Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

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

Analysis of dynamic spectrum leasing for coded Bi-directional communication

In this paper, we aim to present a cooperative relaying based two way wireless communication scheme which can provide both spectral and energy efficiency in future wireless networks. To this end, we propose a novel network coding based Dynamic Spectrum Leasing (DSL) technique in which the cognitive secondary users cooperatively relay the primary data for two-way primary communication. In exchange for the relaying services, the primary grants exclusive access to the secondary users for their own activity. We model the random geometry of the ad hoc secondary users using a Poisson point process. We devise a game theoretic framework for the division of leasing time between the primary cooperation and secondary activity phases. We demonstrate that under these considerations and employing network coding, DSL can improve the number of bits that are successfully transmitted by 54% as compared to un-coded direct two way primary communication. Also the energy costs of the proposed DSL scheme are more than 10 times lower. Employing DSL also enables the cognitive users to get reasonable time for their own transmission after increasing the primary spectral and energy efficiency

The effect of globalization on union bargaining and price-cost margins of firms.

In recent years, Europe has witnessed an accelerated process of economic integration. Trade barriers were removed, the euro was introduced and ten new member states have joined the European Union. This paper analyzes how this process of increased economic integration has affected labor and product markets. To this end, we use a panel of Belgian manufacturing firms to estimate price-cost margins and union bargaining power and show how various measures of globalization affect them. Our findings can be summarized as follows: On average, firms set prices about 30% above marginal costs, but there is substantial variation across sectors, with the lowest mark-up around 19% and the highest around 52%. In addition, we find evidence that unions bargain over both wages and employment. We estimate an index of bargaining power, which reflects the fraction of profits that is passed on to workers into higher wages. Depending on the sector, this fraction varies between 6% and 18% and it increases with the markups of firms. Finally, we find that globalization puts pressure on both markups and union bargaining power, especially when there is increased competition from the low wage countries. This suggests that increased globalization is associated with a moderation of wage claims in unionized countries, which should be associated with positive effects on employment.Mark-ups; Globalization; Trade Unions; International Trade;

Price and wage setting in an integrating Europe : firm level evidence..

Working; Research;

Price and wage setting in an integrating Europe : firm level evidence

Europe has witnessed the last decade an accelerated process of economic integration. Trade barriers were removed, the euro was introduced and ten new member states entered the European Union. Economic integration is likely to have an impact on both labor and product markets. Unlike most other papers, that focus on product and labor markets separately, we look at the link between globalization and product and labor market imperfections simultaneously. To this end, we rely on a rich panel of manufacturing firms in Belgium, a small open economy. We find that union bargaining power is higher in sectors characterized by high price cost margins. Moreover, ignoring imperfections on the labor market, leads to an underestimation of product market power. Concerning the influence of globalization, our main findings are that both price cost margins and union bargaining power are typically lower in sectors that are subject higher international competition. This result is especially true for competition from low wage countriesMark-ups, Trade Unions, International Trade

Allocation of Communication and Computation Resources in Mobile Networks

Konvergence komunikačních a výpočetních technologií vedlo k vzniku Multi-Access Edge Computing (MEC). MEC poskytuje výpočetní výkon na tzv. hraně mobilních sítí (základnové stanice, jádro mobilní sítě), který lze využít pro optimalizaci mobilních sítí v reálném čase. Optimalizacev reálném čase je umožněna díky nízkému komunikačnímu zpoždění například v porovnání s Mobile Cloud Computing (MCC). Optimalizace mobilních sítí vyžaduje informace o mobilní síti od uživatelských zařízeních, avšak sběr těchto informací využívá komunikační prostředky, které jsou využívány i pro přenos uživatelských dat. Zvyšující se počet uživatelských zařízení, senzorů a taktéž komunikace vozidel tvoří překážku pro sběr informací o mobilních sítích z důvodu omezeného množství komunikačních prostředků. Tudíž je nutné navrhnout řešení, která umožní sběr těchto informací pro potřeby optimalizace mobilních sítí. V této práci je navrženo řešení pro komunikaci vysokého počtu zařízeních, které je postaveno na využití přímé komunikace mezi zařízeními. Pro motivování uživatelů, pro využití přeposílání dat pomocí přímé komunikace mezi uživateli je navrženo přidělování komunikačních prostředků jenž vede na přirozenou spolupráci uživatelů. Dále je provedena analýza spotřeby energie při využití přeposílání dat pomocí přímé komunikace mezi uživateli pro ukázání jejích výhod z pohledu spotřeby energie. Pro další zvýšení počtu komunikujících zařízení je využito mobilních létajících základových stanic (FlyBS). Pro nasazení FlyBS je navržen algoritmus, který hledá pozici FlyBS a asociaci uživatel k FlyBS pro zvýšení spokojenosti uživatelů s poskytovanými datovými propustnostmi. MEC lze využít nejen pro optimalizaci mobilních sítí z pohledu mobilních operátorů, ale taktéž uživateli mobilních sítí. Tito uživatelé mohou využít MEC pro přenost výpočetně náročných úloh z jejich mobilních zařízeních do MEC. Z důvodu mobility uživatel je nutné nalézt vhodně přidělení komunikačních a výpočetních prostředků pro uspokojení uživatelských požadavků. Tudíž je navržen algorithmus pro výběr komunikační cesty mezi uživatelem a MEC, jenž je posléze rozšířen o přidělování výpočetných prostředků společně s komunikačními prostředky. Navržené řešení vede k snížení komunikačního zpoždění o desítky procent.The convergence of communication and computing in the mobile networks has led to an introduction of the Multi-Access Edge Computing (MEC). The MEC combines communication and computing resources at the edge of the mobile network and provides an option to optimize the mobile network in real-time. This is possible due to close proximity of the computation resources in terms of communication delay, in comparison to the Mobile Cloud Computing (MCC). The optimization of the mobile networks requires information about the mobile network and User Equipment (UE). Such information, however, consumes a significant amount of communication resources. The finite communication resources along with the ever increasing number of the UEs and other devices, such as sensors, vehicles pose an obstacle for collecting the required information. Therefore, it is necessary to provide solutions to enable the collection of the required mobile network information from the UEs for the purposes of the mobile network optimization. In this thesis, a solution to enable communication of a large number of devices, exploiting Device-to-Device (D2D) communication for data relaying, is proposed. To motivate the UEs to relay data of other UEs, we propose a resource allocation algorithm that leads to a natural cooperation of the UEs. To show, that the relaying is not only beneficial from the perspective of an increased number of UEs, we provide an analysis of the energy consumed by the D2D communication. To further increase the number of the UEs we exploit a recent concept of the flying base stations (FlyBSs), and we develop a joint algorithm for a positioning of the FlyBS and an association of the UEs to increase the UEs satisfaction with the provided data rates. The MEC can be exploited not only for processing of the collected data to optimize the mobile networks, but also by the mobile users. The mobile users can exploit the MEC for the computation offloading, i.e., transferring the computation from their UEs to the MEC. However, due to the inherent mobility of the UEs, it is necessary to determine communication and computation resource allocation in order to satisfy the UEs requirements. Therefore, we first propose a solution for a selection of the communication path between the UEs and the MEC (communication resource allocation). Then, we also design an algorithm for joint communication and computation resource allocation. The proposed solution then lead to a reduction in the computation offloading delay by tens of percent

Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation

In this dissertation, we address the issue of collaborative information processing for diffusive source parameter estimation using wireless sensor networks (WSNs) capable of sensing in dispersive medium/environment, from signal processing perspective. We begin the dissertation by focusing on the mathematical formulation of a special diffusion phenomenon, i.e., an underwater oil spill, along with statistical algorithms for meaningful analysis of sensor data leading to efficient estimation of desired parameters of interest. The objective is to obtain an analytical solution to the problem, rather than using non-model based sophisticated numerical techniques. We tried to make the physical diffusion model as much appropriate as possible, while maintaining some pragmatic and reasonable assumptions for the simplicity of exposition and analytical derivation. The dissertation studies both source localization and tracking for static and moving diffusive sources respectively. For static diffusive source localization, we investigate two parametric estimation techniques based on the maximum-likelihood (ML) and the best linear unbiased estimator (BLUE) for a special case of our obtained physical dispersion model. We prove the consistency and asymptotic normality of the obtained ML solution when the number of sensor nodes and samples approach infinity, and derive the Cramer-Rao lower bound (CRLB) on its performance. In case of a moving diffusive source, we propose a particle filter (PF) based target tracking scheme for moving diffusive source, and analytically derive the posterior Cramer-Rao lower bound (PCRLB) for the moving source state estimates as a theoretical performance bound. Further, we explore nonparametric, machine learning based estimation technique for diffusive source parameter estimation using Dirichlet process mixture model (DPMM). Since real data are often complicated, no parametric model is suitable. As an alternative, we exploit the rich tools of nonparametric Bayesian methods, in particular the DPMM, which provides us with a flexible and data-driven estimation process. We propose DPMM based static diffusive source localization algorithm and provide analytical proof of convergence. The proposed algorithm is also extended to the scenario when multiple diffusive sources of same kind are present in the diffusive field of interest. Efficient power allocation can play an important role in extending the lifetime of a resource constrained WSN. Resource-constrained WSNs rely on collaborative signal and information processing for efficient handling of large volumes of data collected by the sensor nodes. In this dissertation, the problem of collaborative information processing for sequential parameter estimation in a WSN is formulated in a cooperative game-theoretic framework, which addresses the issue of fair resource allocation for estimation task at the Fusion center (FC). The framework allows addressing either resource allocation or commitment for information processing as solutions of cooperative games with underlying theoretical justifications. Different solution concepts found in cooperative games, namely, the Shapley function and Nash bargaining are used to enforce certain kinds of fairness among the nodes in a WSN

On the Efficiency tradeoffs in User-Centric Cloud RAN

Ambitious targets for aggregate throughput, energy efficiency and ubiquitous user experience are propelling the advent of ultra-dense networks. Intercell interference and high energy consumption in an ultra-dense network are the prime hindering factors in pursuit of these goals. To address the aforementioned challenges, in this paper, we propose a novel user-centric network orchestration solution for Cloud RAN based ultra-dense deployments. In this solution, a cluster (virtual disc) is created around users depending on their service priority. Within the cluster radius, only the best remote radio head (RRH) is activated to serve the user, thereby decreasing interference and saving energy. We use stochastic geometry based approach to quantify the area spectral efficiency (ASE) and RRH power consumption models to quantity energy(EE) efficiency of the proposed user-centric Cloud RAN (UCRAN). Through extensive analysis we observe that the cluster sizes that yield optimal ASE and EE are quite different. We propose a game theoretic self-organizing network (GT-SON) framework that can orchestrate the network between ASE and EE focused operational modes in real-time in response to changes in network conditions and the operator's revenue model, to achieve a Pareto optimal solution. A bargaining game is modeled to investigate the ASE-EE tradeoff through adjustment in the exponential efficiency weightage in the Nash bargaining solution (NBS). Results show that compared to current non-user centric network design, the proposed solution offers the flexibility to operate the network at multiple folds higher ASE or EE along with significant improvement in user experience