Neural network design for intelligent mobile network optimisation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe mobile networks users’ demands for data services are increasing exponentially, this is due to two main factors: the first is the evolution of smart phones and their application, and the second is the emerging new technologies for internet of things, smart cities…etc, which keeps pumping more data into the network; ‘though most of the data routed in the current mobile network is non-live data’. This increasing of demands arise the necessity for the mobile network operators to keep improving their network to satisfy it, this improvement takes place via adding hardware or increasing the resources or a combination of both. The radio resources are strictly limited due to spectrum licensing and availability, therefore efficient spectrum utilization is a major goal to be achieved for both network operators and developers. Simultaneous and multiple channel access,and adding more cells to the network are ways used to increase the data exchanged between the network nodes. The current 4G mobile system is based on the Orthogonal Frequency Division Multiple Access (OFDMA) for accessing the medium and the intercell interference degrades the link quality at the cell edge, with the introduction of heterogeneity concept to the LTE in Release 10 of the 3GPP the handover process became even more complex. To mitigate the intercell interference at the cell edge, coordinated multipoint and carrier aggregation techniques are utilized for dual connectivity. This work is focused on designing and proposing enhancing features to improve network performance and sustainability, these features comprises of distributing small cells for data only transmission, handover schemes performance evaluation at cell edge with dual connectivity, and Artificial Intelligence technology for balancing and prediction. In the proposed model design the data and controls of the Small eNodeB (SeNodeB) are processed at the network edge using a Mobile Edge Computing (MEC) server and the SeNodeBs are used to boost services provided to the users, also the concept of caching data has been investigated, the caching units where implemented in different network levels. The proposed system and resource management are simulated using the OPNET modeller and evaluated through multiple scenarios with and without full load, the UE is reconfigured to accommodate dual connectivity and have two separate connections for uplink and downlink, while maintaining connection to the Macro cell via uplink, the downlink is dedicated for small cells when content is requested from the cache. The results clearly show that the proposed system can decrease the latency while the total throughput delivered by the network has highly improved when SeNodeBs are deployed in the system, rising throughput will incur the rise of overall capacity which leads to better services being provided to the users or more users to join and benefit from the network. Handover improvement is also considered in this work, with the help of two Artificial Intelligence (AI) entities better handover performance are achieved. Balanced load over the SeNodeBs results in less frequent handover, the proposed load balancer is based on artificial neural network clustering model with self-organizing map as a hidden layer, it’s trained to forecast the network condition and learn to reduce the number of handovers especially for the UEs at the cell edge by performing only necessary ones, and avoid handovers to the Macro cell for the downlink direction. The examined handovers concern the downlinks when routing non live video stored at the small cell’s cache, and a reduction in the frequent handovers was achieved when running the balancer. Keep revolving in the handover orbit, another way to preserve and utilize network resources is by predicting the handovers before they occur, and allocate the required data in the target SeNodeB, the predictor entity in the proposed system architecture combines the features of Radial Basis Function Neural Network and neural network time series tool to create and update prediction list from the system’s collected data and learn to predict the next SeNodeB to associate with. The prediction entity is simulated using MATLAB, and the results shows that the system was able to deliver up to 92% correct predictions for handovers which led to overall throughput improvement of 75%

Occupancy detection in smart home space using interoperable building automation technologies

To detect whether people are occupying individual rooms in a smart home, a range of sensors and building automation technologies can be employed. For these technologies to function in tandem and exchange useful data in a smart home environment, they must be interoperable. The article presents a new interoperable solution which combines existing decentralized KNX building automation technology with a KNX/LabVIEW software application gateway using visible light communication to track occupancy in a room. The article also describes a novel KNX/IoT software application gateway which uses an MQTT protocol for interoperability between KNX technology and IBM Watson IoT platform. We conducted an experiment with the originally designed solution to detect occupancy in an office room. We used KNX and BACnet building automation technology to produce an interoperable KNX/BACnet hardware gateway which allowed the application of artificial neural network mathematical methods for CO2 waveform prediction. The best results in detecting occupancy in a room were R = 0.9548 (Levenberg-Marquardt algorithm), R = 0.9872 (Bayesian regularization algorithm), and R = 0.8409 (scaled conjugate gradient algorithm), which correspond to the results obtained by other authors and a minimum system prediction accuracy of 96%.Web of Science12art. no. 4

A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys & Tutorials (IEEE COMST