 Electrical and Electronic Engineering  Research Publications
Electrical and Electronic Engineering  Research Publications
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ItemAchieving AIEnabled Robust EndtoEnd Quality of Experience Over Backhaul Radio Access NetworksRoy, D ; Rao, AS ; Alpcan, T ; Das, G ; Palaniswami, M (IEEEINST ELECTRICAL ELECTRONICS ENGINEERS INC, 20220901)Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The Endtoend (E2E) quality of experience (QoE) for these applications depends on the synchronous allocation of networking and computing resources. However, the relationship between the resources and the E2E QoE outcomes is typically stochastic and nonlinear. In order to make efficient resource allocation decisions, it is essential to model these relationships. This article presents a novel machinelearning based approach to learn these relationships and concurrently orchestrate both resources for this purpose. The machine learning models further help make robust allocation decisions regarding stochastic variations and simplify robust optimization to a conventional constrained optimization. When resources are insufficient to accommodate all application requirements, our framework supports executing some of the applications with minimal degradation (graceful degradation) of E2E QoE. We also show how we can implement the learning and optimization methods in a distributed fashion by the SoftwareDefined Network (SDN) and Kubernetes technologies. Our results show that deep learningbased modelling achieves E2E QoE with approximately 99.8% accuracy, and our robust jointoptimization technique allocates resources efficiently when compared to existing differential services alternatives.

ItemAchieving QoS for bursty uRLLC applications over passive optical networksRoy, D ; Rao, AS ; Alpcan, T ; Das, G ; Palaniswami, M (Optica Publishing Group, 20220501)Emerging realtime applications such as those classified under ultrareliable low latency (uRLLC) generate bursty traffic and have strict quality of service (QoS) requirements. The passive optical network (PON) is a popular access network technology, which is envisioned to handle such applications at the access segment of the network. However, the existing standards cannot handle strict QoS constraints for such applications. The available solutions rely on instantaneous heuristic decisions and maintain QoS constraints (mostly bandwidth) in an average sense. Existing proposals in generic networks with optimal strategies are computationally complex and are, therefore, not suitable for uRLLC applications. This paper presents a novel computationally efficient, farsighted bandwidth allocation policy design for facilitating bursty uRLLC traffic in a PON framework while satisfying strict QoS (age of information/delay and bandwidth) requirements. To this purpose, first we design a delaytracking mechanism, which allows us to model the resource allocation problem from a controltheoretic viewpoint as a model predictive control (MPC) problem. MPC helps in making farsighted decisions regarding resource allocations and captures the timevarying dynamics of the network. We provide computationally efficient polynomial time solutions and show their implementation in the PON framework. Compared to existing approaches, MPC can improve delay violations by 15% and 45% at loads of 0.8 and 0.9, respectively, for delayconstrained applications of 1 ms and 4 ms. Our approach is also robust to varying traffic arrivals.

ItemA framework for optimization under limited informationAlpcan, T (SPRINGER, 20130301)

ItemThe importance of spatial distribution when analysing the impact of electric vehicles on voltage stability in distribution networksde Hoog, J ; Muenzel, V ; Jayasuriya, DC ; Alpcan, T ; Brazil, M ; Thomas, DA ; Mareels, I ; Dahlenburg, G ; Jegatheesan, R (SPRINGER HEIDELBERG, 20150301)

ItemA Probabilistic Reverse Power Flows Scenario Analysis FrameworkDemazy, A ; Alpcan, T ; Mareels, I (IEEEINST ELECTRICAL ELECTRONICS ENGINEERS INC, 20200101)

ItemWind Versus Storage Allocation for Price Management in Wholesale Electricity MarketsMasoumzadeh, A ; Nekouei, E ; Alpcan, T (Institute of Electrical and Electronics Engineers, 20200401)This paper investigates the impacts of installing regulated wind and electricity storage on average price and price volatility in electricity markets. A stochastic bilevel optimization model is developed, which computes the optimal allocation of new wind and battery capacities, by minimizing a weighted sum of the average market price and price volatility. A fixed budget is allocated on wind and battery capacities in the upperlevel problem. The operation of strategic/regulated generation, storage, and transmission players is simulated in the lowerlevel problem using a stochastic (Bayesian) Cournotbased game model. Australia's national electricity market, which is experiencing occasional price peaks, is considered as the case study. Our simulation results quantitatively illustrate that the regulated wind is more efficient than storage in reducing the average price, while the regulated storage more effectively reduces the price volatility. According to our numerical results, the storageonly solution reduces the average price at most by 9.4%, and the windonly solution reduces the square root of price volatility at most by 39.3%. However, an optimal mixture of wind and storage can reduce the mean price by 17.6% and the square root of price volatility by 48.1%. It also increases the consumer surplus by 1.52%. Moreover, the optimal mixture of wind and storage is a profitable solution unlike the storageonly solution.

ItemAn Information Analysis of Iterative Algorithms for Network Utility Maximization and Strategic GamesAlpcan, T ; Nekouei, E ; Nair, GN ; Evans, RJ (IEEE, 2019)A variety of resource allocation problems on networked systems, for example, those in cyberphysical systems or Internetofthings applications, require distributed solution methods. Modern distributed algorithms usually require bandwidthlimited digital communication between the system and its users, who are often modeled as independent decision makers with individual preferences. This paper presents a quantitative information flow and knowledge gain analysis of decentralized iterative algorithms with bounded trajectories in the context of convex network utility maximization problems and strategic games with a unique Nash equilibrium solution. First, a novel generic framework is introduced to quantify knowledge gain in network resource allocation problems using entropy by taking into account priors in the solution space. Second, a general result is presented on the interplay between quantization of information and distributed algorithm performance both for linear and sublinear convergence. Third, information flow in distributed algorithms is studied and a lower bound is derived on the total amount of information exchanged for convergence under uniform quantization. The wellknown primaldual decomposition algorithm is used as an example to illustrate the results. Finally, convergence guarantees for distributed algorithms with estimation are investigated. This paper establishes specific links between information concepts and iterative algorithms in addition to building a foundation for integrating learning schemes into distributed optimization.

ItemAdversarial Behavior in Network GamesChorppath, AK ; Alpcan, T ; Boche, H (SPRINGER BIRKHAUSER, 20150301)This paper studies the effects of and countermeasures against adversarial behavior in network resource allocation mechanisms such as auctions and pricing schemes. It models the heterogeneous behavior of users, which ranges from altruistic to selfish and to malicious, within the analytical framework of game theory. A mechanism design approach is adopted to quantify the effect of adversarial behavior, which ranges from extreme selfishness to destructive maliciousness. First, the wellknown result on the Vicrey–Clarke–Groves (VCG) mechanism losing its efficiency property in the presence of malicious users is extended to the case of divisible resource allocation to motivate the need to quantify the effect of malicious behavior. Then, the Price of Malice of the VCG mechanism and of some other network mechanisms are derived. In this context, the dynamics and convergence properties of an iterative distributed pricing algorithm are analyzed. The resistance of a mechanism to collusions is investigated next, and the effect of collusion of some malicious users is quantified. Subsequently, the assumption that the malicious user has information about the utility function of selfish users is relaxed, and a regressionbased iterative learning scheme is presented and applied to both pricing and auction mechanisms. Differentiated pricing as a method to counter adversarial behaviors is proposed and briefly discussed. The results obtained are illustrated with numerical examples and simulations.

ItemPerformance Analysis of GradientBased Nash Seeking Algorithms Under QuantizationNekouei, E ; Nair, GN ; Alpcan, T (IEEEINST ELECTRICAL ELECTRONICS ENGINEERS INC, 20161201)This paper investigates the impact of quantized interagent communications on the asymptotic and transient behavior of gradientbased Nashseeking algorithms in noncooperative games. Using the informationtheoretic notion of entropy power, we establish a universal lower bound on the asymptotic rate of exponential meansquare convergence to the Nash equilibrium (NE). This bound depends on the interagent data rate and the local behavior of the agents' utility functions, and is independent of the quantizer structure. Next, we study transient performance and derive an upper bound on the average time required to settle inside a specified ball around the NE, under uniform quantization. Furthermore, we establish an upper bound on the probability that agents' actions lie outside this ball, and show that this bound decays doubleexponentially with time. Finally, we propose an adaptive quantization scheme that allows the gradient algorithm to converge to the NE despite quantized interagent communications.

ItemSample Complexity of Solving NonCooperative GamesNekouei, E ; Nair, GN ; Alpcan, T ; Evans, RJ (Institute of Electrical and Electronics Engineers, 20200201)This paper studies the complexity of solving two classes of noncooperative games in a distributed manner, in which the players communicate with a set of system nodes over noisy communication channels. The complexity of solving each game class is defined as the minimum number of iterations required to find a Nash equilibrium (NE) of any game in that class with ∈ accuracy. First, we consider the class G of all Nplayer noncooperative games with a continuous action space that admit at least one NE. Using informationtheoretic inequalities, a lower bound on the complexity of solving G is derived which depends on the Kolmogorov 2∈capacity of the constraint set and the total capacity of the communication channels. Our results indicate that the game class G can be solved at most exponentially fast. We next consider the class of all Nplayer noncooperative games with at least one NE such that the players' utility functions satisfy a certain (differential) constraint. We derive lower bounds on the complexity of solving this game class under both Gaussian and nonGaussian noise models. Finally, we derive upper and lower bounds on the sample complexity of a class of quadratic games. It is shown that the complexity of solving this game class scales according to Θ (1/∈ 2 ) where € is the accuracy parameter.