Resource Allocation and Service Management in Next Generation 5G Wireless Networks

The accelerated evolution towards next generation networks is expected to dramatically increase mobile data traffic, posing challenging requirements for future radio cellular communications. User connections are multiplying, whilst data hungry content is dominating wireless services putting significant pressure on network's available spectrum. Ensuring energy-efficient and low latency transmissions, while maintaining advanced Quality of Service (QoS) and high standards of user experience are of profound importance in order to address diversifying user prerequisites and ensure superior and sustainable network performance. At the same time, the rise of 5G networks and the Internet of Things (IoT) evolution is transforming wireless infrastructure towards enhanced heterogeneity, multi-tier architectures and standards, as well as new disruptive telecommunication technologies. The above developments require a rethinking of how wireless networks are designed and operate, in conjunction with the need to understand more holistically how users interact with the network and with each other. In this dissertation, we tackle the problem of efficient resource allocation and service management in various network topologies under a user-centric approach. In the direction of ad-hoc and self-organizing networks where the decision making process lies at the user level, we develop a novel and generic enough framework capable of solving a wide array of problems with regards to resource distribution in an adaptable and multi-disciplinary manner. Aiming at maximizing user satisfaction and also achieve high performance - low power resource utilization, the theory of network utility maximization is adopted, with the examined problems being formulated as non-cooperative games. The considered games are solved via the principles of Game Theory and Optimization, while iterative and low complexity algorithms establish their convergence to steady operational outcomes, i.e., Nash Equilibrium points. This thesis consists a meaningful contribution to the current state of the art research in the field of wireless network optimization, by allowing users to control multiple degrees of freedom with regards to their transmission, considering mobile customers and their strategies as the key elements for the amelioration of network's performance, while also adopting novel technologies in the resource management problems. First, multi-variable resource allocation problems are studied for multi-tier architectures with the use of femtocells, addressing the topic of efficient power and/or rate control, while also the topic is examined in Visible Light Communication (VLC) networks under various access technologies. Next, the problem of customized resource pricing is considered as a separate and bounded resource to be optimized under distinct scenarios, which expresses users' willingness to pay instead of being commonly implemented by a central administrator in the form of penalties. The investigation is further expanded by examining the case of service provider selection in competitive telecommunication markets which aim to increase their market share by applying different pricing policies, while the users model the selection process by behaving as learning automata under a Machine Learning framework. Additionally, the problem of resource allocation is examined for heterogeneous services where users are enabled to dynamically pick the modules needed for their transmission based on their preferences, via the concept of Service Bundling. Moreover, in this thesis we examine the correlation of users' energy requirements with their transmission needs, by allowing the adaptive energy harvesting to reflect the consumed power in the subsequent information transmission in Wireless Powered Communication Networks (WPCNs). Furthermore, in this thesis a fresh perspective with respect to resource allocation is provided assuming real life conditions, by modeling user behavior under Prospect Theory. Subjectivity in decisions of users is introduced in situations of high uncertainty in a more pragmatic manner compared to the literature, where they behave as blind utility maximizers. In addition, network spectrum is considered as a fragile resource which might collapse if over-exploited under the principles of the Tragedy of the Commons, allowing hence users to sense risk and redefine their strategies accordingly. The above framework is applied in different cases where users have to select between a safe and a common pool of resources (CPR) i.e., licensed and unlicensed bands, different access technologies, etc., while also the impact of pricing in protecting resource fragility is studied. Additionally, the above resource allocation problems are expanded in Public Safety Networks (PSNs) assisted by Unmanned Aerial Vehicles (UAVs), while also aspects related to network security against malign user behaviors are examined. Finally, all the above problems are thoroughly evaluated and tested via a series of arithmetic simulations with regards to the main characteristics of their operation, as well as against other approaches from the literature. In each case, important performance gains are identified with respect to the overall energy savings and increased spectrum utilization, while also the advantages of the proposed framework are mirrored in the improvement of the satisfaction and the superior Quality of Service of each user within the network. Lastly, the flexibility and scalability of this work allow for interesting applications in other domains related to resource allocation in wireless networks and beyond

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments

Game theoretical models for clustering and resource sharing in macro-femtocells networks

One of the main challenges of cellular network operators is to keep a good network quality for their users. In most cases, network quality decreases in indoor environments causing users to switch from one operator to another. A promising solution to cope with this issue is the deployment of femtocells that are used mainly at homes to enhance the mobile network coverage. In fact, higher penetration of broadband and mobile phones with high requirements of new applications such as video conferencing and internet games are promoting femtocell market. However, the deployment of femtocells in existing macrocell networks is a very challenging task due to the high complexity of the resource allocation. In this thesis, we focus on proposing several solutions to address the resource allocation problem in macro-femtocell networks with dense deployment of femtocells based on clustering techniques. Clustering techniques are used to reduce the resource allocation complexity of dense-femtocell networks since the resources are allocated locally within each cluster. Furthermore, a cluster head is responsible for the allocation of resources to femtocells within the cluster which avoids the co-tier interference. The clustering techniques have been widely used for distributed resource allocation in heterogeneous networks through the use of game theory models. In this work, three distributed resource allocation algorithms based on cooperative and evolutionary games are proposed. In the first part, we discuss the resource allocation problem for the non-dense deployment of femtocells. Toward this goal, a coalitional game is used to incentive femtocells in the formation of clusters. The approach decomposes in: (i) a base station selection algorithm for public users, (ii) a clustering algorithm based on cooperative game theory and (iii) a resource allocation within each cluster based on the PSO technique. Besides, an interference control mechanism enabled femtocells to leave its current cluster when the interference levels are higher than an interference threshold. In the second part, we focus on a fair allocation of resources for macro-femtocell networks. We develop a clustering algorithm based on a cooperative game for non-dense femtocell network. The Shapley value is applied to find the marginal contribution of every femtocell to all the possible groups of femtocells, thus, finding the fair amount of resources to be allocated to each femtocell within a cluster. This solution is only applied for non-dense femtocell deployment due to that the complexity of calculating the Shapley value increases significantly with a large number of femtocells. Stability criteria based on the ε-concept of game theory is utilized to find the set of stable clusters. Finally, the analysis of the resource allocation for dense-femtocell deployment is addressed through an evolutionary game theory (EGT) model. It is assumed that EGT requires bounded rationality from players, this reduces the complexity and allows the dense deployment of femtocells. In addition, we demonstrate that the set of clusters formed with EGT are stable by means of the replicator dynamics. The proposed model also includes system analysis for users with low mobility such as pedestrians and cyclists

Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends

Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios

Optimization models for resource management in two-tier cellular networks

Macro-femtocell network is the most promising two-tier architecture for the cellular network operators because it can improve their current network capacity without additional costs. Nevertheless, the incorporation of femtocells to the existing cellular networks needs to be finely tuned in order to enhance the usage of the limited wireless resources, because the femtocells operate in the same spectrum as the macrocell. In this thesis, we address the resource optimization problem for the OFDMA two-tier networks for scenarios where femtocells are deployed using hybrid access policy. The hybrid access policy is a technique that could provide different levels of service to authorized users and visitors to the femtocell. This method reduces interference received by femtocell subscribers by granting access to nearby public users. These approaches should find a compromise between the level of access granted to public users and the impact on the subscribers satisfaction. This impact should be reduced in terms of performance or through economic compensation. In this work, two specific issues of an OFDMA two-tier cellular network are addressed. The first is the trade-off between macrocell resource usage efficiency and the fairness of the resource distribution among macro mobile users and femtocells. The second issue is the compromise between interference mitigation and granting access to public users without depriving the subscriber downlink transmissions. We tackle these issues by developing several resource allocation models for non-dense and dense femtocell deployment using Linear Programming and one evolutionary optimization method. In addition, the proposed resource allocation models determine the best suitable serving base station together with bandwidth and transmitted power per user in order to enhance the overall network capacity. The first two parts of this work cope with the resource optimization for non-dense deployment using orthogonal and co-channel allocation. Both parts aim at the maximization of the sum of the weighted user data rates. In the first part, several set of weights are introduced to prioritize the use of femtocells for subscribers and public users close to femtocells. In addition, macrocell power control is incorporated to enhance the power distribution among the active downlink transmissions and to improve the tolerance to the environmental noise. The second part enables the spectral reuse and the power adaptation is a three-folded solution that enhances the power distribution over the active downlink transmissions, improves the tolerance to the environmental noise and a given interference threshold, and achieves the target Quality of Service (QoS). To reduce the complexity of the resource optimization problem for dense deployment, the third part of this work divides the optimization problem into subproblems. The main idea is to divide the user and FC sets into disjoint sets taking into account their locations. Thus, the optimization problem can be solved independently in each OFDMA zone. This solution allows the subcarriers reuse among inner macrocell zones and femtocells located in outer macrocell zones and also between femtocells belonging to different clusters if they are located in the same zone. Macrocell power control is performed to avoid the cross-tier interference among macrocell inner zones and inside femtocells located in outer zones. Another well known method used to reduce the complexity of the resource optimization problem is the femtocell clustering. However, finding the optimal cluster configuration together with the resource allocation is a complex optimization problem due to variable number related to the possible cluster configurations. Therefore, the part four of this work deals with a heuristic cluster based resource allocation model and a motivation scheme for femtocell clustering through the allocation of extra resources for subscriber and “visitor user” transmissions. The cluster based resource allocation model maximizes the network throughput while keeping balanced clusters and minimizing the inter-cluster interference. Finally, the proposed solutions are evaluated through extensive numerical simulations and the numerical results are presented to provide a comparison with the related works found in the literature