GAME THEORETIC APPROACH TO RADIO RESOURCE MANAGEMENT ON THE REVERSE LINK FOR MULTI-RATE CDMA WIRELESS DATA NETWORKS

This work deals with efficient power and rate assignment to mobile stations (MSs) involved in bursty data transmission in cellular CDMA networks. Power control in the current CDMA standards is based on a fixed target signal quality called signal to interference ratio (SIR). The target SIR represents a predefined frame error rate (FER). This approach is inefficient for data-MSs because a fixed target SIR can limit the MS's throughput. Power control should thus provide dynamic target SIRs instead of a fixed target SIR. In the research literature, the power control problem has been modeled using game theory. A limitation of the current literature is that in order to implement the algorithms, each MS needs to know information such as path gains and transmission rates of all other MSs. Fast rate control schemes in the evolving cellular data systems such as cdma2000-1x-EV assign transmission rates to MSs using a probabilistic approach. The limitation here is that the radio resources can be either under or over-utilized. Further, all MSs are not assigned the same rates. In the schemes proposed in the literature, only few MSs, which have the best channel conditions, obtain all radio resources. In this dissertation, we address the power control issue by moving the computation of the Nash equilibrium from each MS to the base station (BS). We also propose equal radio resource allocation for all MSs under the constraint that only the maximum allowable radio resources are used in a cell. This dissertation addresses the problem of how to efficiently assign power and rate to MSs based on dynamic target SIRs for bursty transmissions. The proposed schemes in this work maximize the throughput of each data-MS while still providing equal allocation of radio resources to all MSs and achieving full radio resource utilization in each cell. The proposed schemes result in power and rate control algorithms that however require some assistance from the BS. The performance evaluation and comparisons with cdma2000-1x-Evolution Data Only (1x-EV-DO) show that the proposed schemes can provide better effective rates (rates after error) than the existing schemes

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

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

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

Delay Sensitive Communications over Cognitive Radio Networks

Supporting the quality of service of unlicensed users in cognitive radio networks is very challenging, mainly due to dynamic resource availability because of the licensed users' activities. In this paper, we study the optimal admission control and channel allocation decisions in cognitive overlay networks in order to support delay sensitive communications of unlicensed users. We formulate it as a Markov decision process problem, and solve it by transforming the original formulation into a stochastic shortest path problem. We then propose a simple heuristic control policy, which includes a threshold-based admission control scheme and and a largest-delay-first channel allocation scheme, and prove the optimality of the largest-delay-first channel allocation scheme. We further propose an improved policy using the rollout algorithm. By comparing the performance of both proposed policies with the upper-bound of the maximum revenue, we show that our policies achieve close-to-optimal performance with low complexities.Comment: 11 pages, 8 figure

Price-Based Optimal Resource Allocation in Multi-Hop Wireless Networks

Recent advances in wireless communications and digital electronics have enabled rapid development of a variety of wireless network technologies. The undeniable popularity of wireless network is due to its ubiquity and convenience, which is appreciated by the users. In this dissertation, we study the problem of resource allocation in multihop wireless networks (so called ad hoc networks). A wireless ad hoc network consists of a collection of wireless nodes without a fixed infrastructure. Two wireless nodes communicate with each other directly, if they are within the transmission range of each other. Otherwise, the communication is achieved through the relays of intermediate nodes. Compared with traditional wireline networks, the unique characteristics of wireless networks pose fundamental challenges to the design of effective resource allocation algorithms that are optimal with respect to resource utilization and fair across different network flows. Particularly, the following issues of wireless networks need fresh treatment: (1) Interference of wireless communication. Flows not only contend at the same node (contention in the time domain), but also compete for shared channel if they are within the interference ranges of each other (contention in the spatial domain). (2) Multiple resource usage. Sending data from one wireless node to another needs to consume multiple resources, most notably wireless bandwidth and battery energy. (3) Autonomous communication entities. The wireless nodes usually belong to different autonomous entities. They may lack the incentive to contribute to the network functionality in a cooperative way. (4) Rate diversity. Wireless nodes can adaptively change the transmission bit rate based on perceived channel conditions. This leads to a wireless network with rate diversity, where competing flows within the interference range transmit at different rates. None of the existing resource allocation algorithms in wireless ad hoc networks have realistically considered end-to-end flows spanning multiple hops. Moreover, strategies proposed for wireline networks are not applicable in the context of wireless ad hoc network, due to its unique characteristics. In this dissertation, we propose a new price-based resource allocation framework in wireless ad hoc networks to achieve optimal resource utilization and fairness among competing end-to-end flows. We build our pricing framework on the notion of maximal cliques in wireless ad hoc networks, as compared to individual links in traditional wide-area wireline networks. Based on such a price-based theoretical framework, we present a two-tier iterative algorithm. Distributed across wireless nodes, the algorithm converges to a global network optimum with respect to resource allocations. Further, we present a price pair mechanism to coordinate multiple resource allocations, and to provide incentives simultaneously such that cooperation is promoted and the desired global optimal network operating point is reached by convergence with a fully decentralized self-optimizing algorithm. Such desired network-wide global optimum is characterized with the concept of Nash bargaining solution, which not only provides the Pareto optimal point for the network, but is also consistent with the fairness axioms of game theory. Finally, we present a channel aware price generation scheme to decompose the bit rate adjustment and the flow rate allocation. The allocation result achieves channel time fairness where user fairness and channel utilization is balanced. The major achievements of this dissertation are outlined as follows. It models a system-wide optimal operation point of a wireless network, and outlines the solution space of resource allocation in a multihop wireless network; It presents a price-based distributed resource allocation algorithm to achieve this global optimal point; It presents a low overhead implementation of the price-based resource allocation algorithm; It presents an incentive mechanism that enables the resource allocation algorithm when users are selfish

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