4. generációs mobil rendszerek kutatása = Research on 4-th Generation Mobile Systems

A 3G mobil rendszerek szabványosítása a végéhez közeledik, legalábbis a meghatározó képességek tekintetében. Ezért létfontosságú azon technikák, eljárások vizsgálata, melyek a következő, 4G rendszerekben meghatározó szerepet töltenek majd be. Több ilyen kutatási irányvonal is létezik, ezek közül projektünkben a fontosabbakra koncentráltunk. A következőben felsoroljuk a kutatott területeket, és röviden összegezzük az elért eredményeket. Szórt spektrumú rendszerek Kifejlesztettünk egy új, rádiós interfészen alkalmazható hívásengedélyezési eljárást. Szimulációs vizsgálatokkal támasztottuk alá a megoldás hatékonyságát. A projektben kutatóként résztvevő Jeney Gábor sikeresen megvédte Ph.D. disszertációját neurális hálózatokra épülő többfelhasználós detekciós technikák témában. Az elért eredmények Imre Sándor MTA doktori disszertációjába is beépültek. IP alkalmazása mobil rendszerekben Továbbfejlesztettük, teszteltük és általánosítottuk a projekt keretében megalkotott új, gyűrű alapú topológiára épülő, a jelenleginél nagyobb megbízhatóságú IP alapú hozzáférési koncepciót. A témakörben Szalay Máté Ph.D. disszertációja már a nyilvános védésig jutott. Kvantum-informatikai módszerek alkalmazása 3G/4G detekcióra Új, kvantum-informatikai elvekre épülő többfelhasználós detekciós eljárást dolgoztunk ki. Ehhez új kvantum alapú algoritmusokat is kifejlesztettünk. Az eredményeket nemzetközi folyóiratok mellett egy saját könyvben is publikáltuk. | The project consists of three main research directions. Spread spectrum systems: we developed a new call admission control method for 3G air interfaces. Project member Gabor Jeney obtained the Ph.D. degree and project leader Sandor Imre submitted his DSc theses from this area. Application of IP in mobile systems: A ring-based reliable IP mobility mobile access concept and corresponding protocols have been developed. Project member Máté Szalay submitted his Ph.D. theses from this field. Quantum computing based solutions in 3G/4G detection: Quantum computing based multiuser detection algorithm was developed. Based on the results on this field a book was published at Wiley entitled: 'Quantum Computing and Communications - an engineering approach'

Ubiquitous Computing for Remote Cardiac Patient Monitoring: A Survey

New wireless technologies, such as wireless LAN and sensor networks, for telecardiology purposes give new possibilities for monitoring vital parameters with wearable biomedical sensors, and give patients the freedom to be mobile and still be under continuous monitoring and thereby better quality of patient care. This paper will detail the architecture and quality-of-service (QoS) characteristics in integrated wireless telecardiology platforms. It will also discuss the current promising hardware/software platforms for wireless cardiac monitoring. The design methodology and challenges are provided for realistic implementation

Admission Control Optimisation for QoS and QoE Enhancement in Future Networks

Recent exponential growth in demand for traffic heterogeneity support and the number of associated devices has considerably increased demand for network resources and induced numerous challenges for the networks, such as bottleneck congestion, and inefficient admission control and resource allocation. Challenges such as these degrade network Quality of Service (QoS) and user-perceived Quality of Experience (QoE). This work studies admission control from various perspectives. For example, two novel single-objective optimisation-based admission control models, Dynamica Slice Allocation and Admission Control (DSAAC) and Signalling and Admission Control (SAC), are presented to enhance future limited-capacity network Grade of Service (GoS), and for control signalling optimisation, respectively. DSAAC is an integrated model whereby a cost-estimation function based on user demand and network capacity quantifies resource allocation among users. Moreover, to maximise resource utility, adjustable minimum and maximum slice resource bounds have also been derived. In the case of user blocking from the primary slice due to congestion or resource scarcity, a set of optimisation algorithms on inter-slice admission control and resource allocation and adaptability of slice elasticity have been proposed. A novel SAC model uses an unsupervised learning technique (i.e. Ranking-based clustering) for optimal clustering based on users’ homogeneous demand characteristics to minimise signalling redundancy in the access network. The redundant signalling reduction reduces the additional burden on the network in terms of unnecessary resource utilisation and computational time. Moreover, dynamically reconfigurable QoE-based slice performance bounds are also derived in the SAC model from multiple demand characteristics for clustered user admission to the optimal network. A set of optimisation algorithms are also proposed to attain efficient slice allocation and users’ QoE enhancement via assessing the capability of slice QoE elasticity. An enhancement of the SAC model is proposed through a novel multi-objective optimisation model named Edge Redundancy Minimisation and Admission Control (E-RMAC). A novel E-RMAC model for the first time considers the issue of redundant signalling between the edge and core networks. This model minimises redundant signalling using two classical unsupervised learning algorithms, K-mean and Ranking-based clustering, and maximises the efficiency of the link (bandwidth resources) between the edge and core networks. For multi-operator environments such as Open-RAN, a novel Forecasting and Admission Control (FAC) model for tenant-aware network selection and configuration is proposed. The model features a dynamic demand-estimation scheme embedded with fuzzy-logic-based optimisation for optimal network selection and admission control. FAC for the first time considers the coexistence of the various heterogeneous cellular technologies (2G, 3G,4G, and 5G) and their integration to enhance overall network throughput by efficient resource allocation and utilisation within a multi-operator environment. A QoS/QoE-based service monitoring feature is also presented to update the demand estimates with the support of a forecasting modifier. he provided service monitoring feature helps resource allocation to tenants, approximately closer to the actual demand of the tenants, to improve tenant-acquired QoE and overall network performance. Foremost, a novel and dynamic admission control model named Slice Congestion and Admission Control (SCAC) is also presented in this thesis. SCAC employs machine learning (i.e. unsupervised, reinforcement, and transfer learning) and multi-objective optimisation techniques (i.e. Non-dominated Sorting Genetic Algorithm II ) to minimise bottleneck and intra-slice congestion. Knowledge transfer among requests in form of coefficients has been employed for the first time for optimal slice requests queuing. A unified cost estimation function is also derived in this model for slice selection to ensure fairness among slice request admission. In view of instantaneous network circumstances and load, a reinforcement learning-based admission control policy is established for taking appropriate action on guaranteed soft and best-effort slice requests admissions. Intra-slice, as well as inter-slice resource allocation, along with the adaptability of slice elasticity, are also proposed for maximising slice acceptance ratio and resource utilisation. Extensive simulation results are obtained and compared with similar models found in the literature. The proposed E-RMAC model is 35% superior at reducing redundant signalling between the edge and core networks compared to recent work. The E-RMAC model reduces the complexity from O(U) to O(R) for service signalling and O(N) for resource signalling. This represents a significant saving in the uplink control plane signalling and link capacity compared to the results found in the existing literature. Similarly, the SCAC model reduces bottleneck congestion by approximately 56% over the entire load compared to ground truth and increases the slice acceptance ratio. Inter-slice admission and resource allocation offer admission gain of 25% and 51% over cooperative slice- and intra-slice-based admission control and resource allocation, respectively. Detailed analysis of the results obtained suggests that the proposed models can efficiently manage future heterogeneous traffic flow in terms of enhanced throughput, maximum network resources utilisation, better admission gain, and congestion control

Uncertainty and Congestion Elimination in 4G Network Call Admission Control using Interval Type-2 Intuitionistic Fuzzy Logic

The management and control of the global growth and complex nature of wireless Fourth Generation (4G) Networks elicits the need for Call Admission Control (CAC). However, CAC faces the challenge of network congestion, thereby deteriorating the network Quality of Service (QoS) due to inherent imprecision and uncertainties in the QoS data which leads to difficulties in measuring some objective and constraints of QoS using crisp values. Previous researches have shown the strength of Interval Type-2 Fuzzy Logic System (IT2FLS) in coping adequately with linguistic uncertainties. Intuitionistic fuzzy sets (IFSs) have indicated their ability to further reduce uncertainty by handling conflicting evaluation involving membership (M), nonmembership (NM) and hesitation. This paper applies the Interval Type-2 Intuitionistic Fuzzy Logic System (IT2IFLS) in solving CAC problem in order to achieve a better QoS in 4G Networks

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

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%