed towards a group of UEs then there needs be a clustering method to cluster the UEs. In order to cover multiple UEs, an improved K-means clustering algorithm is used to cluster the multiple UEs into different groups, which is based on the cosine distance. Itcan decrease the number of beams when multiple UEs need be covered by multiple beams at same time. Moreover, a new method has been developed to calculate the weighting matrix for beamforming. It can adjust the values of weighting matrix according to the UEs’ location and direct the main beam in a desired direction whilst minimizing its side lobes in other undesired directions. Then the minimum side lobe beamforming system only needs to know the UEs’ location and can be used to estimate the Channel State Information (CSI) of UEs. Therefore, the scheme also shows lower complexity when compared to the beamforming methods with pre-coding. In order to test the improved K-means clustering algorithm and the new weighting method that can enhance the performance for 3D beamforming system, the two simulation systems are simulated to show the results such as 3D beamforming LTE system and mm-wave system

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Self-organization for 5G and beyond mobile networks using reinforcement learning

The next generations of mobile networks 5G and beyond, must overcome current networks limitations as well as improve network performance. Some of the requirements envisioned for future mobile networks are: addressing the massive growth required in coverage, capacity and traffic; providing better quality of service and experience to end users; supporting ultra high data rates and reliability; ensuring latency as low as one millisecond, among others. Thus, in order for future networks to enable all of these stringent requirements, a promising concept has emerged, self organising networks (SONs). SONs consist of making mobile networks more adaptive and autonomous and are divided in three main branches, depending on their use-cases, namely: self-configuration, self-optimisation, and self-healing. SON is a very promising and broad concept, and in order to enable it, more intelligence needs to be embedded in the mobile network. As such, one possible solution is the utilisation of machine learning (ML) algorithms. ML has many branches, such as supervised, unsupervised and Reinforcement Learning (RL), and all can be used in different SON use-cases. The objectives of this thesis are to explore different RL techniques in the context of SONs, more specifically in self-optimization use-cases. First, the use-case of user-cell association in future heterogeneous networks is analysed and optimised. This scenario considers not only Radio Access Network (RAN) constraints, but also in terms of the backhaul. Based on this, a distributed solution utilizing RL is proposed and compared with other state-of-the-art methods. Results show that the proposed RL algorithm outperforms current ones and is able to achieve better user satisfaction, while minimizing the number of users in outage. Another objective of this thesis is the evaluation of Unmanned Aerial vehicles (UAVs) to optimize cellular networks. It is envisioned that UAVs can be utilized in different SON use-cases and integrated with RL algorithms to determine their optimal 3D positions in space according to network constraints. As such, two different mobile network scenarios are analysed, one emergency and a pop-up network. The emergency scenario considers that a major natural disaster destroyed most of the ground network infrastructure and the goal is to provide coverage to the highest number of users possible using UAVs as access points. The second scenario simulates an event happening in a city and, because of the ground network congestion, network capacity needs to be enhanced by the deployment of aerial base stations. For both scenarios different types of RL algorithms are considered and their complexity and convergence are analysed. In both cases it is shown that UAVs coupled with RL are capable of solving network issues in an efficient and quick manner. Thus, due to its ability to learn from interaction with an environment and from previous experience, without knowing the dynamics of the environment, or relying on previously collected data, RL is considered as a promising solution to enable SON