The rapid growth of large language models (LLMs) has created a demand for reliable, interpretable, and scalable ranking systems capable of comparing models based on human preferences. Traditional benchmarks often fail to capture qualitative nuances in open-domain dialog, where subjective judgments play a central role. In this work, we conduct a comparative study of six prominent ranking algorithms Elo, Glicko, TrueSkill, Bradley-Terry, Markov Chain-based ranking, and a novel HawasRank algorithm applied to the Chatbot Arena dataset containing 244,978 pairwise human preference comparisons. HawasRank is a divergence-based, game-theoretic ranking method inspired by Bregman optimization frameworks, designed to improve convergence speed, transitivity preservation, and computational efficiency in human-in-the-loop LLM evaluations. In this study, we carefully compare these algorithms based on several important criteria, such as predictive accuracy, the occurrence of transitivity violations, sensitivity to hyperparameters, convergence behavior, and CPU resource consumption. The findings of our analysis reveal the trade-offs that exist between different ranking approaches and offer practical guidance for choosing appropriate algorithms, especially in large-scale and evolving LLM evaluation environments.

Cyberbullying is an increasing threat on social media, with serious consequences for mental health, particularly among young people. Despite growing global efforts to address this problem, developing accurate detection systems remains challenging for low-resource languages in both text and audio modalities, such as Arabic and its dialects. This paper presents a binary-labeled dataset for cyberbullying detection in Moroccan Darija. The dataset merges over 4,000 newly collected YouTube comments with the OMCD (Offensive Moroccan Comments Dataset) corpus of 8,024 comments, originally labeled for offensive language. To better reflect the nature of cyberbullying, the entire corpus was reannotated from scratch, clearly distinguishing between bullying and non-bullying content, including subtle forms like sarcasm, shaming, and indirect aggression. Several Arabic transformer models were fine-tuned and evaluated using standard classification metrics. The results show that dialectspecific models, particularly DarijaBERT, achieve the best performance, underlining the importance of context-aware annotation and in-domain pre-training for cyberbullying detection in lowresource settings.

Integrating blockchain technology into the Internet of Things (IoT) has revolutionized industries, enabling decentralized and reliable management of systems, while improving both efficiency and security. However, a key challenge for blockchain-based IoT solutions is ensuring the accuracy of data fed into the blockchain, known as the “blockchain oracle problem.” This work addresses this challenge by proposing the BIVO system (blockchain information verification oracles), a blockchain-based decentralized oracle for IoT networks. The system utilizes a reputation and voting mechanism suitable for both crowdsourced and semi-controlled environments. We also model the weighted voting mechanism as a stochastic game and conduct stress tests to analyze the system’s expected accuracy and cumulative payoffs under various conditions. Our findings indicate that the system achieves higher accuracy compared to nonweighted voting approaches. In semi-controlled environments, the system demonstrates resilience against up to 64% of adversarial nodes. However, under the worst conditions, malicious nodes need to control no more than 36% of the network to benefit from malicious behavior. Additionally, we implemented a prototype of the BIVO system and deployed it on both a local blockchain simulator and the public Ethereum testnet Sepolia to evaluate the cost and feasibility of blockchain integration.

This paper examines a class of mean-field-type games with finite numbers of decision-makers with state dynamics driven by Rosenblatt processes. Rosen-blatt processes are non-Gaussian, non-Poisson, and non-Markov with long-range dependence. We provide equilibrium strategies and equilibrium costs in linear state-and-mean-field-type feedback form for all decision-makers. Interestingly, we show that the equilibrium strategies for state driven by Brownian, multi-fractional Brownian, Gauss-Volterra processes no longer provide equilibrium in presence of Rosenblatt processes.

In this paper, we propose a co-opetitive mean-field-type game (MFTG) approach in which decision makers interact with each other by means of partial cooperation and competition simultaneously. The proposed novel approach allows decision makers' preferences to evolve over time to cooperate with those who contribute to their utilities, and to compete with those who are working against their individual interests. In addition, we consider that each decision maker has a co-opetitive capacity/power. The co-opetitive MFTG involves concepts, such as selfishness, altruism, competition, cooperation, among others; all together within the same strategic interaction. Both co-opetitive MFTGs and evolutionary dynamics describing the evolution of co-opetitive parameters are combined. We design incentives to promote the emergence of co-opetition over selfish behavior.

This paper presents an interplay between deep learning and game theory. It models basic deep learning tasks as strategic games. Then, distributionally robust games and their relationship with deep generative adversarial networks (GANs) are presented. To achieve a higher order convergence rate without using a second derivative of the objective function, a Bregman discrepancy is used to construct a speed-up deep learning. Each player has a continuous action space which corresponds to weight space and aims to learn his/her optimal strategy. The convergence rate of the proposed deep learning algorithm is derived using a mean estimate. Experiments are carried out on a real dataset in both shallow and deep GANs. Both qualitative and quantitative evaluation results show that the generative model trained by the Bregman deep learning algorithm can speed up the state-of-the-art performance.

Multi-access edge computing (MEC) has been proposed to reduce latency inherent in traditional cloud computing. One of the services offered in an MEC network (MECN) is computation offloading in which computing nodes, with limited capabilities and performance, can offload computation-intensive tasks to other computing nodes in the network. Recently, mean-field-type game (MFTG) has been applied in engineering applications in which the number of decision makers is finite and where a decision maker can be distinguishable from other decision makers and have a non-negligible effect on the total utility of the network. Since MECNs are implemented through finite number of computing nodes and the computing capability of a computing node can affect the state (i.e., the number of computation tasks) of the network, we propose non-cooperative and cooperative MFTG approaches to formulate computation offloading problems. In these scenarios, the goal of each computing node is to offload a portion of the aggregate computation tasks from the network that minimizes a specific cost. Then, we utilize a direct approach to calculate the optimal solution of these MFTG problems that minimizes the corresponding cost. Finally, we conclude the paper with simulations to show the significance of the approach.

We propose a nonparallel data-driven emotional speech conversion method. It enables the transfer of emotion-related characteristics of a speech signal while preserving the speaker's identity and linguistic content. Most existing approaches require parallel data and time alignment, which is not available in most real applications. We achieve nonparallel training based on an unsupervised style transfer technique, which learns a translation model between two distributions instead of a deterministic one-to-one mapping between paired examples. The conversion model consists of an encoder and a decoder for each emotion domain. We assume that the speech signal can be decomposed into an emotion-invariant content code and an emotion-related style code in latent space. Emotion conversion is performed by extracting and recombining the content code of the source speech and the style code of the target emotion. We tested our method on a nonparallel corpora with four emotions. Both subjective and objective evaluations show the effectiveness of our approach.

Traffic surveillance plays an important role in the development of a smart city, and it is a fundamental part in many applications such as security monitoring and traffic analysis. People are thrilled by the abundant data generated from the huge traffic networks but have difficulty in using them. In this paper, we propose a distributed mean-field-type filtering (DMF) framework to handle those noisy, partial-observed, and high-dimensional data. The filter incorporates a mean-field term into the system model and decomposes the state space into highly independent parts; filtering is performed in each part and then integrated. Our approach iterates through four operations: sampling, prediction, decomposition, and correction. Theoretical analysis provides a linear bound for the global error, which is independent of the network's cardinality. We implemented DMF in aircraft and vehicle tracking scenarios. Performance evaluation on synthetic and real-world data demonstrates the advantage of our approach over traditional mean-field free filters.

The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article. Given a team of robots and a set of targets (wireless sensor nodes), the planner computes all possible tours that each robot can make if it needs to visit a part of or the entire set of targets. Each segment of the tour relies on a minimum energy path planning algorithm. After the computation of all possible tour-segments, a utility function penalizing the overall energy consumption is formed. Rather than relying on the NP-hard Mobile Element Scheduling (MES) MILP problem, an approach using elements from game theory is employed. The suggested approach converges fast for most practical reasons thus allowing its utilization in near real time applications. Simulations are offered to highlight the efficiency of the developed algorithm.

The recent online platforms propose multiple items for bidding. The state of the art, however, is limited to the analysis of one item auction without resubmission. In this paper we study multi-item lowest unique bid auctions (LUBA) with resubmission in discrete bid spaces under budget constraints. We show that the game does not have pure Bayes-Nash equilibria (except in very special cases). However, at least one mixed Bayes-Nash equilibria exists for arbitrary number of bidders and items. The equilibrium is explicitly computed for two-bidder setup with resubmission possibilities. In the general setting we propose a distributed strategic learning algorithm to approximate equilibria. Computer simulations indicate that the error quickly decays in few number of steps. When the number of bidders per item follows a Poisson distribution, it is shown that the seller can get a non-negligible revenue on several items, and hence making a partial revelation of the true value of the items. Finally, the attitude of the bidders towards the risk is considered. In contrast to risk-neutral agents who bids very small values, the cumulative distribution and the bidding support of risk-sensitive agents are more distributed.

Deep generative models are powerful but difficult to train due to its instability, saturation problem and high dimensional data distribution. This paper introduces a game theory framework with Wasserstein metric to train generative models, in which the unknown data distribution is learned by dynamically optimizing the worst-case payoff. In the game, two types of players work on opposite objectives to solve a minimax problem. The defenders explore the Wasserstein neighborhood of real data to generate a set of hard samples which have the maximum distance from the model distribution. The attackers update the model to fit for the hard set so as to minimize the discrepancy between model and data distributions. Instead of Kullback-Leibler divergence, we use Wasserstein distance to measure the similarity between distributions. The Wasserstein metric is a true distance with better topology in the parameter space, which improves the stability of training. We provide practical algorithms to train deep generative models, in which an encoder network is designed to learn the feature vector of the high dimensional data. The algorithm is tested on CelebA human face dataset and compared with the state-of-the-art generative models. Performance evaluation shows the training process is stable and converges fast. Our model can produce visual pleasing images which are closer to the real distribution in terms of Wasserstein distance.

Content is not always about medias and files, computing tasks output are also a content that can be described, stored and cached. In this paper, we investigated the problem of edge computing and caching in the fog computing networks. In our scenario, we consider the user requests computing tasks from the fog, that the devices could not handle. Moreover, fogs use their storage and computing capabilities to cache the tasks computation results in order to minimize the latency, and use it storage to offload their resources by caching the significant computing tasks output. We propose a clustering method based on the correlation between the tasks and the cached content to decide what fog will give a better service in term of latency to offer better quality of service and experience. The problem of the users-fogs association was formulated as a coalition game between the users and the fogs. Finally, we use the BoltzmannGibbs learning algorithm to make every entity able to learn the best coalition, in order to enhance the convergence of the system to reach a better and optimal stability.

In this paper, we address the problem of energy-aware user satisfaction in self-organizing networks. Our main objective is to meet with the users requirements while reducing energy consumption. Accordingly, we aim at seeking satisfaction equilibria, mainly the efficient satisfaction equilibrium (ESE). We first define conditions of existence and uniqueness of ESE. Considering information-theoretic transmission rate based measure, we fully characterize the ESE and prove that, whenever it exists, it is a solution of a linear system. We show that, at the ESE, no player can increase its Quality of Service without degrading the energy performance. Finally, in order to reach the ESE, we propose a fully distributed scheme based on the Banach-Picard algorithm and show, through simulation results, its qualitative properties.

While trying to get a service from the Base station it happens that many devices will find themselves competing to get the cellular service, which means each device will use the maximum power possible to win the competition, but if there is another solution that will allow the device to get what it wanted with minimum of energy, how the device will react to it? In other words, we know that the communication through a Device-to-Device (D2D) link is done with a minimum of transmitted power, so why not optimize the power in a cellular communication by introducing D2D communication (i.e. switching from competition to cooperation). In this paper we will focus on studying the players'behavior based on quitting games(i.e. a non cooperative game theory), specifying both the pure and mixed Nash equilibrium during a competition between two devices while communicating with the base station, as well as calculating the probability of quitting and the average quitting time.

Device-to-device communications aim to enhance service coverage, particularly at cell edges or in black spots within a neighborhood through relaying and forwarding. In this context, each device is free to move independently, and will therefore change its links to other devices frequently due to connectivity issues. During these movements, a device may be requested to forward traffic unrelated to its own use, and therefore be a temporary router or a relay. This paper analyses the forwarding dilemma from different perspective using psychological game theory. It also provides different approaches that try to solve the forwarding problem in multi-hop relay-enabled networks by means of empathy, mutual support and partial altruism.

This paper investigates the delay minimization problem for instantly decodable network coding (IDNC) based device-to-device (D2D) communications. In D2D enabled systems, users cooperate to recover all their missing packets. The paper proposes a game theoretic framework as a tool for improving the distributed solution by overcoming the need for a central controller or additional signaling in the system. The session is modeled by self-interested players in a non-cooperative potential game. The utility functions are designed so as increasing individual payoff results in a collective behavior which achieves both a desirable system performance in a shared network environment and the Nash equilibrium. Three games are developed whose first reduces the completion time, the second the maximum decoding delay and the third the sum decoding delay. The paper, further, improves the formulations by including a punishment policy upon collision occurrence so as to achieve the Nash bargaining solution. Learning algorithms are proposed for systems with complete and incomplete information, and for the imperfect feedback scenario. Numerical results suggest that the proposed game-theoretical formulation provides appreciable performance gain against the conventional point-to-multipoint (PMP), especially for reliable user-to-user channels.

In energy harvesting communications, the transmitters have to adapt transmission to availability of energy harvested during the course of communication. The performance of the transmission depends on the channel conditions which vary randomly due to mobility and environmental changes. In this paper, we consider the problem of power allocation taking into account the energy arrivals over time and the degree of channel state information (CSI) available at the transmitter, in order to maximize the throughput. Differently from previous work, the CSI at the transmitter is not perfect and may include estimation errors. We solve this problem with respect to the causality and energy storage constraints. We determine the optimal offline policy in the case where the channel is assumed to be perfectly known at the receiver. Also, we obtain the power policy when the transmitter has no CSI. Furthermore, we analyze the asymptotic average throughput in a system where the average recharge rate goes asymptotically to zero.

Device-to-device (D2D) communications, one of the major component of the evolving 5G networks, is showing promising advantages on supporting machine-to-machine (M2M) communications. In this paper, we consider the design of efficient transmission strategy selection algorithm for M2M communications underlaying cellular networks. First, a group of machine type-devices (MTDs) is matched with a particular user equipment (UE). MTDs belonging to the same group can access the same spectrum within its matched UE while the latter quality of service (QoS) is maintained. Next, we propose an efficient evolutionary game based transmission strategy selection algorithm for M2M communications using D2D mode. Specifically, MTDs switch opportunistically from a non-cooperative strategy to a cooperative strategy. Initially, we consider a non-cooperative scenario due to the selfish behavior of devices. In case the latter QoS is not satisfied, MTDs switch to a cooperative game. In a cooperative game, we propose two alternative power control schemes: a fixed mixed-strategy power control scheme where each MTD willing to play cooperatively selects the power strategy from a discrete level of powers and an adaptive mixed-strategy power control scheme. The latter technique enables to set efficiently the discrete power levels using a fuzzy logic and a proportional-integral-derivative (PID) controllers aiming to assure the desired QoS of UEs while maximizing the efficiency of M2M communications. Simulation results show that the evolutionary game based transmission strategy selection algorithm avoids significant degradation of traditional human-to-human (H2H) services in terms of throughput and fairness compared to a single non-cooperative game strategy. Besides, the adaptive mixed-strategy power control scheme outperforms the fixed mixed-strategy power control scheme by saving the battery life of MTDs while guaranteeing the latter QoS.

In this paper, we consider last level cache (LLC) sharing problems in large-scale cloud networks with a fair payoff function. We formulate the problem as a strategic decision-making problem (i.e., a game). We examine the resource-sharing game with finite and infinite number of players. Exploiting the aggregate structure of the payoff functions, we show that the resource-sharing game has a Nash equilibrium in a wide range of return index. We show that the Nash equilibrium is not an evolutionarily stable strategy in the finite regime. Then, we introduce a myopic mean-field response where each player implements a mean-field-taking strategy. We show that such a mean-field-taking strategy is an evolutionarily stable strategy in both finite and infinite regime. We provide closed-form expression of the optimal pricing that gives an efficient resource-sharing policy. As the number of active players grows without bound, we show that the equilibrium strategy converges to a mean-field equilibrium, and the optimal prices for resources converge to the optimal price of the mean-field game. Then, we address the demand satisfaction problem for which a necessary and sufficient condition for satisfactory solutions is provided. In addition, a very fast mean-field learning algorithm is provided.

Nowadays, Small-Cells are widely being deployed to assist and improve performance of mobile networks. Indeed, they are a promising solution to improve coverage and to offload data traffic in mobile networks. In this paper, we propose a signaling-less architecture of the heterogeneous network composed of one single Macro Base Station and a Single Small-Cell. First, we construct a game theoretic framework for channel-state independent interaction. We present many conditions for the existence of Pure Nash equilibrium. Next, and in order to capture the continuous change of the channel state, we build a random matrix game where the channel state is considered to be random (potentially ruled by some given distribution). A characterization of Nash equilibrium is provided in terms of pure strategies and mixed strategies. Convergence to Nash equilibrium is furthermore guaranteed using a variant of the well-known Combined fully distributed payoff and strategy learning. Our algorithm converges faster (only 10–20 iterations are required to converge to Nash equilibrium) and only need a limited amount of local information. This is quite promising since it says that our scheme is almost applicable for all environments (fast fading included).

The SC-FDMA (single-carrier frequency division multiple access) is the access scheme that has been adopted by 3GPP (3rd generation partnership project) for the LTE (long term evolution) uplink. The SC-FDMA is an attractive alternative to OFDMA (orthogonal frequency-division multiple access) especially on the uplink owing to its low peak-to-average power ratio. This fact increases the power efficiency and reduces the cost of the power amplifiers at the mobile terminals. The use of SC-FDMA on the uplink implies that for highly loaded cells the base station allocates a single subcarrier to each user. This results in the limitation of the achievable rate on the uplink. In this work, we propose a coalition game between mobile terminals that allows them to improve their performance by sharing their subcarriers without creating any interference to each other. The proposed scheme allows a fair use of the subcarriers and leads to a significant capacity gain for each user. The cooperation between the nodes is modelled using coalitional game theory. In this game, each coalition tries to maximize its utility in terms of rate. In the absence of cooperation cost, it can be shown that the grand coalition is sum-rate optimal and stable, i.e., the mobile terminals have no incentive to leave the grand coalition.

In this paper, we introduce a game theoretic framework for studying the problem of minimizing the completion time of instantly decodable network coding (IDNC) for cooperative data exchange (CDE) in decentralized wireless network. In this configuration, clients cooperate with each other to recover the erased packets without a central controller. Game theory is employed herein as a tool for improving the distributed solution by overcoming the need for a central controller or additional signaling in the system. We model the session by self-interested players in a non-cooperative potential game. The utility function is designed such that increasing individual payoff results in a collective behavior achieving both a desirable system performance in a shared network environment and the Pareto optimal solution. We further show that our distributed solution achieves the centralized solution. Through extensive simulations, our approach is compared to the best performance that could be found in the conventional point-to-multipoint (PMP) recovery process. Numerical results show that our formulation largely outperforms the conventional PMP scheme in most practical situations and achieves a lower delay.

Mean-field games have been studied under the assumption of very large number of players. For such large systems, the basic idea consists of approximating large games by a stylized game model with a continuum of players. The approach has been shown to be useful in some applications. However, the stylized game model with continuum of decision-makers is rarely observed in practice and the approximation proposed in the asymptotic regime is meaningless for networks with few entities. In this paper, we propose a mean-field framework that is suitable not only for large systems but also for a small world with few number of entities. The applicability of the proposed framework is illustrated through various examples including dynamic auction with asymmetric valuation distributions, and spiteful bidders.

In this paper we consider a random access system where each user can be in two modes of operation, has a packet or not and the set of users which have a packet is available to a shared medium. We propose an evolving coalitional game theory to analyze the system outcomes. Unlike classical coalitional approaches that assume that coalitional structures are fixed and formed with cost-free, we explain how coalitions can be formed in a fully distributed manner using evolutionary dynamics and coalitional combined fully distributed payoff and strategy (CODIPAS) learning. We introduce the concept of evolutionarily stable coalitional structure (ESCS), which is, when it is formed it is resilient by small perturbation of strategies. We show that (i) the formation and the stability of coalitions depend mainly on the cost of making a coalition compared to the benefit of cooperation, (ii) the grand coalition can be unstable and a localized coalitional structure is formed as an evolutionarily stable coalitional structure. When the core is empty, the coalitional CODIPAS scheme selects one of the stable sets. Finally, we discuss the convergence and complexity of the proposed coalitional CODIPAS learning in access control with different users' activities.

Inter-operator spectrum sharing has been proposed to exploit spectrum efficiency in future communication system. By using game theory and learning algorithm, we design a partially distributed implementation method. Such a method is proven to reach the system equilibrium which improves spectrum efficiency between operators. Furthermore, by queueing analysis and robust potential game, we show that the convergence rate can be accelerated by incorporating more asynchronous operator systems. This distributed implementation method creates a low complexity solution for operators to realize inter-operator spectrum sharing for 3GPP LTE-A systems or similar.

This paper deals with the problem of joint sensing and medium accessing in cognitive radio networks. We design a non-cooperative two-step game to describe the Sense-Transmit-Wait paradigm in an opportunistic point of view. We give a full characterization of the Nash equilibria and analyze the optimal pricing policy, from the network owner view, for both centralized setting and decentralized setting. Next, we propose a combined learning algorithm that is fully distributed and allows the cognitive users to learn their optimal payoffs and their optimal strategies in both symmetric and asymmetric cases. The derived results are illustrated by numerical results and provide some insights on how to deploy cognitive radios in medium access cognitive radio networks in terms of sensing capabilities.

In recent years, many approaches and techniques have been explored for the optimization of energy usage in Wireless Sensor Networks (WSN). It is well recognized that a proper energy consumption model is the foundation for developing and evaluating a power management scheme in WSN. In this paper, we propose a new complete information Markov Decision Process (MDP) model to characterize sensors energy levels. We also propose and compare several centralized power control policies to select the more efficient policy that optimizes throughput and energy consumption.

In this article, we investigate the competitive interaction between electrical vehicles or hybrid oil-electricity vehicles in a Cournot market consisting of electricity transactions to or from an underlying electricity distribution network. We provide a mean field game formulation for this competition, and introduce the set of fundamental differential equations ruling the behavior of the vehicles at the feedback Nash equilibrium, referred here to as the mean field equilibrium. This framework allows for a consistent analysis of the evolution of the price of electricity as well as of the instantaneous electricity demand in the power grid. Simulations precisely quantify those parameters and suggest that significant reduction of the daily electricity peak demand can be achieved by appropriate electricity pricing.

In this paper, we study game dynamics and learning schemes for heterogeneous 4G networks. We introduce a novel learning scheme called cost-to-learn that incorporates the cost to switch, the switching delay, and the cost of changing to a new action and, captures the realistic behavior of the users that we have experimented on OPNET simulations. Considering a dynamic and uncertain environment where the users and operators have only a numerical value of their own payoffs as information, we construct various heterogeneous combined fully distributed payoff and strategy reinforcement learning (CODIPAS-RL): the users try to learn their own optimal payoff and their optimal strategy simultaneously. We establish the asymptotic pseudo-trajectories as solution of differential equations. Using evolutionary game dynamics, we prove the convergence and stability properties in specific classes of dynamic robust games. We provide various numerical examples and OPNET simulations in the context network selection in wireless local area networks (WLAN) and Long Term Evolution (LTE).

In this paper, we consider the challenging problem of power allocation in interfering wireless networks with services having stringent delay constraints. The objective is to allocate optimally the power to each user, take advantage of the time diversity of the time varying channel and satisfy the delay constraints of the services. To this end, we model the bit rate dynamics of the users as a linear stochastic control scheme and formulate a risk-sensitive control framework that minimizes an exponential cost function of the deviation between the traffic arrivals and departures from the queues. We provide the optimal solution of the power control problem and provide simulation results to assess the superiority of our solution compared to some existing solutions.

In this paper, we study dynamic robust power-allocation games in multiple-input-multiple-output systems under the imperfectness of the channel-state information at the transmitters. Using a robust pseudopotential-game approach, we show the existence of robust solutions in both discrete and continuous action spaces under suitable conditions. Considering the imperfectness in terms of the payoff measurement at the transmitters, we propose a COmbined fully DIstributed Payoff and Strategy Reinforcement Learning (CODIPAS-RL) in which each transmitter learns its payoff function, as well as the associated optimal covariance matrix strategies. Under the heterogeneous CODIPAS-RL, the transmitters can use different learning patterns (heterogeneous learning) and different learning rates. We provide sufficient conditions for the almost-sure convergence of the heterogeneous learning to ordinary differential equations. Extensions of the CODIPAS-RL to Itô's stochastic differential equations are discussed.

This paper is concerned with the concept of equilibrium and quality of service (QoS) provisioning in self-configuring wireless networks with non-cooperative radio devices (RD). In contrast with the Nash equilibrium (NE), where RDs are interested in selfishly maximizing its QoS, we present a concept of equilibrium, named satisfaction equilibrium (SE), where RDs are interested only in guaranteing a minimum QoS. We provide the conditions for the existence and the uniqueness of the SE. Later, in order to provide an equilibrium selection framework for the SE, we introduce the concept of effort or cost of satisfaction, for instance, in terms of transmit power levels, constellation sizes, etc. Using the idea of effort, the set of efficient SE (ESE) is defined. At the ESE, transmitters satisfy their minimum QoS incurring in the lowest effort. We prove that contrary to the (generalized) NE, at least one ESE always exists whenever the network is able to simultaneously support the individual QoS requests. Finally, we provide a fully decentralized algorithm to allow self-configuring networks to converge to one of the SE relying only on local information.

We consider a noncooperative interaction among a large population of mobiles that interfere with each other through many local interactions. The first objective of this paper is to extend the evolutionary game framework to allow an arbitrary number of mobiles that are involved in a local interaction. We allow for interactions between mobiles that are not necessarily reciprocal. We study 1) multiple-access control in a slotted Aloha-based wireless network and 2) power control in wideband code-division multiple-access wireless networks. We define and characterize the equilibrium (called evolutionarily stable strategy) for these games and study the influence of wireless channels and pricing on the evolution of dynamics and the equilibrium.

In this paper, we propose an evolutionary game-theoretic framework for hybrid additive white Gaussian noise multiple access channels. We consider a communication system consisting of multiple users and multiple receivers, where each user chooses a rate and splits it over the receivers. Users have coupled constraints determined by the capacity regions. We show the existence of Nash equilibrium under general conditions and characterize the equilibria of the static game. Building upon the static game, we formulate a system of hybrid evolutionary game dynamics using G-function dynamics and Smith dynamics on rate control and channel selection, respectively. We show that the evolutionary hybrid multiple access game has an equilibrium and illustrate these dynamics with numerical examples.

We study a dynamic random access game with a finite number of opportunities for transmission and with energy constraints. We provide sufficient conditions for feasible strategies and for existence of Nash-Pareto solutions and show that finding Nash-Pareto policies of the dynamic random access game is equivalent to partitioning the set of time slot opportunities with constraints into a set of terminals. We further derive upper bounds for pure Nash-Pareto policies, and extend the study to non-integer energy constraints and unknown termination time, where Time Division Multiplexing policies can be suboptimal. We show that the dynamic random access game has several strong equilibria (resilient to coalition of any size), and we compute them explicitly. We introduce the (strong) price of anarchy concept to measure the gap between the payoff under strong equilibria and the social optimum.

We consider evolving games with finite number of players, in which each player interacts with other randomly selected players. The types and actions of each player in an interaction together determine the instantaneous payoff for all involved players. They also determine the rate of transition between type-actions. We provide a rigorous derivation of the asymptotic behavior of this system as the size of the population grows. We show that the large population asymptotic of the microscopic model is equivalent to a macroscopic evolutionary game in which a local interaction is described by a single player against an evolving population profile. We derive various classes of evolutionary game dynamics. We apply these results to spatial random access games in wireless networks.

Information dissemination is one of the most important aspects of routing mechanisms for network management. We study in this paper the problem of disseminating an information by a source trough all the nodes of a network. We consider an epidemic routing protocol in an Ad hoc network with a delayed feedback to the source. Indeed, the source controls the dissemination of the information in the network by sending an anti-packet, like a vaccine. We show that there exists a sufficient condition for stability of the information dissemination in the network depending on the spreading of information in the network and the delay of the feedback to the source.

A major contribution of biology to competitive decision making is the development of the discipline of evolutionary games. Its ESS (evolutionary stable strategy) equilibrium concept, well adapted to large populations of players, describes robustness against deviations of a whole fraction of the population (in contrast to Nash equilibrium that requires robustness against a single user's deviation). The second appealing element of evolutionary games, the replicator dynamics, describes the evolution of strategies in time. Under suitable conditions, this (and some other) dynamics, converge to an ESS. In this paper we study the effect of slow time scale delays on the convergence of various dynamics. We apply this to an evolutionary game describing competition between mobile terminals over the access to a common channel.