Generalised Radio Resource Sharing Framework for Heterogeneous Radio Networks

Recent years have seen a significant interest in quantitative measurements of licensed and unlicensed spectrum use. Several research groups, companies and regulatory bodies have conducted studies of varying times and locations with the aim to capture the over- all utilisation rate of spectrum. The studies have shown that large amount of allocated spectrum are under-utilised, and create the so called \spectrum holes", resulting in a waste of valuable frequency resources. In order to satisfy the requirements of increased demands of spectrum resources and to improve spectrum utilisation, dynamic spectrum sharing (DSS) is proposed in the literature along with cognitive radio networks (CRNs). DSS and CRNs have been studied from many perspectives, for example spectrum sensing to identify the idle channels has been under the microscope to improve detection proba- bility. As well as spectrum sensing, the DSS performance analysis remains an important topic moving towards better spectrum utilisation to meet the exponential growth of traffi�c demand. In this dissertation we have studied both techniques to achieve different objectives such as enhancing the probability of detection and spectrum utilisation. In order to improve spectrum sensing decisions we have proposed a cooperative spec- trum sensing scheme which takes the propagation conditions into consideration. The proposed location aware scheme shows an improved performance over conventional hard combination scheme, highlighting the requirements of location awareness in cognitive radio networks (CRNs). Due to the exponentially growing wireless applications and services, traffi�c demand is increasing rapidly. To cope with such growth wireless network operators seek radio resource cooperation strategies for their users with the highest possible grade of service (GoS). However, it is diffi�cult to fathom the potential benefits of such cooperation, thus we propose a set of analytical models for DSS to analyse the blocking probability gain and degradation for operators. The thesis focuses on examining the performance gains that DSS can entail, in different scenarios. A number of dynamic spectrum sharing scenarios are proposed. The proposed models focus on measuring the blocking probability of secondary network operators as a trade-o� with a marginal increase of the blocking probability of a primary network in return of monetary rewards. We derived the global balance equation and an explicit expression of the blocking probability for each model. The robustness of the proposed analytical models is evaluated under different scenarios by considering varying tra�c intensities, different network sizes and adding reserved resources (or pooled capacity). The results show that the blocking probabilities can be reduced significantly with the proposed analytical DSS models in comparison to the existing local spectrum access schemes. In addition to the sharing models, we further assume that the secondary operator aims to borrow spectrum bandwidths from primary operators when more spectrum resources available for borrowing than the actual demand considering a merchant mode. Two optimisation models are proposed using stochastic optimisation models in which the secondary operator (i) spends the minimum amount of money to achieve the target GoS assuming an unrestricted budget or (ii) gains the maximum amount of pro�t to achieve the target GoS assuming restricted budget. Results obtained from each model are then compared with results derived from algorithms in which spectrum borrowings were random. Comparisons showed that the gain in the results obtained from our pro- posed stochastic optimisation model is significantly higher than heuristic counterparts. A post-optimisation performance analysis of the operators in the form of analysis of blocking probability in various scenarios is investigated to determine the probable per- formance gain and degradation of the secondary and primary operators respectively. We mathematically model the sharing agreement scenario and derive the closed form solution of blocking probabilities for each operator. Results show how the secondary and primary operators perform in terms of blocking probability under various offered loads and sharing capacity. The simulation results demonstrate that at most trading windows, the proposed opti- mal algorithms outperforms their heuristic counterparts. When we consider 80 cells, the proposed pro�t maximisation algorithm results in 33.3% gain in net pro�t to the secondary operators as well as facilitating 2.35% more resources than the heuristic ap- proach. In addition, the cost minimisation algorithm results in 46.34% gain over the heuristic algorithm when considering the same number of cells (80)

Spectrum Sharing Optimization and Analysis in Cellular Networks under Target Performance and Budget Restriction

Dynamic Spectrum Sharing (DSS) aims to provide opportunistic access to under-utilised spectrum in cellular networks for secondary network operators. In this paper we propose an algorithm using stochastic and optimisation models to borrow spectrum bandwidths under the assumption that more resources exist for secondary access than the secondary network demand by considering a merchant mode. The main aim of the paper is to address the problem of spectrum borrowing in DSS environments, where a secondary network operator aims to borrow the required spectrum from multiple primary network operators to achieve a maximum profit under specific grade of service (GoS) and budget restriction. We assume that the primary network operators offer spectrum access opportunities with variable number of channels (contiguous and/or non-contiguous) at variable prices. Results obtained are then compared with results derived from an algorithm in which spectrum borrowing are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimisation framework is significantly higher than random counterpart

NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks

With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)

Optimal Auctions in Oligopoly Spectrum Market with Concealed Cost

This paper presents a mathematical approach to the future dynamic spectrum market, where multiple secondary operators compete to gain radio resources. The secondary network operators (SNOs) face various concurrent auctions. We discuss techniques, which can be used to select auctions to optimize their objectives and increase the winning probability. To achieve these goals, a matching problem is formulated and solved, where secondary operators are paired with auctions, which can provide spectrum with the highest expected quality of service (QoS). A total outlay optimization is structured for auctions with concealed reserve prices, which are only revealed to the secondary operators for some price upon request. More specifically, we solve a nonlinear problem to determine the minimum set of auctions by using the brute force algorithm. We further introduce a surplus maximization and demonstrate an auction mechanism of spectrum allocation by modifying the Bayesian-Nash equilibrium. The mathematical analyses highlight that the optimal choice is achievable through the proposed mathematical formulation

Optimal Auctions in Oligopoly Spectrum Market with Concealed Cost

This paper presents a mathematical approach to the future dynamic spectrum market, where multiple secondary operators compete to gain radio resources. The secondary network operators (SNOs) face various concurrent auctions. We discuss techniques, which can be used to select auctions to optimize their objectives and increase the winning probability. To achieve these goals, a matching problem is formulated and solved, where secondary operators are paired with auctions, which can provide spectrum with the highest expected quality of service (QoS). A total outlay optimization is structured for auctions with concealed reserve prices, which are only revealed to the secondary operators for some price upon request. More specifically, we solve a nonlinear problem to determine the minimum set of auctions by using the brute force algorithm. We further introduce a surplus maximization and demonstrate an auction mechanism of spectrum allocation by modifying the Bayesian-Nash equilibrium. The mathematical analyses highlight that the optimal choice is achievable through the proposed mathematical formulation

Dynamic Spectrum Sharing Optimization and Post-optimization Analysis with Multiple Operators in Cellular Networks

Dynamic spectrum sharing aims to provide secondary access to under-utilised spectrum in cellular networks. The main aim of the paper is twofold. Firstly, secondary operator aims to borrow spectrum bandwidths under the assumption that more spectrum resources exist considering a merchant mode. Two optimization models are proposed using stochastic and optimization models in which the secondary operator (i) spends the minimal cost to achieve the target grade of service assuming unrestricted budget or (ii) gains the maximal profit to achieve the target grade of service assuming restricted budget. Results obtained from each model are then compared with results derived from algorithms in which spectrum borrowings are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than heuristic counterparts. Secondly, post-optimization performance analysis of the operators in the form of blocking probability in various scenarios is investigated to determine the probable performance gain and degradation of the secondary and primary operators respectively. We mathematically model the sharing agreement scenario and derive the closed form solution of blocking probabilities for each operator. Results show how the secondary operator perform in terms of blocking probability under various offered loads and sharing capacit

Dynamic Spectrum Sharing Optimization and Post-optimization Analysis with Multiple Operators in Cellular Networks

Dynamic spectrum sharing aims to provide secondary access to under-utilized spectrum in cellular networks. The main aim of the paper is twofold. Firstly, secondary operator aims to borrow spectrum bandwidths under the assumption that more spectrum resources exist considering a merchant mode. Two optimization models are proposed using stochastic and optimization models in which the secondary operator (i) spends the minimal cost to achieve the target grade of service assuming unrestricted budget or (ii) gains the maximal profit to achieve the target grade of service assuming restricted budget. Results obtained from each model are then compared with results derived from algorithms in which spectrum borrowings are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than heuristic counterparts. Secondly, post-optimization performance analysis of the operators in the form of blocking probability in various scenarios is investigated to determine the probable performance gain and degradation of the secondary and primary operators respectively. We mathematically model the sharing agreement scenario and derive the closed form solution of blocking probabilities for each operator. Results show how the secondary operator perform in terms of blocking probability under various offered loads and sharing capacity

Radio Resource Sharing Framework for Cooperative Multi-operator Networks with Dynamic Overflow Modelling

Due to the exponentially growing wireless services and application demand as well as the heterogeneity of the same, wireless network operators are expected to be seeking for radio resource co-operation strategies to the user group with the highest possible quality of experience (QoE). In this paper we have proposed an analytical framework for dynamic spectrum access (DSA) to adopt such cooperation within intra-network as well as inter-network operators scenarios, while sharing radio resources; assuming radio resource sharing agreement is in place. The proposed model focused onto reducing global blocking probability within a given geographical area to attain wireless services as a trade-off with increased blocking probability within local (individual network operator specific) network blocking probability; yet lower than the acceptable threshold. We derived the global balance equation and found an explicit expression of the blocking probability for each resource sharing model presented in this paper. The robustness of the proposed analytical framework is evaluated under three application specific scenarios considering various traffic intensity on demand as well as a set of global reserved resources (within one of the application specific scenarios). The results show that within a geographical area, the blocking probabilities can be reduced up to 60% with the proposed DSA framework in comparison to the existing local spectrum access schemes

Optimized Resource Sharing for Federated Cloud Services with Desired Performance and Limited OpEx

The provision of cloud resources to meet user demands in 5G wireless networks is a challenging task due to the high workload predicted to be experienced by cloud service providers (CSPs). Cloud federation has emerged as a paradigm to support CSPs with resource limitations by borrowing surplus resources of other CSPs in periods of high demands. The major concern of each CSP with resource limitations is to borrow resources from other federation participants in such a way that cloud services are provided to the end-users with a desired grade of service (GoS) as well as the overall profit is maximized. This paper proposes an efficient mechanism based on the merchant mode to dynamically facilitate optimal allocation of cloud resources, maximizing the profit of CSPs as well as improving the GoS. The robustness of the proposed optimal scheme is evaluated by comparing it with the heuristic algorithm. The simulation results demonstrate that at each trading window, the proposed optimal scheme outperforms its heuristic counterpart. Moreover, after 50 trading windows, the proposed approach results in 43.5% gain in net profit to CSPs as well as facilitating 3.35% of additional resource

ON THE LOCATION-AWARE COOPERATIVE SPECTRUM SENSING IN URBAN ENVIRONMENT

Spectrum sensing is a key enabling technology for cognitive radio networks (CRNs). The main objective of spectrum sensing is to provide more spectrum access opportunities to cognitive radio users without interfering with the operations of the licensed network. Spectrum sensing decisions can lead to erroneous sensing with low performance due to fading, shadowing and other interferences caused by either terrain inconsistency or dense urban structure. In order to improve spectrum sensing decisions, in this paper a cooperative spectrum sensing scheme is proposed. The propagation conditions such as the variance and intensity of terrain and urban structure between two points with respect to signal propagation are taken into consideration. We have also derived the optimum fusion rule which accounts for location reliability of secondary users (SUs). The analytical results show that the proposed scheme slightly outperforms the conventional cooperative spectrum sensing approaches