Habib B. A. Sidi

Evolutionary forwarding games in delay tolerant networks: Equilibria, mechanism design and stochastic approximation

In this paper, we apply evolutionary games to non-cooperative forwarding control in Delay Tolerant Networks (DTNs). The main focus is on mechanisms to rule the participation of the relays to the delivery of messages in DTNs. Thus, we express the success probability as a function of the competition that takes place within a large population of mobiles, and we characterize the effect of reward-based mechanisms on the performance of such systems. Devices acting as active relays, in fact, sacrifice part of their batteries in order to support message replication and thus increase the probability to reach the destination. In our scheme, a relay can choose the strategy by which they participate to the message relaying. A mobile that participates receives a unit of reward based on the reward mechanism selected by the network. A utility function is introduced as the difference between the expected reward and the energy cost, i.e., the cost spent by the relay to sustain forwarding operations. We show how the evolution dynamics and the equilibrium behavior (called Evolutionary Stable Strategy - ESS) are influenced by the characteristics of inter contact time, energy expenditure and pricing characteristics. We extend our analysis to mechanisms that the system can introduce in order to have the message delivered to the destination with high probability within a given deadline and under energy constraints which bound the number of released copies per message. Finally, we apply our findings in order to devise decentralized forwarding algorithms that are rooted in the theory of stochastic approximations. Thus, we demonstrate that the ESS can be attained without complete knowledge of the system state and letting the source monitor number of released copies per message only. We provide extensive numerical results to validate the proposed scheme.

A game theoretic approach for the association problem in two-tier HetNets

This paper addresses a Bayesian game theoretic framework for determining the association rules that decide to which cell a given mobile user should associate in LTE two-tier Heterogeneous Networks (HetNets). Users are assumed to compete to maximize their throughput by picking the best locally serving cell with respect to their own measurement, their demand and a partial statistical channel state information (CSI) of other users. In particular, we investigate the properties of a hierarchical game, in which the macro-cell BS is a player on its own. We derive analytically the utilities related to the channel quality perceived by users to obtain the equilibria. We show by means of a Stackelberg formulation, how the operator, by dynamically choosing the offset about the state of the channel, can optimize its global utility while end-users maximize their individual utilities. The proposed hierarchical decision approach for wireless networks can reach a good trade-off between the global network performance at the equilibrium and the requested amount of signaling. Typically, it is shown that when the network goal is orthogonal to users goal, this can lead the users to a misleading association problem. Numerical results validate the expectation from the theoretical analysis and illustrate the advantages of the proposed approach.

Automated dynamic offset applied to cell association

In this paper, we develop a hierarchical Bayesian game framework for automated dynamic offset selection. Users compete to maximize their throughput by picking the best locally serving radio access network (RAN) with respect to their own measurement, their demand and a partial statistical channel state information (CSI) of other users. In particular, we investigate the properties of a Stackelberg game, in which the base station is a player on its own. We derive analytically the utilities related to the channel quality perceived by users to obtain the equilibria. We study the Price of Anarchy (PoA) of such system, where the PoA is the ratio of the social welfare attained when a network planner chooses policies to maximize social welfare versus the social welfare attained in Nash/Stackeleberg equilibrium when users choose their policies strategically. We show by means of a Stackelberg formulation, how the operator, by sending appropriate information about the state of the channel, can configure a dynamic offset that optimizes its global utility while users maximize their individual utilities. The proposed hierarchical decision approach for wireless networks can reach a good trade-off between the global network performance at the equilibrium and the requested amount of signaling. Typically, it is shown that when the network goal is orthogonal to users goal, this can lead the users to a misleading association problem.

Fractional frequency reuse stackelberg model for self-organizing networks

Growth of network access technologies in the mobile environment has raised several new issues due to the interference between the available accesses. Thanks to the currently used access methods such as OFDMA in mobile networks, and LTE systems, the intra-cell interferences are avoided and the quality of service has increased. Nevertheless, the diversity and multiplicity of base stations in the network has left behind, a major problem of inter-cell interferences. In this paper, we focus on the optimization of the total throughput of cellular networks using fractional frequency reuse and allowing each mobile user to individually choose its serving base station. We derive analytically the utilities related to the network manager and mobile users and develop a Stackelberg game to obtain the equilibria. We propose a distributed algorithm that allows the base stations, using a light collaboration, to achieve an efficient utilization of the frequencies, with the optic of maximizing the total system utility. This algorithm is based on stochastic gradient descent which requires some information to be exchanged between neighboring base stations. At user association level, we propose an iterative distributed algorithm based on automata learning algorithm. Both algorithms allow the system to converge to the Stackelberg equilibrium. Furthermore, simulation results carried out based on a realistic network setting show promising results in terms of global utility and convergence issues.

Delay Tolerant Networks in Partially Overlapped Networks: A Non-cooperative Game Approach

Epidemic forwarding protocol in Delay Tolerant Networks maximizes successful data delivery probability but at the same time incurs high costs in terms of redundancy of packet copies in the system and energy consumption. Two-hop routing on the other hand minimizes the packet flooding and the energy costs but degrades the delivery probability. This paper presents a framework to achieve a tradeoff between the successful data delivery probability and the energy costs. Each mobile has to decide which routing protocol it wants to use for packet delivering. In such a problem, we consider a non-cooperative game theory approach. We explore the scenario where the source and the destination mobiles are enclosed in two different regions, which are partially overlapped. We study the impact of the proportion of the surface covered by both regions on the Nash equilibrium and price of anarchy. We also design a fully distributed algorithm that can be employed for convergence to the Nash equilibrium. This algorithm does not require any knowledge of some parameter of the system as the number of mobiles or the rate of contacts between mobiles.

Small Cells' Deployment Strategy and Self-Optimization for EMF Exposure Reduction in HetNets

The increasing densification of telecommunications mobile networks raises the compelling problem of exposure to electromagnetic fields (EMF). In this paper, we address the particular concept of heterogeneous networks, where small cells (SCs) are deployed to build up a second layer to the existing macrocell network. SC deployment is shown to be particularly effective in reducing EMF exposure, in addition to capacity enhancement. Previous studies on EMF have focused on the uplink (UL) and downlink (DL) components separately, although they are strongly correlated. Relying on the definition of the exposure index (EI) from the European FP7 Lexnet project, we evaluate the impact on EMF exposure due to SC deployment. Guidelines for EMF-efficient deployment of SCs are first presented and evaluated. Then, a new enhanced intercell interference coordination (eICIC) algorithm using UL and DL metrics to optimize capacity and exposure to EMF is proposed. Static and self-optimizing dynamic implementations of the solution are considered, using a utility function related to the sojourn time of the users. Time-domain and frequency-domain self-optimized eICIC are compared in terms of EMF exposure reduction and capacity enhancement. The eICIC algorithms are combined with a self-optimized coverage range expansion function leading to further reduction of the EI. The proposed solution for optimizing both capacity and exposure to EMF is compared with a capacity-driven solution based on the DL metrics only. Simulation results illustrate the performance gains obtained for the different SC deployment scenarios.

A two-stage game theoretic approach for self-organizing networks

Growth of network access technologies in the mobile environment has raised several new issues due to the interference between the available access. Thanks to the currently used access methods such as the orthogonal frequency division multiple access in mobile networks and the long term evolution-advanced systems, the intra-cell interferences are avoided and the quality of service has increased. Nevertheless, the diversity and multiplicity of base stations in the network have left behind a major problem of inter-cell interferences. In this paper, we focus on the optimization of the total throughput of cellular networks using fractional frequency reuse and allowing each mobile user to individually choose its serving base station. We derive analytically the utilities related to the network manager and mobile users and develop a Stackelberg game to obtain the equilibrium. We propose a distributed algorithm that allows the base stations, using a light collaboration, to achieve an efficient utilization of the frequencies, with the optic of maximizing the total system utility. This algorithm is based on stochastic gradient descent which requires some information to be exchanged between neighboring base stations. At user association level, we propose an iterative distributed algorithm based on automata learning algorithm. Both algorithms allow the system to converge to the Stackelberg equilibrium. Furthermore, simulation results carried out based on a realistic network setting show promising results in terms of global utility and convergence issues. In this setting, we include scenarios with a varying number of users and address the problem of robustness and scalability of the proposed approach.