A novel optimal small cells deployment for next-generation cellular networks

Small-cell-deployments have pulled cellular operators to boost coverage and capacity in high-demand areas (for example, downtown hot spots). The location of these small cells (SCs) should be determined in order to achieve successful deployments. In this paper, we propose a new approach that optimizes small cells deployment in cellular networks to achieve three objectives: reduce the total cost of network installation, balancing the allocation of resources, i.e. placement of each SC and their transmitted power, and providing optimal coverage area with a lower amount of interference between adjacent stations. An accurate formula was obtained to determine the optimum number of SC deployment (NSC). Finally, we derive a mathematical expression to calculate the critical-handoff-point (CHP) for neighboring wireless stations

Small cell deployment for data only transmission assisted by mobile edge computing functionality

© 2017 IEEE. The continuous evolution of mobile networks is demanding and promising. The increase in mobile population, the introduction of new applications and use cases which represents a major shift in the paradigm from voice to data especially with the adoption of the IP network from the internet in the mobile networks. This has imposed challenges in current architecture of mobile networks in terms of backhaul capacity and efficiency which can hardly satisfy such user demands due to heavy burden on the backhaul links and long latency. Deployment of the next generation mobile networks require an ecosystem of changeable components that represent an integration of IT and Telecommunications Networking in order to bring new possibilities and capabilities that are key sources to the Average Revenue Per User for operators. A key transformation is the ability to provide network functionality and content delivery at the edge of the network based on Mobile Edge Computing borrowed from the IT network while providing the service by deploying a heterogeneous Radio Access network

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%

Small cells deployment for traffic handling in centralized heterogeneous network

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonAs the next phase of mobile technology, 5G is coming with a new vision that is characterized by a connected society, in which everything will be effectively connected, providing a variety of services and diverse business models that require more than just higher data rates and more capacity to target new kinds of ultra-reliable and flexible connection. However, next generation of applications, services and use cases will have extreme variation in requirements which in turn amplified the demand on the network resources. Therefore, 5G will require a whole new design that take into consideration efficient resource management and utilisation. An observation that was made throughout this research refers to the demand for more capacity, reduced latency, and increased density as common factors of many of the next generation use cases. This inescapably implies that the use of small cells is an ideal solution for next generation applications requirements, provided that the necessary storage and computing resources need to be distributed closer to the actual user. In this context, this research proposed an architecture of a centralised heterogenous network, consisting of Macro and Small cells with storage and computing resources, all controlled by a centralized functionality embedded within a gateway at the edge of the network. Compared to the basic network, the proposed solutions have been proven to provide overall system performance enhancement. This involves extending the system by adding small cells to serve dedicated services for User Equipment (UE) with dual connectivity from local server which reduces the overall system delay while increasing the overall system throughput. The added centralized mobility management was proven to be capable of tracing the mobility of the UEs within the system coverage, by keeping one connection with the main cell while moving between small cells resulting in enhancement to the handover delay by 11% without service interruptions. Finally, the proposed slicing model demonstrated the system’s ability to provide different levels of services to users based on different Quality of Service (QoS) requirements and to differentiate between various applications without affecting the performance of other services, benefiting from more flexible infrastructure than the traditional network. In addition, a 50% improvement in the performance was observed in terms of the CPU utilization. In such architecture, the required capacity can be added exactly where it is needed and when it is needed, coverage problems can be directly addressed, higher throughput, lower latency, and efficient mobility management can be achieved as a result of efficient resource management and distribution which is one of key factors in the deployment of next generation mobile network system