Fitness Landscape Analysis for Scalable Multicast RRM Problem in Cellular Network

International audienceThis paper aims to solve the Radio Resource Management (RRM) problem for Multimedia Broadcast Multicast Service (MBMS) system in cellular network. We develop a flexible model to perform dynamic radio resource allocation for MBMS service by using metaheuristic approach. We conduct fitness landscape analysis to study the characteristics of the proposed model, which helps us to select appropriate search strategy. Simulation results show that the proposed algorithm provides better performance than existing algorithms. Keywords: fitness landscape, metaheuristic approach, multimedia multicast, radio resource management

Access Network Selection in Heterogeneous Networks

The future Heterogeneous Wireless Network (HWN) is composed of multiple Radio Access Technologies (RATs), therefore new Radio Resource Management (RRM) schemes and mechanisms are necessary to benefit from the individual characteristics of each RAT and to exploit the gain resulting from jointly considering the whole set of the available radio resources in each RAT. These new RRM schemes have to support mobile users who can access more than one RAT alternatively or simultaneously using a multi-mode terminal. An important RRM consideration for overall HWN stability, resource utilization, user satisfaction, and Quality of Service (QoS) provisioning is the selection of the most optimal and promising Access Network (AN) for a new service request. The RRM mechanism that is responsible for selecting the most optimal and promising AN for a new service request in the HWN is called the initial Access Network Selection (ANS). This thesis explores the issue of ANS in the HWN. Several ANS solutions that attempt to increase the user satisfaction, the operator benefits, and the QoS are designed, implemented, and evaluated. The thesis first presents a comprehensive foundation for the initial ANS in the H\VN. Then, the thesis analyses and develops a generic framework for solving the ANS problem and any other similar optimized selection problem. The advantages and strengths of the developed framework are discussed. Combined Fuzzy Logic (FL), Multiple Criteria Decision Making (MCDM) and Genetic Algorithms (GA) are used to give the developed framework the required scalability, flexibility, and simplicity. The developed framework is used to present and design several novel ANS algorithms that consider the user, the operator, and the QoS view points. Different numbers of RATs, MCDM tools, and FL inference system types are used in each algorithm. A suitable simulation models over the HWN with a new set of performance evolution metrics for the ANS solution are designed and implemented. The simulation results show that the new algorithms have better and more robust performance over the random, the service type, and the terminal speed based selection algorithms that are used as reference algorithms. Our novel algorithms outperform the reference algorithms in- terms of the percentage of the satisfied users who are assigned to the network of their preferences and the percentage of the users who are assigned to networks with stronger signal strength. The new algorithms maximize the operator benefits by saving the high cost network resources and utilizing the usage of the low cost network resources. Usually better results are achieved by assigning the weights using the GA optional component in the implemented algorithms

Efficient radio resource management for the fifth generation slice networks

It is predicted that the IMT-2020 (5G network) will meet increasing user demands and, hence, it is therefore, expected to be as flexible as possible. The relevant standardisation bodies and academia have accepted the critical role of network slicing in the implementation of the 5G network. The network slicing paradigm allows the physical infrastructure and resources of the mobile network to be “sliced” into logical networks, which are operated by different entities, and then engineered to address the specific requirements of different verticals, business models, and individual subscribers. Network slicing offers propitious solutions to the flexibility requirements of the 5G network. The attributes and characteristics of network slicing support the multi-tenancy paradigm, which is predicted to drastically reduce the operational expenditure (OPEX) and capital expenditure (CAPEX) of mobile network operators. Furthermore, network slices enable mobile virtual network operators to compete with one another using the same physical networks but customising their slices and network operation according to their market segment's characteristics and requirements. However, owing to scarce radio resources, the dynamic characteristics of the wireless links, and its capacity, implementing network slicing at the base stations and the access network xix becomes an uphill task. Moreover, an unplanned 5G slice network deployment results in technical challenges such as unfairness in radio resource allocation, poor quality of service provisioning, network profit maximisation challenges, and rises in energy consumption in a bid to meet QoS specifications. Therefore, there is a need to develop efficient radio resource management algorithms that address the above mentioned technical challenges. The core aim of this research is to develop and evaluate efficient radio resource management algorithms and schemes that will be implemented in 5G slice networks to guarantee the QoS of users in terms of throughput and latency while ensuring that 5G slice networks are energy efficient and economically profitable. This thesis mainly addresses key challenges relating to efficient radio resource management. First, a particle swarm-intelligent profit-aware resource allocation scheme for a 5G slice network is proposed to prioritise the profitability of the network while at the same time ensuring that the QoS requirements of slice users are not compromised. It is observed that the proposed new radio swarm-intelligent profit-aware resource allocation (NR-SiRARE) scheme outperforms the LTE-OFDMA swarm-intelligent profit-aware resource (LO-SiRARE) scheme. However, the network profit for the NR-SiRARE is greatly affected by significant degradation of the path loss associated with millimetre waves. Second, this thesis examines the resource allocation challenge in a multi-tenant multi-slice multi-tier heterogeneous network. To maximise the total utility of a multi-tenant multislice multi-tier heterogeneous network, a latency-aware dynamic resource allocation problem is formulated as an optimisation problem. Via the hierarchical decomposition method for heterogeneous networks, the formulated optimisation problem is transformed to reduce the computational complexities of the proposed solutions. Furthermore, a genetic algorithmbased latency-aware resource allocation scheme is proposed to solve the maximum utility problem by considering related constraints. It is observed that GI-LARE scheme outperforms the static slicing (SS) and an optimal resource allocation (ORA) schemes. Moreover, the GI-LARE appears to be near optimal when compared with an exact solution based on spatial branch and bound. Third, this thesis addresses a distributed resource allocation problem in a multi-slice multitier multi-domain network with different players. A three-level hierarchical business model comprising InPs, MVNOs, and service providers (SP) is examined. The radio resource allocation problem is formulated as a maximum utility optimisation problem. A multi-tier multi-domain slice user matching game and a distributed backtracking multi-player multidomain games schemes are proposed to solve the maximum utility optimisation problem. The distributed backtracking scheme is based on the Fisher Market and Auction theory principles. The proposed multi-tier multi-domain scheme outperforms the GI-LARE and the SS schemes. This is attributed to the availability of resources from other InPs and MVNOs; and the flexibility associated with a multi-domain network. Lastly, an energy-efficient resource allocation problem for 5G slice networks in a highly dense heterogeneous environment is investigated. A mathematical formulation of energy-efficient resource allocation in 5G slice networks is developed as a mixed-integer linear fractional optimisation problem (MILFP). The method adopts hierarchical decomposition techniques to reduce complexities. Furthermore, the slice user association, QoS for different slice use cases, an adapted water filling algorithm, and stochastic geometry tools are employed to xxi model the global energy efficiency (GEE) of the 5G slice network. Besides, neither stochastic geometry nor a three-level hierarchical business model schemes have been employed to model the global energy efficiency of the 5G slice network in the literature, making it the first time such method will be applied to 5G slice network. With rigorous numerical simulations based on Monte-Carlo numerical simulation technique, the performance of the proposed algorithms and schemes was evaluated to show their adaptability, efficiency and robustness for a 5G slice network

An intelligent call admission control algorithm for load balancing in 5G-satellite networks

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Cellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different RATs integrated. In addition to 5G, the user’s device (UD) will be able to connect to the network via LTE, WiMAX, Wi-Fi, Satellite, and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. Satellite networks are among the most sophisticated communication technologies which offer specific benefits in geographically dispersed and dynamic networks. Utilising their inherent advantages in broadcasting capabilities, global coverage, decreased dependency on terrestrial infrastructure, and high security, they offer highly efficient, effective, and rapid network deployments. Satellites are more suited for large-scale communications than terrestrial communication networks. Due to their extensive service coverage and strong multilink transmission capabilities, satellites offer global high-speed connectivity and adaptable access systems. The convergence of 5G technology and satellite networks therefore marks a significant milestone in the evolution of global connectivity. However, this integration introduces a complex problem related to resource management, particularly in Satellite – Terrestrial Integrated Networks (STINs). The key issue at hand is the efficient allocation of resources in STINs to enhance Quality of Service (QoS) for users. The root cause of this issue originates from a vast quantity of users sharing these resources, the dynamic nature of generated traffic, the scarcity of wireless spectrum resources, and the random allocation of wireless channels. Hence, resource allocation is critical to ensure user satisfaction, fair traffic distribution, maximised throughput, and minimised congestion. Achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This research endeavours to address this challenge through the development and evaluation of an intelligent call admission control (CAC) algorithm based on Enhanced Particle Swarm Optimization (EPSO). The primary aim of this research is to design an EPSO-based CAC algorithm tailored specifically for 5G-satellite heterogeneous wireless networks. The algorithm's objectives include maximising the number of admitted calls while maintaining Quality of Service (QoS) for existing users, improving network resource utilization, reducing congestion, ensuring fairness, and enhancing user satisfaction. To achieve these objectives, a detailed research methodology is outlined, encompassing algorithm development, numerical simulations, and comparative analysis. The proposed EPSO algorithm is benchmarked against alternative artificial intelligence and machine learning algorithms, including the Artificial Bee Colony algorithm, Simulated Annealing algorithm, and Q-Learning algorithm. Performance metrics such as throughput, call blocking rates, and fairness are employed to evaluate the algorithms' efficacy in achieving load-balancing objectives. The experimental findings yield insights into the performance of the EPSO-based CAC algorithm and its comparative advantages over alternative techniques. Through rigorous analysis, this research elucidates the EPSO algorithm's strengths in dynamically adapting to changing network conditions, optimising resource allocation, and ensuring equitable distribution of traffic among different RATs. The result shows the EPSO algorithm outperforms the other 3 algorithms in all the scenarios. The contributions of this thesis extend beyond academic research, with potential societal implications including enhanced connectivity, efficiency, and user experiences in 5G-Satellite heterogeneous wireless networks. By advancing intelligent resource management techniques, this research paves the way for improved network performance and reliability in the evolving landscape of wireless communication

Spectrum sharing and management techniques in mobile networks

Το φάσμα συχνοτήτων αποδεικνύεται σπάνιο κομμάτι για τους πόρους ενός κινητού δικτύου το οποίο πρέπει να ληφθεί υπόψιν στη σχεδίαση τηλεπικοινωνιακών συστημάτων 5ης γενιάς. Επιπλέον οι πάροχοι κινητών δικτύων θα πρέπει να επαναπροσδιορίσουν επιχειρησιακά μοντέλα τα οποία μέχρι τώρα δεν θεωρούνταν αναγκαία (π.χ., γνωσιακά ραδιοδίκτυα), ή να εξετάσουν την υιοθέτηση νέων μοντέλων που αναδεικνύονται (π.χ., αδειοδοτούμενη από κοινού πρόσβαση) ώστε να καλύψουν τις ολοένα αυξανόμενες ανάγκες για εύρος ζώνης. Ο μερισμός φάσματος θεωρείται αναπόφευκτος για συστήματα 5G και η διατριβή παρέχει λύση για προσαρμοστικό μερισμό φάσματος με πολλαπλά καθεστώτα εξουσιοδότησης, βάσει ενός καινοτόμου αρχιτεκτονικού πλαισίου το οποίο επιτρέπει στα δικτυακά στοιχεία να λαμβάνουν αποφάσεις για απόκτηση φάσματος. Η προτεινόμενη διαδικασία λήψης αποφάσεων είναι μία καινοτόμα τεχνική προσαρμοστικού μερισμού φάσματος βασιζόμενη σε ελεγκτές ασαφούς λογικής που καθορίζονν το καταλληλότερο σχήμα μερισμού φάσματος και σε ενισχυμένη μάθηση που ρυθμίζει τους κανόνες ασαφούς λογικής, στοχεύοντας να βρει τη βέλτιστη πολιτική που πρέπει να ακολουθεί ο πάροχος ώστε να προσφέρει την επιθυμητή ποιότητα υπηρεσιών στους χρήστες, διατηρώντας πόρους (οικονομικούς ή ραδιοπόρους) όπου είναι εφικτό. Η τελευταία συνεισφορά της διατριβής είναι ένας μηχανισμός που εξασφαλίζει δίκαιη πρόσβαση σε φάσμα ανάμεσα σε χρήστες σε σενάρια στα οποία η εκχώρηση άδειας χρήσης φάσματος δεν είναι προαπαιτούμενη.Radio spectrum has loomed out to be a scarce resource that needs to be carefully considered when designing 5G communication systems and Mobile Network Operators (MNOs) will need to revisit business models that were not of their prior interest (e.g. Cognitive Radio) or consider adopting new business models that emerge (e.g. Licensed Shared Access) so as to cover the extended capacity needs. Spectrum sharing is considered unavoidable for 5G systems and this thesis provides a solution for adaptive spectrum sharing under multiple authorization regimes based on a novel architecture framework that enables network elements to proceed in decisions for spectrum acquisition. The decision making process for spectrum acquisition proposed is a novel Adaptive Spectrum Sharing technique that uses Fuzzy Logic controllers to determine the most suitable spectrum sharing option and reinforcement learning to tune the fuzzy logic rules, aiming to find an optimal policy that MNO should follow in order to offer the desirable Quality of Service to its users, while preserving resources (either economical, or radio) when possible. The final contribution of this thesis is a mechanism that ensures fair access to spectrum among the users in scenarios in which conveying spectrum license is not prerequisite

AN EFFICIENT INTERFERENCE AVOIDANCE SCHEME FOR DEVICE-TODEVICE ENABLED FIFTH GENERATION NARROWBAND INTERNET OF THINGS NETWOKS’

Narrowband Internet of Things (NB-IoT) is a low-power wide-area (LPWA) technology built on long-term evolution (LTE) functionalities and standardized by the 3rd-Generation Partnership Project (3GPP). Due to its support for massive machine-type communication (mMTC) and different IoT use cases with rigorous standards in terms of connection, energy efficiency, reachability, reliability, and latency, NB-IoT has attracted the research community. However, as the capacity needs for various IoT use cases expand, the LTE evolved packet core (EPC) system's numerous functionalities may become overburdened and suboptimal. Several research efforts are currently in progress to address these challenges. As a result, an overview of these efforts with a specific focus on the optimized architecture of the LTE EPC functionalities, the 5G architectural design for NB-IoT integration, the enabling technologies necessary for 5G NB-IoT, 5G new radio (NR) coexistence with NB-IoT, and feasible architectural deployment schemes of NB-IoT with cellular networks is discussed. This thesis also presents cloud-assisted relay with backscatter communication as part of a detailed study of the technical performance attributes and channel communication characteristics from the physical (PHY) and medium access control (MAC) layers of the NB-IoT, with a focus on 5G. The numerous drawbacks that come with simulating these systems are explored. The enabling market for NB-IoT, the benefits for a few use cases, and the potential critical challenges associated with their deployment are all highlighted. Fortunately, the cyclic prefix orthogonal frequency division multiplexing (CPOFDM) based waveform by 3GPP NR for improved mobile broadband (eMBB) services does not prohibit the use of other waveforms in other services, such as the NB-IoT service for mMTC. As a result, the coexistence of 5G NR and NB-IoT must be manageably orthogonal (or quasi-orthogonal) to minimize mutual interference that limits the form of freedom in the waveform's overall design. As a result, 5G coexistence with NB-IoT will introduce a new interference challenge, distinct from that of the legacy network, even though the NR's coexistence with NB-IoT is believed to improve network capacity and expand the coverage of the user data rate, as well as improves robust communication through frequency reuse. Interference challenges may make channel estimation difficult for NB-IoT devices, limiting the user performance and spectral efficiency. Various existing interference mitigation solutions either add to the network's overhead, computational complexity and delay or are hampered by low data rate and coverage. These algorithms are unsuitable for an NB-IoT network owing to the low-complexity nature. As a result, a D2D communication based interference-control technique becomes an effective strategy for addressing this problem. This thesis used D2D communication to decrease the network bottleneck in dense 5G NBIoT networks prone to interference. For D2D-enabled 5G NB-IoT systems, the thesis presents an interference-avoidance resource allocation that considers the less favourable cell edge NUEs. To simplify the algorithm's computing complexity and reduce interference power, the system divides the optimization problem into three sub-problems. First, in an orthogonal deployment technique using channel state information (CSI), the channel gain factor is leveraged by selecting a probable reuse channel with higher QoS control. Second, a bisection search approach is used to find the best power control that maximizes the network sum rate, and third, the Hungarian algorithm is used to build a maximum bipartite matching strategy to choose the optimal pairing pattern between the sets of NUEs and the D2D pairs. The proposed approach improves the D2D sum rate and overall network SINR of the 5G NB-IoT system, according to the numerical data. The maximum power constraint of the D2D pair, D2D's location, Pico-base station (PBS) cell radius, number of potential reuse channels, and cluster distance impact the D2D pair's performance. The simulation results achieve 28.35%, 31.33%, and 39% SINR performance higher than the ARSAD, DCORA, and RRA algorithms when the number of NUEs is twice the number of D2D pairs, and 2.52%, 14.80%, and 39.89% SINR performance higher than the ARSAD, RRA, and DCORA when the number of NUEs and D2D pairs are equal. As a result, a D2D sum rate increase of 9.23%, 11.26%, and 13.92% higher than the ARSAD, DCORA, and RRA when the NUE’s number is twice the number of D2D pairs, and a D2D’s sum rate increase of 1.18%, 4.64% and 15.93% higher than the ARSAD, RRA and DCORA respectively, with an equal number of NUEs and D2D pairs is achieved. The results demonstrate the efficacy of the proposed scheme. The thesis also addressed the problem where the cell-edge NUE's QoS is critical to challenges such as long-distance transmission, delays, low bandwidth utilization, and high system overhead that affect 5G NB-IoT network performance. In this case, most cell-edge NUEs boost their transmit power to maximize network throughput. Integrating cooperating D2D relaying technique into 5G NB-IoT heterogeneous network (HetNet) uplink spectrum sharing increases the system's spectral efficiency and interference power, further degrading the network. Using a max-max SINR (Max-SINR) approach, this thesis proposed an interference-aware D2D relaying strategy for 5G NB-IoT QoS improvement for a cell-edge NUE to achieve optimum system performance. The Lagrangian-dual technique is used to optimize the transmit power of the cell-edge NUE to the relay based on the average interference power constraint, while the relay to the NB-IoT base station (NBS) employs a fixed transmit power. To choose an optimal D2D relay node, the channel-to-interference plus noise ratio (CINR) of all available D2D relays is used to maximize the minimum cell-edge NUE's data rate while ensuring the cellular NUEs' QoS requirements are satisfied. Best harmonic mean, best-worst, half-duplex relay selection, and a D2D communication scheme were among the other relaying selection strategies studied. The simulation results reveal that the Max-SINR selection scheme outperforms all other selection schemes due to the high channel gain between the two communication devices except for the D2D communication scheme. The proposed algorithm achieves 21.27% SINR performance, which is nearly identical to the half-duplex scheme, but outperforms the best-worst and harmonic selection techniques by 81.27% and 40.29%, respectively. As a result, as the number of D2D relays increases, the capacity increases by 14.10% and 47.19%, respectively, over harmonic and half-duplex techniques. Finally, the thesis presents future research works on interference control in addition with the open research directions on PHY and MAC properties and a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis presented in Chapter 2 to encourage further study on 5G NB-IoT

Satellite Communications

This study is motivated by the need to give the reader a broad view of the developments, key concepts, and technologies related to information society evolution, with a focus on the wireless communications and geoinformation technologies and their role in the environment. Giving perspective, it aims at assisting people active in the industry, the public sector, and Earth science fields as well, by providing a base for their continued work and thinking

Räumliche Optimierung der Bestandesstruktur unter Berücksichtigung von Einzelbaumeffekten

In dieser Dissertation werden erstmals Kenntnisse über ökologische Felder von Einzelbäumen mit Methoden der räumlichen Optimierung kombiniert, um ein Werkzeug zu schaffen, mit dem Empfehlungen für die Strukturierung von Beständen erarbeiten werden können. Dabei waren drei unterschiedliche waldbauliche Problemstellungen Ausgangspunkt der Arbeit. Die ausführliche Beschreibung der Probleme führte zur Ableitung eines allgemeinen Optimierungsproblems, das nach optimalen Stammverteilungsplänen bzgl. verschiedener, waldbaulicher Zielsetzungen sucht. Der erster Schwerpunkt war die mathematische Herleitung der Zielgrößen. Hierbei wurde die Idee der Einzelbaumeffekte und das Konzept der ökologischen Felder verwendet, um die Zielgrößen aus den Einzelbaumeffekten zu entwickeln. Der zweite Schwerpunkt umfasste die Suche nach einem geeigneten Optimierungsmodell, mit dem die Horizontalstruktur eines Bestandes basierend auf weitreichenden, stetigen Einzelbaumeffekten räumlich optimiert werden konnte. Der gegebene Überblick zum Stand der Forschung bzgl. der räumlichen Optimierung in der Forstwissenschaft zeigte auf, dass nur Teilaspekte des allgemeinen Optimierungsproblems bisher modelliert worden sind. Von den vier daraufhin neu entwickelten Optimierungsmodellen wurden ein kontinuierliches und ein diskretes Modells nach der Auswertung der Eigenschaften weiterverwendet. Die Bewertung von verschiedenen, vorgestellten Nachbarschaftsdefinitionen und Varianten von lokalen Suchverfahren, Meta- und Hybridheuristiken führte zur Verwendung von k-opt für das diskrete Optimierungsmodell, von Compass Search für das kontinuierliche Optimierungsmodell und von Threshold Accepting und Iterated Local Search für beide Modelle. Für alle drei Optimierungsprobleme wurden jeweils zwei Tests je Algorithmus mit einer in C++ implementierten Optimierungssoftware durchgeführt. Beim ersten Test sollten in kurzer Zeit wiederholt gute Lösungen berechnet werden, während im zweiten Test wesentlich mehr Funktionswertberechnungen zur Verfügung standen, um eine sehr gute Lösung zu erhalten. Die Auswertung der Testrechnungen zeigte, dass das diskrete Optimierungsmodell dem kontinuierlichen Modell außer bei einem geringen Bestockungsgrad des Bestandes vorzuziehen ist. Die Zielfunktionsdefinitionen hatten wesentlichen Einfluss auf die Lösungen, vor allem bei gegenläufigen Zielen. Sehr gute Lösungen wiesen dabei charakteristische Verteilungsschemata der Baumpositionen auf, die nur durch eine Optimierung und nicht durch das wiederholte, zufällige Verteilen von Bäumen gefunden werden konnten. Für das diskrete Modell lieferte Threshold Accepting vor 2-opt und Iterated Local Search fast immer die besten Ergebnisse. 4-opt war immer deutlich schlechter als die anderen Algorithmen. Threshold Accepting berechnete sowohl sehr schnell gute Lösungen und als auch die besten Lösungen, wenn eine intensive Suche mit sehr vielen Funktionswertberechnungen möglich war

Massachusetts Domestic and Foreign Corporations Subject to an Excise: For the Use of Assessors (2004)

International audienc