80 research outputs found

    Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

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    Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

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

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    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

    Matching theory for priority-based cell association in the downlink of wireless small cell networks

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    The deployment of small cells, overlaid on existing cellular infrastructure, is seen as a key feature in next-generation cellular systems. In this paper, the problem of user association in the downlink of small cell networks (SCNs) is considered. The problem is formulated as a many-to-one matching game in which the users and SCBSs rank one another based on utility functions that account for both the achievable performance, in terms of rate and fairness to cell edge users, as captured by newly proposed priorities. To solve this game, a novel distributed algorithm that can reach a stable matching is proposed. Simulation results show that the proposed approach yields an average utility gain of up to 65% compared to a common association algorithm that is based on received signal strength. Compared to the classical deferred acceptance algorithm, the results also show a 40% utility gain and a more fair utility distribution among the users.Comment: 5 page

    Improving power and resource management in heterogeneous downlink OFDMA networks

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    © 2020 by the authors. In the past decade, low power consumption schemes have undergone degraded communication performance, where they fail to maintain the trade-off between the resource and power consumption. In this paper, management of resource and power consumption on small cell orthogonal frequency-division multiple access (OFDMA) networks is enacted using the sleep mode selection method. The sleep mode selection method uses both power and resource management, where the former is responsible for a heterogeneous network, and the latter is managed using a deactivation algorithm. Further, to improve the communication performance during sleep mode selection, a semi-Markov sleep mode selection decision-making process is developed. Spectrum reuse maximization is achieved using a small cell deactivation strategy that potentially identifies and eliminates the sleep mode cells. The performance of this hybrid technique is evaluated and compared against benchmark techniques. The results demonstrate that the proposed hybrid performance model shows effective power and resource management with reduced computational cost compared with benchmark techniques

    Adaptive management of cognitive radio networks employing femtocells

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    Network planning and management are challenging issues in a two-tier network. Tailoring to cognitive radio networks (CRNs), network operations and transmissions become more challenging due to the dynamic spectrum availability. This paper proposes an adaptive network management system that provides switching between different CRN management structures in response to the spectrum availability and changes in the service time required for the radio access. The considered network management system includes conventional macrocell-only structure, and centralized/distributed structures overlaid with femtocells. Furthermore, analytical expressions of per-tier successful connection probability and throughput are provided to characterize the network performance for different network managements. Spectrum access in dynamic radio environments is formulated according to the quality of service (QoS) constraint that is related to the connection probability and outage probability. Results show that the proposed intelligent network management system improves the maximum capacity and reduces the number of blocked connections by adapting between various network managements in response to free spectrum transmission slots. A road map for the deployment and management of cognitive macro/femto networks is also presented

    Improving fractional frequency reuse (FFR) for interference mitigation in Multi-tier 4G wireless networks

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    Includes bibliography.The need to provide quality indoor coverage for mobile network users in an indoor environment has become paramount to communication service providers (CSPs). Femto-cells due to their low capital expenditure (CAPEX) and operating expenditure (OPEX) have seen widespread adoption as a possible solution to the indoor coverage challenge. The major drawback of its adoption is the possibility of erratic but significant interference to both the Femto-cell and the Macro-cell tiers owing to their Ad-hoc mode of deployment. The Fractional Frequency Reuse (FFR) is an interference mitigation scheme, due to its effectiveness and low complexity; it has been proposed to be an efficient technique of solving the problem of interference in the cross-boundary region. In this study, a critical analysis of the existing schemes revealed that Femto-cell users at the border between the cell centre region (CCR) and the cell edge region (CER) suffer cross-boundary interference. An algorithm that integrates a buffer zone between the existing CCR and CER has been developed to solve the cross-boundary interference challenge experienced by the Femto-cell users. A system level simulation implemented in MATLAB was used to evaluate the developed algorithm. The network performance (in terms of user-achieved signal-to-interference-plus-noise ratio (SINR) and its daughter metrics such as channel capacity and throughput) was estimated. In terms of the SINR, the performance improvement recorded for Femto-cell users at the border region after the implementation of the buffer zone was more than eighty per cent (80%). There were significant improvements in terms of the channel capacity and throughput for the Femto-users present at the buffer region with the implementation of the developed algorithm
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