arXiv subjects

Kenza Hamidouche

Publications and source records attributed to Kenza Hamidouche.

15 recordsLinked to original sources

arXiv2021

Fast Design Space Exploration of Nonlinear Systems: Part II

Nonlinear system design is often a multi-objective optimization problem involving search for a design that satisfies a number of predefined constraints. The design space is typically very large since it includes all possible system architectures with different combinations of components composing each architecture. In this article, we address nonlinear system design space exploration through a two-step approach encapsulated in a framework called Fast Design Space Exploration of Nonlinear Systems (ASSENT). In the first step, we use a genetic algorithm to search for system architectures that allow discrete choices for component values or else only component values for a fixed architecture. This step yields a coarse design since the system may or may not meet the target specifications. In the second step, we use an inverse design to search over a continuous space and fine-tune the component values with the goal of improving the value of the objective function. We use a neural network to model the system response. The neural network is converted into a mixed-integer linear program for active learning to sample component values efficiently. We illustrate the efficacy of ASSENT on problems ranging from nonlinear system design to design of electrical circuits. Experimental results show that ASSENT achieves the same or better value of the objective function compared to various other optimization techniques for nonlinear system design by up to 54%. We improve sample efficiency by 6-10x compared to reinforcement learning based synthesis of electrical circuits.

eess.SY↗

arXiv2020

CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks

The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands, and also suffers from a relatively low positive detection rate in the early stages of the resultant COVID-19 disease. Hence, there is a need for an alternative approach for repeated large-scale testing of SARS-CoV-2/COVID-19. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus. We collected data from 87 individuals, spanning three cohorts including healthy, asymptomatic, and symptomatic patients. We trained DNNs on various subsets of the features automatically extracted from six WMS and questionnaire categories to perform ablation studies to determine which subsets are most efficacious in terms of test accuracy for a three-way classification. The highest test accuracy obtained was 98.1%. We also augmented the real training dataset with a synthetic training dataset drawn from the same probability distribution to impose a prior on DNN weights and leveraged a grow-and-prune synthesis paradigm to learn both DNN architecture and weights. This boosted the accuracy of the various DNNs further and simultaneously reduced their size and floating-point operations.

cs.HC↗

arXiv2020

DISPATCH: Design Space Exploration of Cyber-Physical Systems

Design of cyber-physical systems (CPSs) is a challenging task that involves searching over a large search space of various CPS configurations and possible values of components composing the system. Hence, there is a need for sample-efficient CPS design space exploration to select the system architecture and component values that meet the target system requirements. We address this challenge by formulating CPS design as a multi-objective optimization problem and propose DISPATCH, a two-step methodology for sample-efficient search over the design space. First, we use a genetic algorithm to search over discrete choices of system component values for architecture search and component selection or only component selection and terminate the algorithm even before meeting the system requirements, thus yielding a coarse design. In the second step, we use an inverse design to search over a continuous space to fine-tune the component values and meet the diverse set of system requirements. We use a neural network as a surrogate function for the inverse design of the system. The neural network, converted into a mixed-integer linear program, is used for active learning to sample component values efficiently in a continuous search space. We illustrate the efficacy of DISPATCH on electrical circuit benchmarks: two-stage and three-stage transimpedence amplifiers. Simulation results show that the proposed methodology improves sample efficiency by 5-14x compared to a prior synthesis method that relies on reinforcement learning. It also synthesizes circuits with the best performance (highest bandwidth/lowest area) compared to designs synthesized using reinforcement learning, Bayesian optimization, or humans.

cs.LG↗

arXiv2020

Policy Optimization for Linear-Quadratic Zero-Sum Mean-Field Type Games

In this paper, zero-sum mean-field type games (ZSMFTG) with linear dynamics and quadratic utility are studied under infinite-horizon discounted utility function. ZSMFTG are a class of games in which two decision makers whose utilities sum to zero, compete to influence a large population of agents. In particular, the case in which the transition and utility functions depend on the state, the action of the controllers, and the mean of the state and the actions, is investigated. The game is analyzed and explicit expressions for the Nash equilibrium strategies are derived. Moreover, two policy optimization methods that rely on policy gradient are proposed for both model-based and sample-based frameworks. In the first case, the gradients are computed exactly using the model whereas they are estimated using Monte-Carlo simulations in the second case. Numerical experiments show the convergence of the two players' controls as well as the utility function when the two algorithms are used in different scenarios.

math.OC↗

arXiv2020

Linear-Quadratic Zero-Sum Mean-Field Type Games: Optimality Conditions and Policy Optimization

In this paper, zero-sum mean-field type games (ZSMFTG) with linear dynamics and quadratic cost are studied under infinite-horizon discounted utility function. ZSMFTG are a class of games in which two decision makers whose utilities sum to zero, compete to influence a large population of indistinguishable agents. In particular, the case in which the transition and utility functions depend on the state, the action of the controllers, and the mean of the state and the actions, is investigated. The optimality conditions of the game are analysed for both open-loop and closed-loop controls, and explicit expressions for the Nash equilibrium strategies are derived. Moreover, two policy optimization methods that rely on policy gradient are proposed for both model-based and sample-based frameworks. In the model-based case, the gradients are computed exactly using the model, whereas they are estimated using Monte-Carlo simulations in the sample-based case. Numerical experiments are conducted to show the convergence of the utility function as well as the two players' controls.

math.OC↗

arXiv2019

Learning-based Resource Optimization in Ultra Reliable Low Latency HetNets

In this paper, the problems of user offloading and resource optimization are jointly addressed to support ultra-reliable and low latency communications (URLLC) in HetNets. In particular, a multi-tier network with a single macro base station (MBS) and multiple overlaid small cell base stations (SBSs) is considered that includes users with different latency and reliability constraints. Modeling the latency and reliability constraints of users with probabilistic guarantees, the joint problem of user offloading and resource allocation (JUR) in a URLLC setting is formulated as an optimization problem to minimize the cost of serving users for the MBS. In the considered scheme, SBSs bid to serve URLLC users under their coverage at a given price, and the MBS decides whether to serve each user locally or to offload it to one of the overlaid SBSs. Since the JUR optimization is NP-hard, we propose a low complexity learning-based heuristic method (LHM) which includes a support vector machine-based user association model and a convex resource optimization (CRO) algorithm. To further reduce the delay, we propose an alternating direction method of multipliers (ADMM)-based solution to the CRO problem. Simulation results show that using LHM, the MBS significantly decreases the spectrum access delay for users (by $\sim$ 93\%) as compared to JUR, while also reducing its bandwidth and power costs in serving users (by $\sim$ 33\%) as compared to directly serving users without offloading.

cs.NI↗

arXiv2018

How Secure are Multicarrier Communication Systems Against Signal Exploitation Attacks?

In this paper, robustness of non-contiguous orthogonal frequency division multiplexing (NC-OFDM) transmissions is investigated and contrasted to OFDM transmissions for fending off signal exploitation attacks. In contrast to ODFM transmissions, NC-OFDM transmissions take place over a subset of active subcarriers to either avoid incumbent transmissions or for strategic considerations. A point-to-point communication system is considered in this paper in the presence of an adversary (exploiter) that aims to infer transmission parameters (e.g., the subset of active subcarriers and duration of the signal) using a deep neural network (DNN). This method has been proposed since the existing methods for exploitation, which are based on cyclostationary analysis, have been shown to have limited success in NC-OFDM systems. A good estimation of the transmission parameters allows the adversary to transmit spurious data and attack the legitimate receiver. Simulation results show that the DNN can infer the transmit parameters of OFDM signals with very good accuracy. However, NC-OFDM with fully random selection of active subcarriers makes it difficult for the adversary to exploit the waveform and thus for the receiver to be affected by the spurious data. Moreover, the more structured the set of active subcarriers selected by the transmitter is, the easier it is for the adversary to infer the transmission parameters and attack the receiver using a DNN.

eess.SP↗

arXiv2018

Collaborative Artificial Intelligence (AI) for User-Cell association in Ultra-Dense Cellular Systems

In this paper, the problem of cell association between small base stations (SBSs) and users in dense wireless networks is studied using artificial intelligence (AI) techniques. The problem is formulated as a mean-field game in which the users' goal is to maximize their data rate by exploiting local data and the data available at neighboring users via an imitation process. Such a collaborative learning process prevents the users from exchanging their data directly via the cellular network's limited backhaul links and, thus, allows them to improve their cell association policy collaboratively with minimum computing. To solve this problem, a neural Q-learning learning algorithm is proposed that enables the users to predict their reward function using a neural network whose input is the SBSs selected by neighboring users and the local data of the considered user. Simulation results show that the proposed imitation-based mechanism for cell association converges faster to the optimal solution, compared with conventional cell association mechanisms without imitation.

cs.IT↗

arXiv2018

Contract-based Incentive Mechanism for LTE over Unlicensed Channels

In this paper, a novel economic approach, based on the framework of contract theory, is proposed for providing incentives for LTE over unlicensed channels (LTE-U) in cellular-networks. In this model, a mobile network operator (MNO) designs and offers a set of contracts to the users to motivate them to accept being served over the unlicensed bands. A practical model in which the information about the quality-of-service (QoS) required by every user is not known to the MNO and other users, is considered. For this contractual model, the closed-form expression of the price charged by the MNO for every user is derived and the problem of spectrum allocation is formulated as a matching game with incomplete information. For the matching problem, a distributed algorithm is proposed to assign the users to the licensed and unlicensed spectra. Simulation results show that the proposed pricing mechanism can increase the fraction of users that achieve their QoS requirements by up to 45\% compared to classical algorithms that do not account for users requirements. Moreover, the performance of the proposed algorithm in the case of incomplete information is shown to approach the performance of the same mechanism with complete information.

cs.IT↗

arXiv2017

The 5G Cellular Backhaul Management Dilemma: To Cache or to Serve

With the introduction of caching capabilities into small cell networks (SCNs), new backaul management mechanisms need to be developed to prevent the predicted files that are downloaded by the at the small base stations (SBSs) to be cached from jeopardizing the urgent requests that need to be served via the backhaul. Moreover, these mechanisms must account for the heterogeneity of the backhaul that will be encompassing both wireless backhaul links at various frequency bands and a wired backhaul component. In this paper, the heterogeneous backhaul management problem is formulated as a minority game in which each SBS has to define the number of predicted files to download, without affecting the required transmission rate of the current requests. For the formulated game, it is shown that a unique fair proper mixed Nash equilibrium (PMNE) exists. Self-organizing reinforcement learning algorithm is proposed and proved to converge to a unique Boltzmann-Gibbs equilibrium which approximates the desired PMNE. Simulation results show that the performance of the proposed approach can be close to that of the ideal optimal algorithm while it outperforms a centralized greedy approach in terms of the amount of data that is cached without jeopardizing the quality-of-service of current requests.

cs.IT↗

arXiv2016

A Stackelberg Game for Incentive Proactive Caching Mechanisms in Wireless Networks

In this paper, an incentive proactive cache mechanism in cache-enabled small cell networks (SCNs) is proposed, in order to motivate the content providers (CPs) to participate in the caching procedure. A network composed of a single mobile network operator (MNO) and multiple CPs is considered. The MNO aims to define the price it charges the CPs to maximize its revenue while the CPs compete to determine the number of files they cache at the MNO's small base stations (SBSs) to improve the quality of service (QoS) of their users. This problem is formulated as a Stackelberg game where a single MNO is considered as the leader and the multiple CPs willing to cache files are the followers. The followers game is modeled as a non-cooperative game and both the existence and uniqueness of a Nash equilibrium (NE) are proved. The closed-form expression of the NE which corresponds to the amount of storage each CP requests from the MNO is derived. An optimization problem is formulated at the MNO side to determine the optimal price that the MNO should charge the CPs. Simulation results show that at the equilibrium, the MNO and CPs can all achieve a utility that is up to 50% higher than the cases in which the prices and storage quantities are requested arbitrarily.

cs.IT↗

arXiv2016

Breaking the Economic Barrier of Caching in Cellular Networks: Incentives and Contracts

In this paper, a novel approach for providing incentives for caching in small cell networks (SCNs) is proposed based on the economics framework of contract theory. In this model, a mobile network operator (MNO) designs contracts that will be offered to a number of content providers (CPs) to motivate them to cache their content at the MNO's small base stations (SBSs). A practical model in which information about the traffic generated by the CPs' users is not known to the MNO is considered. Under such asymmetric information, the incentive contract between the MNO and each CP is properly designed so as to determine the amount of allocated storage to the CP and the charged price by the MNO. The contracts are derived by the MNO in a way to maximize the global benefit of the CPs and prevent them from using their private information to manipulate the outcome of the caching process. For this interdependent contract model, the closed-form expressions of the price and the allocated storage space to each CP are derived. This proposed mechanism is shown to satisfy the sufficient and necessary conditions for the feasibility of a contract. Moreover, it is shown that the proposed pricing model is budget balanced, enabling the MNO to cover all the caching expenses via the prices charged to the CPs. Simulation results show that none of the CPs will have an incentive to choose a contract designed for CPs with different traffic loads.

cs.IT↗

arXiv2016

A Multi-Game Framework for Harmonized LTE-U and WiFi Coexistence over Unlicensed Bands

The introduction of LTE over unlicensed bands (LTE-U) will enable LTE base stations (BSs) to boost their capacity and offload their traffic by exploiting the underused unlicensed bands. However, to reap the benefits of LTE-U, it is necessary to address various new challenges associated with LTE-U and WiFi coexistence. In particular, new resource management techniques must be developed to optimize the usage of the network resources while handling the interdependence between WiFi and LTE users and ensuring that WiFi users are not jeopardized. To this end, in this paper, a new game theoretic tool, dubbed as \emph{multi-game} framework is proposed as a promising approach for modeling resource allocation problems in LTE-U. In such a framework, multiple, co-existing and coupled games across heterogeneous channels can be formulated to capture the specific characteristics of LTE-U. Such games can be of different properties and types but their outcomes are largely interdependent. After introducing the basics of the multi-game framework, two classes of algorithms are outlined to achieve the new solution concepts of multi-games. Simulation results are then conducted to show how such a multi-game can effectively capture the specific properties of LTE-U and make of it a "friendly" neighbor to WiFi.

cs.IT↗

arXiv2016

Mean-Field Games for Distributed Caching in Ultra-Dense Small Cell Networks

In this paper, the problem of distributed caching in dense wireless small cell networks (SCNs) is studied using mean field games (MFGs). In the considered SCN, small base stations (SBSs) are equipped with data storage units and cooperate to serve users' requests either from files cached in the storage or directly from the capacity-limited backhaul. The aim of the SBSs is to define a caching policy that reduces the load on the capacity-limited backhaul links. This cache control problem is formulated as a stochastic differential game (SDG). In this game, each SBS takes into consideration the storage state of the other SBSs to decide on the fraction of content it should cache. To solve this problem, the formulated SDG is reduced to an MFG by considering an ultra-dense network of SBSs in which the existence and uniqueness of the mean-field equilibrium is shown to be guaranteed. Simulation results show that this framework allows an efficient use of the available storage space at the SBSs while properly tracking the files' popularity. The results also show that, compared to a baseline model in which SBSs are not aware of the instantaneous system state, the proposed framework increases the number of served files from the SBSs by more than 69%.

cs.IT↗

arXiv2014

Many-to-Many Matching Games for Proactive Social-Caching in Wireless Small Cell Networks

In this paper, we address the caching problem in small cell networks from a game theoretic point of view. In particular, we formulate the caching problem as a many-to-many matching game between small base stations and service providers' servers. The servers store a set of videos and aim to cache these videos at the small base stations in order to reduce the experienced delay by the end-users. On the other hand, small base stations cache the videos according to their local popularity, so as to reduce the load on the backhaul links. We propose a new matching algorithm for the many-to-many problem and prove that it reaches a pairwise stable outcome. Simulation results show that the number of satisfied requests by the small base stations in the proposed caching algorithm can reach up to three times the satisfaction of a random caching policy. Moreover, the expected download time of all the videos can be reduced significantly.

cs.NI↗