Hybridisation of genetic algorithm with simulated annealing for vertical-handover in heterogeneous wireless networks

To provide the seamless mobility in heterogeneous wireless networks two significant methods, simulated annealing (SA) and genetic algorithms (GAs) are hybrid. In this paradigm, vertical handovers (VHs) are necessary for seamless mobility. In this paper, the hybrid algorithm has the ability to find the optimal network to connect with a good quality of service (QoS) in accordance with the user's preferences. The intelligent algorithm was developed to provide solutions near to real time and to avoid slow and considerable computations according to the features of the mobile devices. Moreover, a cost function is used to sustain the chosen QoS during transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR and monetary cost. Simulation results presented that choosing the SA rules would minimise the cost function and the GA-SA algorithm could reduce the number of unnecessary handovers, and thereby avoid the 'Ping-Pong' effect

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Efficient spectrum-handoff schemes for cognitive radio networks

Radio spectrum access is important for terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations. The services offered by terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations have evolved due to technological advances. They are expected to meet increasing users' demands which will require more spectrum. The increasing demand for high throughput by users necessitates allocating additional spectrum to terrestrial wireless networks. Terrestrial radio astronomy observations s require additional bandwidth to observe more spectral windows. Commercial earth observation requires more spectrum for enhanced transmission of earth observation data. The evolution of terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations leads to the emergence of new interference scenarios. For instance, terrestrial wireless networks pose interference risks to mobile ground stations; while inter-satellite links can interfere with terrestrial radio astronomy observations. Terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations also require mechanisms that will enhance the performance of their users. This thesis proposes a framework that prevents interference between terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations when they co-exist; and enhance the performance of their users. The framework uses the cognitive radio; because it is capable of multi-context operation. In the thesis, two interference avoidance mechanisms are presented. The first mechanism prevents interference between terrestrial radio astronomy observations and inter-satellite links. The second mechanism prevent interference between terrestrial wireless networks and the commercial earth observation ground segment. The first interference reductionmechanism determines the inter-satellite link transmission duration. Analysis shows that interference-free inter-satellite links transmission is achievable during terrestrial radio astronomy observation switching for up to 50.7 seconds. The second mechanism enables the mobile ground station, with a trained neural network, to predict the terrestrial wireless network channel idle state. The prediction of the TWN channel idle state prevents interference between the terrestrial wireless network and the mobile ground station. Simulation shows that incorporating prediction in the mobile ground station enhances uplink throughput by 40.6% and reduces latency by 18.6%. In addition, the thesis also presents mechanisms to enhance the performance of the users in terrestrial wireless network, commercial earth observations and terrestrial radio astronomy observations. The thesis presents mechanisms that enhance user performance in homogeneous and heterogeneous terrestrial wireless networks. Mechanisms that enhance the performance of LTE-Advanced users with learning diversity are also presented. Furthermore, a future commercial earth observation network model that increases the accessible earth climatic data is presented. The performance of terrestrial radio astronomy observation users is enhanced by presenting mechanisms that improve angular resolution, power efficiency and reduce infrastructure costs

Vertical Handover Decision Making Using QoS Reputation and GM(1,1) Prediction

Telecommunication consumers are fueling a demand for mobile devices that are rapidly increasing in their capability to provide a wider range of services. These services in turn are consuming more bandwidth and require richer quality of service (QoS) in order to ensure a good end user experience when performing activities such as streaming video content or facilitating voice over IP (VoIP). As a result, network providers are expanding and improving their coverage area while technology to establish Wi-Fi hotspots is becoming more accessible to every day users. This combination of increase in demand and accessibility, coupled with users’ ever increasing expectations for high quality service presents a growing need to seamlessly optimize the use of the overlaid heterogeneous networks in urban areas to maximize the end user experience via the use of a vertical handover mechanism (VHO). Grey systems theory has been used in a wide range of systems including economic, financial, transportation, and military to accurately forecast time series based on limited information. In this thesis we build on a novel reputation based VHO decision rating system by proposing the use of the grey model first order one variable, GM(1,1), in the handover decision making progress. The low complexity of the GM(1,1) model allows for a quick and efficient prediction of the future reputation score for a given network, providing deeper insight into the current state of the target network. Furthermore, we analyze how this model helps balance the load across the heterogeneous networks employing its strategy