Bio-inspired delayed evolutionary game dynamics with?networking applications

We study in this paper some evolutionary games where competition between individuals from a large population occurs through many local interactions between randomly selected individuals. We focus on games that have the property of possessing a single interior evolutionarily stable strategy (ESS). We study in particular the effect of the time delays on the convergence of evolutionary dynamics to the ESS in an evolutionary game in which each pure strategy is associated with its own delay. In particular, we study a multiple access game as well as a Hawk and Dove game. We study the properties of the ESS in these games and also the effect of time delays on the convergence of various bio-inspired evolutionary game dynamics to the ESS.     

Optimal energy-delay tradeoff for opportunistic spectrum access in cognitive radio networks

Cognitive radio (CR) has been considered as a promising technology to enhance spectrum efficiency via opportunistic transmission at link level. Basic CR features allow secondary users (SUs) to transmit only when the licensed channel is not occupied by primary users (PUs). However, waiting for an idle time slot may lead to large packet delays and high energy consumption. We further consider that the SU may decide, at any moment, to use another dedicated way of communication (4G) in order to transmit his packets. Thus, we consider an Opportunistic Spectrum Access (OSA) mechanism that takes into account packet delay and energy consumption. We formulate the OSA problem as a Partially Observable Markov Decision Process (POMDP) by explicitly considering the energy consumption as well as packets’ delay, which are often ignored in existing OSA solutions. Specifically, we consider a POMDP with an average reward criterion. We derive structural properties of the value function and we show the existence of optimal strategies in the class of the threshold strategies. For implementation purposes, we propose online learning mechanisms that estimate the PU activity and determine the appropriate threshold strategy on the fly. In particular, numerical illustrations validate our theoretical findings.     

The evolution of transport protocols: An evolutionary game perspective

Today’s Internet is well adapted to the evolution of protocols at various network layers. Much of the intelligence of congestion control is delegated to the end users and they have a large amount of freedom in the choice of the protocols they use. In the absence of a centralized policy for a global deployment of a unique protocol to perform a given task, the Internet experiences a competitive evolution between various versions of protocols. The evolution manifests itself through the upgrading of existing protocols, abandonment of some protocols and appearance of new ones. We highlight in this paper the modeling capabilities of the evolutionary game paradigm for explaining past evolution and predicting the future one. In particular, using this paradigm we derive conditions under which (i) a successful protocol would dominate and wipe away other protocols, or (ii) various competing protocols could coexist. In the latter case we also predict the share of users that would use each of the protocols. We further use evolutionary games to propose guidelines for upgrading protocols in order to achieve desirable stability behavior of the system.     

Optimal Measurement-based Pricing for an M/M/1 Queue

In this paper, we consider a system modelled as an M/M/1 queue. Jobs corresponding to different classes are sent to the queue and are characterized by a delay cost per unit of time and a demand function. Our goal is to design an optimal pricing scheme for the queue, where the total charge depends on both the mean delay at the queue and arrival rate of each customer. We also assume that those two values have to be (statistically) measured, introducing errors on the total charge that might avert jobs from using the system, and then decrease demand. This model can be applied in telecommunication networks, where pricing can be used to control congestion, and the network can be characterized by a single bottleneck queue; the throughput of each class would be determined through passive measurements while the delay would be determined through active measurements.     

Hierarchy sustains partial cooperation and induces a Braess-like paradox in slotted aloha-based networks

This paper studies a hierarchical distributed choice of retransmission probabilities in slotted aloha. In particular, we consider a wireless system composed of one central receiver and several selfish mobile users communicating via the slotted aloha protocol. The set of mobile users is split into two classes: leaders and followers. We then study the induced non-cooperative hierarchical game based on the Stackelberg equilibrium concept. Using a 4D Markovian model, we compute the steady state of the system and derive the average throughput and the expected delay as well. We start by discussing the protocol design and propose a controlled slotted aloha using a virtual controller. The virtual controller can sustain partial cooperation among concurrent mobile users when accessing the channel by making the channel lossy. This leads us to identify a Braess-like paradox in which reducing capacity to the system may improve the performance of all mobile users. We then investigate the impact of hierarchy among mobile users in such a random access protocol and discuss how to distribute leader/follower roles. We show that the global performance of the system is improved compared to standard slotted aloha system. However, slight performances slow-down may be observed for the followers group when the total number of mobile users is relatively small.