Network Selection and Resource Allocation Games for Wireless Access Networks

Wireless access networks are often characterized by the interaction of different end users, communication technologies, and network operators. This paper analyzes the dynamics among these "actors" by focusing on the processes of wireless network selection, where end users may choose among multiple available access networks to get connectivity, and resource allocation, where network operators may set their radio resources to provide connectivity. The interaction among end users is modeled as a non-cooperative congestion game where players (end users) selfishly select the access network that minimizes their perceived selection cost. A method based on mathematical programming is proposed to find Nash equilibria and characterize their optimality under three cost functions, which are representative of different technological scenarios. System level simulations are then used to evaluate the actual throughput and fairness of the equilibrium points. The interaction among end users and network operators is then assessed through a two-stage multi-leader/multi-follower game, where network operators (leaders) play in the first stage by properly setting the radio resources to maximize their users, and end users (followers) play in the second stage the aforementioned network selection game. The existence of exact and approximated subgame perfect Nash equilibria of the two-stage game is thoroughly assessed and numerical results are provided on the "quality" of such equilibria

A Game Theory based Contention Window Adjustment for IEEE 802.11 under Heavy Load

The 802.11 families are considered as the most applicable set of standards for Wireless Local Area Networks (WLANs) where nodes make access to the wireless media using random access techniques. In such networks, each node adjusts its contention window to the minimum size irrespective of the number of competing nodes, so in saturated mode and excessive number of nodes available, the network performance is reduced due to severe collision probability. A cooperative game is being proposed to adjust the users’ contention windows in improving the network throughput, delay and packet drop ratio under heavy traffic load circumstances. The system’s performance evaluated by simulations indicate some superiorities of the proposed method over 802.11-DCF (Distribute Coordinate Function)

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

Resource Management in Delay Tolerant Networks and Smart Grid

In recent years, significant advances have been achieved in communication networks and electric power systems. Communication networks are developed to provide services within not only well-connected network environments such as wireless local area networks, but also challenged network environments where continuous end-to-end connections can hardly be established between information sources and destinations. Delay tolerant network (DTN) is proposed to achieve this objective by utilizing a store-carry-and-forward routing scheme. However, as the network connections in DTNs are intermittent in nature, the management of network resources such as communication bandwidth and buffer storage becomes a challenging issue. On the other hand, the smart grid is to explore information and communication technologies in electric power grids to achieve electricity delivery in a more efficient and reliable way. A high penetration level of electric vehicles and renewable power generation is expected in the future smart grid. However, the randomness of electric vehicle mobility and the intermittency of renewable power generation bring new challenges to the resources management in the smart grid, such as electric power, energy storage, and communication bandwidth management. This thesis consists of two parts. In part I, we focus on the resource management in DTNs. Specifically, we investigate data dissemination and on-demand data delivery which are two of the major data services in DTNs. Two kinds of mobile nodes are considered for the two types of services which correspond to the pedestrians and high-speed train passengers, respectively. For pedestrian nodes, the roadside wireless local area networks are used as an auxiliary communication infrastructure for data service delivery. We consider a cooperative data dissemination approach with a packet pre-downloading mechanism and propose a double-loop receiver-initiated medium access control scheme to resolve the channel contention among multiple direct/relay links and exploit the predictable traffic characteristics as a result of packet pre-downloading. For high-speed train nodes, we investigate on-demand data service delivery via a cellular/infostation integrated network. The optimal resource allocation problem is formulated by taking account of the intermittent network connectivity and multi-service demands. In order to achieve efficient resource allocation with low computational complexity, the original problem is transformed into a single-machine preemptive scheduling problem and an online resource allocation algorithm is proposed. If the link from the backbone network to an infostation is a bottleneck, a service pre-downloading algorithm is also proposed to facilitate the resource allocation. In part II, we focus on resource management in the smart grid. We first investigate the optimal energy delivery for plug-in hybrid electric vehicles via vehicle-to-grid systems. A dynamic programming formulation is established by considering the bidirectional energy flow, non-stationary energy demand, battery characteristics, and time-of-use electricity price. We prove the optimality of a state-dependent double-threshold policy based on the stochastic inventory theory. A modified backward iteration algorithm is devised for practical applications, where an exponentially weighted moving average algorithm is used to estimate the statistics of vehicle mobility and energy demand. Then, we propose a decentralized economic dispatch approach for microgrids such that the optimal decision on power generation is made by each distributed generation unit locally via multiagent coordination. To avoid a slow convergence speed of multiagent coordination, we propose a heterogeneous wireless network architecture for microgrids. Two multiagent coordination schemes are proposed for the single-stage and hierarchical operation modes, respectively. The optimal number of activated cellular communication devices is obtained based on the tradeoff between communication and generation costs

Flexible Spectrum Assignment for Local Wireless Networks

In this dissertation, we consider the problem of assigning spectrum to wireless local-area networks (WLANs). In line with recent IEEE 802.11 amendments and newer hardware capabilities, we consider situations where wireless nodes have the ability to adapt not only their channel center-frequency but also their channel width. This capability brings an important additional degree of freedom, which adds more granularity and potentially enables more efficient spectrum assignments. However, it also comes with new challenges; when consuming a varying amount of spectrum, the nodes should not only seek to reduce interference, but they should also seek to efficiently fill the available spectrum. Furthermore, the performances obtained in practice are especially difficult to predict when nodes employ variable bandwidths. We first propose an algorithm that acts in a decentralized way, with no communication among the neighboring access points (APs). Despite being decentralized, this algorithm is self-organizing and solves an explicit tradeoff between interference mitigation and efficient spectrum usage. In order for the APs to continuously adapt their spectrum to neighboring conditions while using only one network interface, this algorithm relies on a new kind of measurement, during which the APs monitor their surrounding networks for short durations. We implement this algorithm on a testbed and observe drastic performance gains compared to default spectrum assignments, or compared to efficient assignments using a fixed channel width. Next, we propose a procedure to explicitly predict the performance achievable in practice, when nodes operate with arbitrary spectrum configurations, traffic intensities, transmit powers, etc. This problem is notoriously difficult, as it requires capturing several complex interactions that take place at the MAC and PHY layers. Rather than trying to find an explicit model acting at this level of generality, we explore a different point in the design space. Using a limited number of real-world measurements, we use supervised machine-learning techniques to learn implicit performance models. We observe that these models largely outperform other measurement-based models based on SINR, and that they perform well, even when they are used to predict performance in contexts very different from the context prevailing during the initial set of measurements used for learning. We then build a second algorithm that uses the above-mentioned learned models to assign the spectrum. This algorithm is distributed and collaborative, meaning that neighboring APs have to exchange a limited amount of control traffic. It is also utility-optimal -- a feature enabled both by the presence of a model for predicting performance and the ability of APs to collaboratively take decisions. We implement this algorithm on a testbed, and we design a simple scheme that enables neighboring APs to discover themselves and to implement collaboration using their wired backbone network. We observe that it is possible to effectively gear the performance obtained in practice towards different objectives (in terms of efficiency and/or fairness), depending on the utility functions optimized by the nodes. Finally, we study the problem of scheduling packets both in time and frequency domains. Such ways of scheduling packets have been made possible by recent progress in system design, which make it possible to dynamically tune and negotiate the spectrum band [...

Distributed Spectrum Assignment for Home WLANs

We consider the problem of jointly allocating chan- nel center frequencies and bandwidths for IEEE 802.11 wireless LANs (WLANs). The bandwidth used on a link affects sig- nificantly both the capacity experienced on this link and the interference produced on neighboring links. Therefore, when jointly assigning both center frequencies and channel widths, there is a trade-off between interference mitigation and the potential capacity offered on each link. We study this trade- off and we present SAW (spectrum assignment for WLANs), a decentralized algorithm that finds efficient configurations. SAW is tailored for 802.11 home networks. It is distributed, online and transparent. It does not require a central coordinator and it constantly adapts the spectrum usage without disrupting network traffic. A key feature of SAW is that the access points (APs) need only a few out-of-band measurements in order to make spectrum allocation decisions. Despite being completely decentralized, the algorithm is self-organizing and provably converges towards efficient spectrum allocations. We evaluate SAW using both simulation and a deployment on an indoor testbed composed of off-the-shelf 802.11 hardware. We observe that it dramatically increases the overall network efficiency and fairness

Fine-Grained Radio Resource Management to Control Interference in Dense Wi-Fi Networks

In spite of the enormous popularity of Wi-Fienabled devices, the utilization of Wi-Fi radio resources (e.g. RF spectrum and transmission power levels) at Access Points (APs) is degraded in current decentralized Radio Resource Management (RRM) schemes. Most state of the art central control solutions apply configurations in which the network-wide impacts of the involved parameters and their mutual relationships are ignored. In this paper, we propose an algorithm for jointly adjusting the transmission power levels and optimizing the RF channel assignment of APs by taking into account the flows’ required qualities while minimizing their interference impacts throughout the network. The proposed solution is tailored for an operatoragnostic and Software Defined Wireless Networking (SDWN)- based centralised RRM in dense Wi-Fi networks. Our extensive simulation results validate the performance improvement of the proposed algorithm compared to the main state of the art alternative by showing more than 25% higher spectrum efficiency, satisfying the users’ demands and further mitigating the networkwide interference through a flow-based and quality-oriented power level adjustment