An Analytical Framework for Heterogeneous Partial Feedback Design in Heterogeneous Multicell OFDMA Networks

The inherent heterogeneous structure resulting from user densities and large scale channel effects motivates heterogeneous partial feedback design in heterogeneous networks. In such emerging networks, a distributed scheduling policy which enjoys multiuser diversity as well as maintains fairness among users is favored for individual user rate enhancement and guarantees. For a system employing the cumulative distribution function based scheduling, which satisfies the two above mentioned desired features, we develop an analytical framework to investigate heterogeneous partial feedback in a general OFDMA-based heterogeneous multicell employing the best-M partial feedback strategy. Exact sum rate analysis is first carried out and closed form expressions are obtained by a novel decomposition of the probability density function of the selected user's signal-to-interference-plus-noise ratio. To draw further insight, we perform asymptotic analysis using extreme value theory to examine the effect of partial feedback on the randomness of multiuser diversity, show the asymptotic optimality of best-1 feedback, and derive an asymptotic approximation for the sum rate in order to determine the minimum required partial feedback.Comment: To appear in IEEE Trans. on Signal Processin

Spectral Efficiency of Multi-User Adaptive Cognitive Radio Networks

In this correspondence, the comprehensive problem of joint power, rate, and subcarrier allocation have been investigated for enhancing the spectral efficiency of multi-user orthogonal frequency-division multiple access (OFDMA) cognitive radio (CR) networks subject to satisfying total average transmission power and aggregate interference constraints. We propose novel optimal radio resource allocation (RRA) algorithms under different scenarios with deterministic and probabilistic interference violation limits based on a perfect and imperfect availability of cross-link channel state information (CSI). In particular, we propose a probabilistic approach to mitigate the total imposed interference on the primary service under imperfect cross-link CSI. A closed-form mathematical formulation of the cumulative density function (cdf) for the received signal-to-interference-plus-noise ratio (SINR) is formulated to evaluate the resultant average spectral efficiency (ASE). Dual decomposition is utilized to obtain sub-optimal solutions for the non-convex optimization problems. Through simulation results, we investigate the achievable performance and the impact of parameters uncertainty on the overall system performance. Furthermore, we present that the developed RRA algorithms can considerably improve the cognitive performance whilst abide the imposed power constraints. In particular, the performance under imperfect cross-link CSI knowledge for the proposed `probabilistic case' is compared to the conventional scenarios to show the potential gain in employing this scheme

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

Self-organising network management for heterogeneous LTE-advanced networks

This thesis was submitted for the award of Doctor of Philosophy and awarded by Brunel University LondonSince 2004, when the Long Term Evolution (LTE) was first proposed to be publicly available in the year 2009, a plethora of new characteristics, techniques and applications have been constantly enhancing it since its first release, over the past decade. As a result, the research aims for LTE-Advanced (LTE-A) have been released to create a ubiquitous and supportive network for mobile users. The incorporation of heterogeneous networks (HetNets) has been proposed as one of the main enhancements of LTE-A systems over the existing LTE releases, by proposing the deployment of small-cell applications, such as femtocells, to provide more coverage and quality of service (QoS) within the network, whilst also reducing capital expenditure. These principal advantages can be obtained at the cost of new challenges such as inter-cell interference, which occurs when different network applications share the same frequency channel in the network. In this thesis, the main challenges of HetNets in LTE-A platform have been addressed and novel solutions are proposed by using self-organising network (SON) management approaches, which allows the cooperative cellular systems to observe, decide and amend their ongoing operation based on network conditions. The novel SON algorithms are modelled and simulated in OPNET modeler simulation software for the three processes of resource allocation, mobility management and interference coordination in multi-tier macro-femto networks. Different channel allocation methods based on cooperative transmission, frequency reuse and dynamic spectrum access are investigated and a novel SON sub-channel allocation method is proposed based on hybrid fractional frequency reuse (HFFR) scheme to provide dynamic resource allocation between macrocells and femtocells, while avoiding co-tier and cross-tier interference. Mobility management is also addressed as another important issue in HetNets, especially in hand-ins from macrocell to femtocell base stations. The existing research considers a limited number of methods for handover optimisation, such as signal strength and call admission control (CAC) to avoid unnecessary handovers, while our novel SON handover management method implements a comprehensive algorithm that performs sensing process, as well as resource availability and user residence checks to initiate the handover process at the optimal time. In addition to this, the novel femto over macro priority (FoMP) check in this process also gives the femtocell target nodes priority over the congested macrocells in order to improve the QoS at both the network tiers. Inter-cell interference, as the key challenge of HetNets, is also investigated by research on the existing time-domain, frequency-domain and power control methods. A novel SON interference mitigation algorithm is proposed, which is based on enhanced inter-cell interference coordination (eICIC) with power control process. The 3-phase power control algorithm contains signal to interference plus noise ratio (SINR) measurements, channel quality indicator (CQI) mapping and transmission power amendments to avoid the occurrence of interference due to the effects of high transmission power. The results of this research confirm that if heterogeneous systems are backed-up with SON management strategies, not only can improve the network capacity and QoS, but also the new network challenges such as inter-cell interference can also be mitigated in new releases of LTE-A network