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The deployment of RISs in future 6G networks is expected to substantially improve mobile network coverage. This paper introduces a new cross-layer low-complexity scheme for the online optimization of RIS-assisted communication systems. It jointly combines BS and RIS configuration and fair UEs’ scheduling which is critical for high-performance deployments. A RIS beam synthesis method is especially proposed for RIS configuration. The proposed solution embeds two nested control loops: i) a fast control loop working at the OFDMA slot scale and consisting in a standard UEs proportional fair scheduler, and ii) a slow control loop operating at the OFDMA frame scale which adapts the RIS’ configuration to the UEs’ spatial distribution and maximizes the UEs’ aggregated performance. The slow control loop is based on an online stochastic approximation algorithm whose convergence to the optimal restpoint is proved. In a reference scenario, the proposed scheduler achieves a gain of 47% in mean spectral efficiency for NLOS UEs over a baseline scheme.

Masks save lives. Therefore, while the culture of wearing masks is promoted, it is critical to understand the various aspects of how that culture is adopted. The main contribution of this paper is in the modeling of the mask game. Wearing a mask provides partial protection against epidemics at some cost of comfort. Players can be differentiated according to both their risk state as well as their health state (susceptible, infected and removed). We formulate the problem as a Bayesian game in which players know their own risk state and ignore their own health state and the health and risk states of their counterparts. Using ideas from evolutionary games, we reduce the problem to a one-shot equivalent game and describe the structure of the symmetric equilibria. We prove that the policies adopted by the players at such equilibria admit a threshold structure. More specifically, players wear masks only if their risk state is equal to or bigger than a given threshold.

This preface introduces the special issue on Dynamic Games for Modeling and Control of Epidemics. It showcases 12 papers with timely contributions to dynamic games and their applications to the modeling, analysis, and control of epidemics. The papers in this collection connect dynamic games and epidemic models to address the recent challenges related to screening, containment, and mitigation strategies for epidemics. This collection covers broad application areas in networks, human behaviors, and epidemiology as well as a diverse range of dynamic game methods, including evolutionary games, differential games, and mean-field games.

An investigation of the decision of self-optimizing users to participate in a processor sharing system was conducted. In the scenario examined in this study, potential computer users, who want to minimize their own service time, arrive sequentially at a computer center, each deciding to link to a central mainframe computer or use a personal computer. The service rate at the mainframe computer is reduced as its load increases. As they arrive, users determine the current load by looking at the number of other users in the mainframe but also have to anticipate the possible load throughout their service time to predict their own service time. Based on the current and possible load, users have to decide if they will use the mainframe or the personal computer. The problem was examined in a game theoretic framework. The Nash equilibrium solution for the resulting dynamic game was analyzed.

An alternative approach based on pathwise bounds and the existence of long-run average rates was utilized to model both the input process and the service mechanisms in vacation and polling queueing systems. The input and output processes were correlated with model-dependent conditions that characterized bounds by their average rate and burstiness constraints. Downtime corresponded to a number of factors such as vacation time, switchover and server operations. The model integrated a time axis feature divided into a sequence of disjoint time cycles and was also capable of supporting arrival and departure processes. Some of its possible applications include communication systems and flexible manufacturing processes.

Policy time sharing (PTS) solutions are derived using a category of nonstationary policies that are ideal for solving Markov processes with average-cost decision criteria. These PTS-type policies are easy to implement and have relatively straightforward computation procedures. Accordingly, such policies can be applied to certain adaptive problems that involve constrained optimization such as those that arise in telecommunications networks. The application of these optimal solutions requires switching between the use of several stationary policies until a predetermined state is reached. The use of PTS-type policies for other optimization problems such as those involving compact action space is also discussed.

We model the competition over several blockchains characterizing multiple cryptocurrencies as a non-cooperative game. Then, we specialize our results to two instances of the general game, showing properties of the Nash equilibrium. In particular, leveraging results about congestion games, we establish the existence of pure Nash equilibria and provide efficient algorithms for finding such equilibria.

Designing an optimal network topology while balancing multiple, possibly conflicting objectives such as cost, performance, and resiliency to viruses is a challenging endeavor, let alone in the case of decentralized network formation. We, therefore, propose a game-formation technique where each player aims to minimize its cost in installing links, the probability of being infected by a virus, and the sum of hopcounts on its shortest paths to all other nodes. In this paper, we first determine the Nash equilibria and the price of anarchy (PoA) for our novel network formation game, second demonstrate that the PoA is usually low, which suggests that (near-)optimal topologies can be formed in a decentralized way, and third give suggestions for practitioners for those cases where the PoA is high and some centralized control/incentives are advisable.

Standard evolutionary game theory framework is a useful tool to study large interacting systems and to understand the strategic behavior of individuals in such complex systems. Adding an individual state to model local feature of each player in this context allows one to study a wider range of problems in various application areas as networking, biology, etc. In this paper, we introduce such an extension of evolutionary game framework and particularly, we focus on the dynamical aspects of this system. Precisely, we study the coupled dynamics of the policies and the individual states inside a population of interacting individuals. We first define a general model by coupling replicator dynamics and continuous-time Markov decision processes, and we then consider a particular case of a two policies and two states evolutionary game. We first obtain a system of combined dynamics, and we show that the rest points of this system are equilibria profiles of our evolutionary game with individual state dynamics. Second, by assuming two different timescales between states and policies dynamics, we can compute explicitly the equilibria. Then, by transforming our evolutionary game with individual states into a standard evolutionary game, we obtain an equilibrium profile which is equivalent, in terms of occupation measures and expected fitness to the previous one. All our results are illustrated with numerical analysis.

In this paper, we propose a distributed network selection scheme for the heterogeneous wireless network environment. The network selection problem is formulated as a multiobjective optimization problem which maximizes the channel capacity and minimizes the blocking probability simultaneously. By taking the throughput metric into consideration, the formulated multiobjective optimization problem is transformed into a maximization problem. We solve the transformed maximization problem to calculate the network selection result in a distributed method. The calculated network selection result proves to be a Pareto Optimal solution of the original multiobjective optimization problem. The proposed scheme guarantees that based on limited local information, each user can select a new network device with high channel capacity and low blocking probability. Comprehensive experiment results show that the proposed scheme promotes the total throughput and user-served ratio significantly.

We study a nonzero-sum game considered on the solutions of a hybrid dynamical system that evolves in continuous time and that is subjected to abrupt changes in parameters. The changes in the parameters are synchronized with (and determined by) the changes in the states–actions of two Markov decision processes, each of which is controlled by a player who aims at minimizing his or her objective function. The lengths of the time intervals between the “jumps” of the parameters are assumed to be small. We show that an asymptotic Nash equilibrium of such hybrid game can be constructed on the basis of a Nash equilibrium of a deterministic averaged dynamic game.

Cache networks are one of the building blocks of information centric networks (ICNs). Most of the recent work on cache networks has focused on networks of request driven caches, which are populated based on users requests for content generated by publishers. However, user generated content still poses the most pressing challenges. For such content timelines are the de facto sharing solution. In this paper, we establish a connection between timelines and publisher-driven caches. We propose simple models and metrics to analyze publisher-driven caches, allowing for variable-sized objects. Then, we design two efficient algorithms for timeline workload shaping, leveraging admission and price control in order, for instance, to aid service providers to attain prescribed service level agreements.

We study a mean-field approximation of the M/M/$$\infty $$ ∞ queueing system. The problem we deal is quite different from standard games of congestion as we consider the case in which higher congestion results in smaller costs per user. This is motivated by a situation in which some TV show is broadcast so that the same cost is needed no matter how many users follow the show. Using a mean-field approximation, we show that this results in multiple equilibria of threshold type which we explicitly compute. We further derive the social optimal policy and compute the price of anarchy. We then study the game with partial information and show that by appropriate limitation of the queue-state information obtained by the players, we can obtain the same performance as when all the information is available to the players. We show that the mean-field approximation becomes tight as the workload increases, thus the results obtained for the mean-field model well approximate the discrete one.

We consider a game of timing between a random number of content creators, who compete for position and exposure time over an ordered shared medium such as an online classified list. Contents (such as ads, messages, multimedia items, or comments) are ordered according to their submission times, with more recent submissions displayed at the top (and better) positions. The instantaneous effectiveness of each item depends on its current display position, as well as on a time-dependent site exposure function which is common to all. Each content creator may choose the submission time of his or her item within a finite time interval, with the goal of maximizing the total exposure of this item. We formulate the problem as a noncooperative game and analyze its symmetric Nash equilibrium. We show existence of the equilibrium profile, characterize it in terms of a differential boundary value problem, provide sufficient conditions for its uniqueness, and devise a numerical scheme for its computation. We further compute the equilibrium profile explicitly for certain special cases, which include a two-player small match and a Poisson-distributed number of players, and evaluate the social efficiency of these equilibria.

Ensuring system survivability in the wake of advanced persistent threats is a big challenge that the security community is facing to ensure critical infrastructure protection. In this paper, we define metrics and models for the assessment of coordinated massive malware campaigns targeting critical infrastructure sectors. First, we develop an analytical model that allows us to capture the effect of neighborhood on different metrics (e.g., infection probability and contagion probability). Then, we assess the impact of putting operational but possibly infected nodes into quarantine. Finally, we study the implications of scanning nodes for early detection of malware (e.g., worms), accounting for false positives and false negatives. Evaluating our methodology using an hierarchical topology typical of factory automation networks, we find that malware infections can be effectively contained by using quarantine and appropriate rates of scanning for soft impacts.

Defining an optimal protection strategy against viruses, spam propagation, or any other kind of contamination process is an important feature for designing new networks and architectures. In this paper, we consider decentralized optimal protection strategies when a virus is propagating over a network through an SIS epidemic process. We assume that each node in the network can fully protect itself from infection at a constant cost, or the node can use recovery software, once it is infected. We model our system using a game-theoretic framework and find pure, mixed equilibria, and the Price of Anarchy in several network topologies. Further, we propose a decentralized algorithm and an iterative procedure to compute a pure equilibrium in the general case of a multiple communities network. Finally, we evaluate the algorithms and give numerical illustrations of all our results.

Self-organizing network (SON) technology aims at autonomously deploying, optimizing and repairing radio access networks (RANs). SON algorithms typically use key performance indicators (KPIs) from the RAN. It is shown that in certain cases, it is essential to take into account the impact of the backhaul state in the design of the SON algorithm. We revisit the base station (BS) load definition taking into account the backhaul state. We provide an analytical formula for the load along with a simple estimator for both elastic and guaranteed bit-rate (GBR) traffic. We incorporate the proposed load estimator in a self-optimized load balancing (LB) algorithm. Simulation results for a backhaul constrained heterogeneous network illustrate how the correct load definition can guarantee a proper operation of the SON algorithm.

In this short paper, we show two new algorithms for finding stable structures in ordinal coalition potential games. The first one is anytime and enumerative. It performs on a graph. The second one is a modified Deferred Acceptance Algorithm (DAA) using counter-proposals. It finds a many-to-one matching. We illustrate with the example of video caching from a content creator's servers to a service provider's servers.

The classic optimal transportation problem consists in finding the most cost-effective way of moving masses from one set of locations to another, minimizing its transportation cost. The formulation of this problem and its solution have been useful to understand various mathematical, economical, and control theory phenomena, such as, e.g., Witsenhausen's counterexample in stochastic control theory, the principal-agent problem in microeconomic theory, location and planning problems, etc. In this work, we incorporate the effect of network congestion to the optimal transportation problem and we are able to find a closed form expression for its solution. As an application of our work, we focus on the mobile association problem in cellular networks (the determination of the cells corresponding to each base station). In the continuum setting, this problem corresponds to the determination of the locations at which mobile terminals prefer to connect (by also considering the congestion they generate) to a given base station rather than to other base stations. Two types of problems have been addressed: a global optimization problem for minimizing the total power needed by the mobile terminals over the whole network (global optimum), and a user optimization problem, in which each mobile terminal chooses to which base station to connect in order to minimize its own cost (user equilibrium). This work combines optimal transportation with strategic decision making to characterize both solutions. [ABSTRACT FROM AUTHOR]

The global Internet has enabled a massive access of internauts to content. At the same time, it allowed individuals to use the Internet in order to distribute content. When individuals pass through a content provider to distribute contents, they can benefit from many tools that the content provider has in order to accelerate the dessimination of the content. These include caching as well as recommendation systems. The content provider gives preferential treatment to individuals who pay for advertisement. In this paper, we study competition between several contents, each characterized by some given potential popularity. We answer the question of when is it worthwhile to invest in advertisement as a function of the potential popularity of a content as well as its competing contents, who are faced with a similar question. We formulate the problem as a stochastic game with a finite state and action space and obtain the structure of the equilibria policy under a linear structure of the dissemination utility as well as on the advertisement costs. We then consider open loop control (no state information) and solve the game using a transformation into a differential game with a compact state space.The global Internet has enabled a massive access of internauts to content. At the same time, it allowed individuals to use the Internet in order to distribute content. When individuals pass through a content provider to distribute contents, they can benefit from many tools that the content provider has in order to accelerate the dessimination of the content. These include caching as well as recommendation systems. The content provider gives preferential treatment to individuals who pay for advertisement. In this paper, we study competition between several contents, each characterized by some given potential popularity. We answer the question of when is it worthwhile to invest in advertisement as a function of the potential popularity of a content as well as its competing contents, who are faced with a similar question. We formulate the problem as a stochastic game with a finite state and action space and obtain the structure of the equilibria policy under a linear structure of the dissemination utility as well as on the advertisement costs. We then consider open loop control (no state information) and solve the game using a transformation into a differential game with a compact state space.

We revisit, in this paper, the well-studied Hawk and Dove game within a dynamic framework. A non-standard evolutionary game approach is taken, in which the starting point of the modeling is the dynamic evolution of the population as a function of the strategies used, instead of a fitness-based model (in which the fitness functions determine the evolution). This work is motivated by the discussion in the book of Thomas L. Vincent co-authored with J. Brown (Evolutionary game theory, natural selection and Darwinian dynamics) in which they raise (on p. 73) the puzzling question of whom should one consider to be the players: the individuals or the populations?We revisit, in this paper, the well-studied Hawk and Dove game within a dynamic framework. A non-standard evolutionary game approach is taken, in which the starting point of the modeling is the dynamic evolution of the population as a function of the strategies used, instead of a fitness-based model (in which the fitness functions determine the evolution). This work is motivated by the discussion in the book of Thomas L. Vincent co-authored with J. Brown (Evolutionary game theory, natural selection and Darwinian dynamics) in which they raise (on p. 73) the puzzling question of whom should one consider to be the players: the individuals or the populations?

This paper studies the impact of exclusive contracts between a content provider (CP) and an internet service provider (ISP) in a nonneutral network. We consider a simple linear demand function for the CPs. We studywhen an exclusive contract is benefcial to the colluding pair and evaluate its impact on the noncolluding players at equilibrium. For the case of two CPs and one ISP we show that collusion may not always be benefcial. We derive an explicit condition in terms of the advertisement revenues of the CPs that tells when a collusion is proftable to the colluding entities.

Containment of worms constitutes an important challenge in mobile wireless networks as recent outbreaks have revealed actual vulnerabilities. We introduce a defense strategy that quarantines the malware by reducing the communication range. This countermeasure confronts us with a tradeoff: reducing the communication range suppresses the spread of the malware; however, it also deteriorates the network performance. We model the propagation of the malware as a deterministic epidemic. Using an optimal control framework, we select the optimal communication range that captures the above tradeoff by minimizing a global cost function. Using Pontryagin's Maximum Principle, we derive structural characteristics of the optimal communication range as a function of time for general cost functions. Our numerical computations reveal that the dynamic optimal control of the communication range significantly outperforms static choices and is also robust to errors in estimation of the network and attack parameters. [ABSTRACT FROM PUBLISHER]

The growing awareness to negative impact of wireless technology on our environment has lead to designing green networks in which energy saving plays an important role. We consider energy saving by switching off a fraction of the base stations. This saving comes at some cost: the coverage is reduced, and moreover, the uplink transmission power of mobiles may increase. This may imply exposure of the human body to stronger electromagnetic fields. We quantify this through the deactivation of base stations under the assumptions that the random location of base stations and mobiles form Poisson processes. Our model yields explicit expressions for both the uplink power and the coverage probability. For the case with non negligible interference, the uplink power is obtained by using stochastic differential equations assuming exponential attenuation model. We observe that when the mobiles have no power constraints, unlike in the case of negligible interference, switching off base stations reduces the uplink power.

We study the optimal control of propagation of packets in delay tolerant mobile ad-hoc networks. We consider a two-hop forwarding policy under which the expected number of nodes carrying copies of the packets obeys a linear dynamics. We exploit this property to formulate the problem in the framework of linear quadratic optimal control which allows us to obtain closed-form expressions for the optimal control and to study numerically the tradeoffs by varying various parameters that define the cost. [ABSTRACT FROM AUTHOR]

Much research has been devoted to the tradeoff between throughput and power consumption. Yet little has been done concerning another central challenge: the limitation on battery life. In this paper we study power control in a way that combines all above-mentioned aspects. We propose an appropriate performance metric and establish an optimization framework to analyze it: an adaptation of the Anonymous Sequential Game framework introduced in 1988 by Jovanovic and Rosenthal. We characterize the structure of the equilibrium policies, and provide a fast algorithm to reach equilibrium. [ABSTRACT FROM AUTHOR]

We present a class of evolutionary games involving large populations that have many pairwise interactions between randomly selected players. The fitness of a player depends not only on the actions chosen in the interaction but also on the individual state of the players. Players have a finite life time during which they participate in several local interactions and take actions. The actions taken by a player determine not only the immediate fitness but also the transition probabilities to its next individual state. We define and characterize the Evolutionary Stable Strategies for these games and propose a method to compute them. We illustrate the model and results through a networking problem. [ABSTRACT FROM AUTHOR]

We consider properties of constrained games, where the strategy set available to a player depends on the choice of strategies made by other players. We show that the utilities of each player associated with that player's own performance and constraints are not sufficient to model a constrained game and to define equilibria; for the latter, one also needs to model how a player values the fact that other players meet their constraints. We study three different approaches to other players' constraints, and show that they exhibit completely different equilibrium behaviors. Further, we study a general class of stochastic games with partial information, and focus on the case where the players are indifferent to whether the constraints of other players hold. [ABSTRACT FROM AUTHOR]

Consider the situation where, in a single network or system, several different types of atomic and nonatomic users coexist and have attained their own optima unilaterally. We call the combination of the optima a "mixed optimum." For a distributed system with identical nodes each having identical arrivals, we obtain the analytic expression of the unique mixed optimum, where mutual job forwarding among nodes may occur for some atomic users, resulting in paradoxical performance degradation. [ABSTRACT FROM AUTHOR]

In this paper we analyze connectivity issues in one-dimensional ad hoc networks. Starting with a deterministic channel model, we show how an equivalent GI|D|∞ queueing model may be used to address network connectivity. In this way, we obtain exact results for the coverage probability, the node isolation probability and the connectivity distance for various node placement statistics. We then show how a GI|G|∞ model may be used to study broadcast percolation problems in ad hoc networks with general node placement in the presence of fading channels. In particular, we obtain explicit results for the case of nodes distributed according to a Poisson distribution operating in a fading/shadowing environment. In the latter case, heavy traffic theorems are applied to derive the critical transmission power for connectivity and broadcast percolation distance in highly dense networks. The impact of signal processing schemes able to exploit the diversity provided by small-scale fading by means of multiple antennas is considered. The analysis is then extended to the case of unreliable ad hoc networks, with an in-depth discussion of asymptotic results.In this paper we analyze connectivity issues in one-dimensional ad hoc networks. Starting with a deterministic channel model, we show how an equivalent GI|D|∞ queueing model may be used to address network connectivity. In this way, we obtain exact results for the coverage probability, the node isolation probability and the connectivity distance for various node placement statistics. We then show how a GI|G|∞ model may be used to study broadcast percolation problems in ad hoc networks with general node placement in the presence of fading channels. In particular, we obtain explicit results for the case of nodes distributed according to a Poisson distribution operating in a fading/shadowing environment. In the latter case, heavy traffic theorems are applied to derive the critical transmission power for connectivity and broadcast percolation distance in highly dense networks. The impact of signal processing schemes able to exploit the diversity provided by small-scale fading by means of multiple antennas is considered. The analysis is then extended to the case of unreliable ad hoc networks, with an in-depth discussion of asymptotic results.

Abstract: The Discriminatory Processor Sharing (DPS) model is a multi-class generalization of the egalitarian Processor Sharing model. In the DPS model all jobs present in the system are served simultaneously at rates controlled by a vector of weights {gk > 0; k = 1,..., K }. If there are Nk jobs of class k present in the system, k = 1,..., K, each class-k job is served at rate $$g_k/\sum_{j=1}^K{g_j}{N_j}$$ . The present article provides an overview of the analytical results for the DPS model. In particular, we focus on response times and numbers of jobs in the system.

Many models for customers impatience in queueing systems have been studied in the past; the source of impatience has always been taken to be either a long wait already experienced at a queue, or a long wait anticipated by a customer upon arrival. In this paper we consider systems with servers vacations where customers’ impatience is due to an absenteeof servers upon arrival. Such a model, representing frequent behavior by waiting customers in service systems, has never been treated before in the literature. We present a comprehensive analysis of the single-server, M/M/1 and M/G/1 queues, as well as of the multi-server M/M/cqueue, for both the multiple and the single-vacation cases, and obtain various closed-form results. In particular, we show that the proportion of customer abandonments under the single-vacation regime is smaller than that under the multiple-vacation discipline.Many models for customers impatience in queueing systems have been studied in the past; the source of impatience has always been taken to be either a long wait already experienced at a queue, or a long wait anticipated by a customer upon arrival. In this paper we consider systems with servers vacations where customers’ impatience is due to an absenteeof servers upon arrival. Such a model, representing frequent behavior by waiting customers in service systems, has never been treated before in the literature. We present a comprehensive analysis of the single-server, M/M/1 and M/G/1 queues, as well as of the multi-server M/M/cqueue, for both the multiple and the single-vacation cases, and obtain various closed-form results. In particular, we show that the proportion of customer abandonments under the single-vacation regime is smaller than that under the multiple-vacation discipline.

Traditional definitions of capacity of CDMA networks are either related to the number of calls they can handle (pole capacity) or to the arrival rate that guarantees that the rejection rate (or outage) is below a given fraction (Erlang capacity). We extend the latter definition to other quality of service (QoS). We consider best-effort (BE) traffic sharing the network resources with real-time (RT) applications. As is often the case in CDMA systems, we assume that the BE traffic access is done using a time-shared channel (such as the HDR or the HSDPA). BE applications can adapt their instantaneous transmission rate to the available one and thus need not be subject to admission control or outages. Their meaningful QoS is the average delay (i.e. the sojourn time). The delay aware capacity is then defined as the arrival rate of BE calls that the system can handle such that their expected delay is bounded by a given constant. We compute in this paper both the blocking probability of the RT traffic having an adaptive Grade of Service (GoS) as well as the expected delay of the BE traffic for an uplink multicell WCDMA system. This yields the Erlang capacity for former and the delay capacity for the latter.Traditional definitions of capacity of CDMA networks are either related to the number of calls they can handle (pole capacity) or to the arrival rate that guarantees that the rejection rate (or outage) is below a given fraction (Erlang capacity). We extend the latter definition to other quality of service (QoS). We consider best-effort (BE) traffic sharing the network resources with real-time (RT) applications. As is often the case in CDMA systems, we assume that the BE traffic access is done using a time-shared channel (such as the HDR or the HSDPA). BE applications can adapt their instantaneous transmission rate to the available one and thus need not be subject to admission control or outages. Their meaningful QoS is the average delay (i.e. the sojourn time). The delay aware capacity is then defined as the arrival rate of BE calls that the system can handle such that their expected delay is bounded by a given constant. We compute in this paper both the blocking probability of the RT traffic having an adaptive Grade of Service (GoS) as well as the expected delay of the BE traffic for an uplink multicell WCDMA system. This yields the Erlang capacity for former and the delay capacity for the latter.

We propose two modeling approaches of additive-increase/multiplicative-decrease (AIMD) congestion control mechanisms. The first separates the increase and decrease parts whereas the second describes the rate evolution of the congestion window as a continuous process governed by a differential equation. We relate these approaches and show that the second (fluid-flow) model results from deterministic time averaging of the first (discontinuous) model. A generalized class of nonlinear protocols, which includes Floyd's HighSpeed TCP, is then proposed and analyzed. Our findings are validated by simulation. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyzes a single server queueing system in which service is alternated between two queues and the server requires a (finite) switchover time to switch from one queue to the other. The distinction from classical results is that the sequence of switchover times from each of the queues need not be i.i.d. nor independent from each other; each sequence is merely required to form a stationary ergodic sequence. With the help of stochastic recursive equations explicit expressions are derived for a number of performance measures, most notably for the average delay of a customer and the average queue lengths under different service disciplines. With these expressions a comparison is made between the service disciplines and the influence of correlation is studied. Finally, through a number of examples it is shown that the correlation can significantly increase the mean delay and the average queue lengths indicating that the correlation between switchover times should not be ignored. This has important implications for communication systems in which a common communication channel is shared amongst various users and where the time between consecutive data transfers is correlated (for example in ad-hoc networks). In addition to this a number of notational mistakes in well-known existing literature are pointed out.

We consider the vector fixed point equations arising out of the analysis of the saturation throughput of a single cell IEEE 802.11e wireless local area network with nodes that have different back-off parameters, including different Arbitration InterFrame Space (AIFS) values. We consider balanced and unbalanced solutions of the fixed point equations arising in homogeneous and nonhomogeneous networks. We are concerned, in particular, with (i) whether the fixed point is balanced within a class, and (ii) whether the fixed point is unique. Our simulations show that when multiple unbalanced fixed points exist in a homogeneous system then the time behaviour of the system demonstrates severe short term unfairness (or multistability). Implications for the use of the fixed point formulation for performance analysis are also discussed. We provide a condition for the fixed point solution to be balanced within a class, and also a condition for uniqueness. We then provide an extension of our general fixed point analysis to capture AIFS based differentiation; again a condition for uniqueness is established. An asymptotic analysis of the fixed point is provided for the case in which packets are never abandoned, and the number of nodes goes to ∞. Finally the fixed point equations are used to obtain insights into the throughput differentiation provided by different initial back-offs, persistence factors, and AIFS, for finite number of nodes, and for differentiation parameter values similar to those in the standard.

We study the parametric perturbation of Markov chains with denumerable state spaces. We consider both regular and singular perturbations. By the latter we mean that transition probabilities of a Markov chain, with several ergodic classes, are perturbed such that (rare) transitions among the different ergodic classes of the unperturbed chain are allowed. Singularly perturbed Markov chains have been studied in the literature under more restrictive assumptions such as strong recurrence ergodicity or Doeblin conditions. We relax these conditions so that our results can be applied to queueing models (where the conditions mentioned above typically fail to hold). Assuming ?-geometric ergodicity, we are able to explicitly express the steady-state distribution of the perturbed Markov chain as a Taylor series in the perturbation parameter. We apply our results to quasi-birth-and-death processes and queueing models.

We analyze a feedback system consisting of a finite buffer fluid queue and a responsive source. The source alternates between silence periods and active periods. At random epochs of times, the source becomes ready to send a burst of fluid. The length of the bursts (length of the active periods) are independent and identically distributed with some general distribution. The queue employs a threshold discarding policy in the sense that only those bursts at whose commencement epoch (the instant at which the source is ready to send) the workload (i.e., the amount of fluid in the buffer) is less than some preset threshold are accepted. If the burst is rejected then the source backs off from sending. We work within the framework of Poisson counter-driven stochastic differential equations and obtain the moment generating function and hence the probability density function of the stationary workload process. We then comment on the stability of this fluid queue. Our explicit characterizations will further provide useful insights and “engineering” guidelines for better network designing.

We consider a stream of packets that arrive at a queue with a finite buffer. A group of consecutive packets constitutes a frame. We assume that when an arriving packet finds the queue full, not only is the packet lost but also the future packets that belong to the same frame will be rejected. The first part of the paper deals with a detailed packet level queueing model; we obtain exact expressions for the stationary queue length distribution and the goodput ratio(i.e. the fraction of arriving frames that experience no losses). The second part deals with a fluid model and the fluid analysis leads to simple closed form expressions for the stationary workload process and the fluid goodput ratio.

We propose two novel on-line estimation algorithms to determine the size of a dynamic multicast group. We first use a Wiener filter to derive an optimal estimator for the membership size of the session in case the join process is Poisson and the lifetime of participants is distributed exponentially. We next develop the best first-order linear filter from which we derive an estimator that holds for any lifetime distribution. We apply this approach to the case where the lifetime distribution is hyperexponential. Both estimators hold under any traffic regime. Applying both estimators on real traces corresponding to video sessions, we find that both schemes behave well, one of which performs slightly better than the other in some cases. We further provide guidelines on how to tune the parameters involved in both schemes in order to achieve high quality estimation while simultaneously avoiding feedback implosion.

This note shows how centralized or distributed power control algorithms in wireless communications can be viewed as S-modular games coupled policy sets (coupling is due to the fact that the set of powers of a mobile that satisfy the signal-to-interference ratio constraints depends on powers used by other mobiles). This sheds a new light on convergence properties of existing synchronous and asynchronous algorithms, and allows us to establish new convergence results of power control algorithms. Furthermore, known properties of power control algorithms allow us to extend the theory of S-modular games and obtain conditions for the uniqueness of the equilibrium and convergence of best response algorithms independently of the initial state. [ABSTRACT FROM AUTHOR]

Acknowledgement (ACK) filtering has been proposed as a technique to alleviate the congestion at the input of a slow channel located on the reverse path of a TCP connection. Old ACKs waiting at the input of the slow channel are erased when new ACKs are to be queued. In the literature the case of one‐ACK per connection at a time has been studied. In this paper we show that this is too aggressive for short transfers where ACKs arrive in bursts due to the slow start phase, and where the TCP source needs to receive the maximum number of ACKs to increase fast its window. We study first static filtering where a certain ACK queue length is allowed. We show analytically how this length needs to be chosen. We present then some algorithms that adapt the filtering of ACKs as a function of the slow channel utilization rather than the ACK queue length. These algorithms provide a good compromise between reducing the ACK queueing delay and passing a large number of ACKs that guarantee a fast window increase. Copyright © 2003 John Wiley & Sons, Ltd.

We present a game-theoretic treatment of distributed power control in CDMA wireless systems. We make use of the conceptual framework of noncooperative game theory to obtain a distributed and market-based control mechanism. Thus, we address not only the power control problem, but also pricing and allocation of a single resource among several users. A cost function is introduced as the difference between the pricing and utility functions, and the existence of a unique Nash equilibrium is established. In addition, two update algorithms, namely, parallel update and random update, are shown to be globally stable under specific conditions. Convergence properties and robustness of each algorithm are also studied through extensive simulations.

Presents a noncooperative game framework for combined routing and flow control in a network of parallel links. Relation between the utility function of the user and power criterion; Characteristics of the utility function; Acquisition of a Nash equilibrium.

Examines the concept of mixed equilibrium (ME) for multiclass routing games. Integration of Nash equilibrium and Wardrop equilibrium; Existence and uniqueness of ME; Application of the mixed equilibrium to a parallel links network.

In this paper we introduce the notion of regular ordering for periodic sequences based on the gaps between the entries. We define the notion of regular preserving functions using Schur convexity. This is used to extend some optimization results in queuing control problems. In particular, we show that the maximal traveling time in a stochastic event graph as well as the transmission times in a channel with redundancy, decrease (in the stochastic sense) when the input sequence becomes more regular.

Examines a class of noncooperative general topology networks shared by N users. Adoption of a class of polynomial link cost functions; Efficiency and uniqueness of the Nash equilibrium; Discussion on the computation of the equilibrium in the special case of the affine cost structure for a topology of parallel links.

We study a class of noncooperative networks where Nusers send traffic to a destination node over two links with given capacities in such a way that a Nash equilibrium is achieved. Under a linear cost structure for the individual users, we obtain several dynamic policy adjustment schemes for the online computation of the Nash equilibrium and study their local convergence properties. These policy adjustment schemes require minimum information on the part of each user regarding the cost–utility functions of the others.

In this article, we study a method to compare queueing systems and their fluid limits. For a certain class of queueing systems, it is shown that the expected workload (and certain functions of the workload) is higher in the queueing system than in the fluid approximation. This class is characterized by convexity of the value function in the state component(s) where external arrivals occur. The main example that we consider is a tandem of multiserver queues with general service times and Markov-modulated arrivals. The analysis is based on dynamic programming and the use of phase-type distributions. Numerical examples to illustrate the results are also given.