Electrical and Electronic Engineering - Research Publications
http://hdl.handle.net/11343/362
2020-09-30T07:21:42ZVisible to long-wave infrared chip-scale spectrometers based on photodetectors with tailored responsivities and multispectral filters
http://hdl.handle.net/11343/242409
Cadusch, JJ; Meng, J; Craig, BJ; Shrestha, VR; Crozier, KB
2020-09-01
Visible to long-wave infrared chip-scale spectrometers based on photodetectors with tailored responsivities and multispectral filters
Chip-scale microspectrometers, operational across the visible to long-wave infrared spectral region will enable many remote sensing spectroscopy applications in a variety of fields including consumer electronics, process control in manufacturing, as well as environmental and agricultural monitoring. The low weight and small device footprint of such spectrometers could allow for integration into handheld, unattended vehicles or wearable-electronics based systems. This review will focus on recent developments in nanophotonic microspectrometer designs, which fall into two design categories: (i) planar filter-arrays used in conjunction with visible or IR detector arrays and (ii) microspectrometers using filter-free detector designs with tailored responsivities, where spectral filtering and photocurrent generation occur within the same nanostructure.
Journal Article
2020-09-01T00:00:00ZEnabling Remote Human-to-Machine Applications With AI-Enhanced Servers Over Access Networks
http://hdl.handle.net/11343/242244
Mondal, S; Ruan, L; Maier, M; Larrabeiti, D; Das, G; Wong, E
2020
Enabling Remote Human-to-Machine Applications With AI-Enhanced Servers Over Access Networks
The recent research trends for achieving ultra-reliable and low-latency communication networks are largely driven by smart manufacturing and industrial Internet-of-Things applications. Such applications are being realized through Tactile Internet that allows users to control remote things and involve the bidirectional transmission of video, audio, and haptic data. However, the end-to-end propagation latency presents a stubborn bottleneck, which can be alleviated by using various artificial intelligence-based application layer and network layer prediction algorithms, e.g., forecasting and preempting haptic feedback transmission. In this paper, we study the experimental data on traffic characteristics of control signals and haptic feedback samples obtained through virtual reality-based human-to-machine teleoperation. Moreover, we propose the installation of edge-intelligence servers between master and slave devices to implement the preemption of haptic feedback from control signals. Harnessing virtual reality-based teleoperation experiments, we further propose a two-stage artificial intelligence-based module for forecasting haptic feedback samples. The first-stage unit is a supervised binary classifier that detects if haptic sample forecasting is necessary and the second-stage unit is a reinforcement learning unit that ensures haptic feedback samples are forecasted accurately when different types of material are present. Furthermore, by evaluating analytical expressions, we show the feasibility of deploying remote human-to-machine teleoperation over fiber backhaul by using our proposed artificial intelligence-based module, even under heavy traffic intensity.
Journal Article
2020-01-01T00:00:00ZPolarization-diversity receiver using remotely delivered local oscillator without optical polarization control.
http://hdl.handle.net/11343/242054
Ji, H; Zhou, X; Sun, C; Shieh, W
2020-07-20
Polarization-diversity receiver using remotely delivered local oscillator without optical polarization control.
Silicon photonics coherent transceivers have integrated all the necessary optics except the lasers. The laser source has become a major obstacle to further reduce the cost, footprint, power consumption of the coherent transceivers for short-reach optical interconnects. One solution is to utilize remotely delivered local oscillator (LO) from the transmitter, which has the benefits of relaxing the requirements of wavelength stability and laser linewidth and simplifying the digital signal processing (DSP) of carrier/phase recovery. However, a sophisticated adaptive polarization controller (APC) driven by a control loop in the electrical domain with a complicated algorithm is required to dynamically track and compensate for the polarization wandering of the received LO. In this paper, we propose a hybrid single-polarization coherent receiver and Stokes vector receiver (SVR) for polarization-diversity coherent detection without a need of optical polarization control for the remotely delivered LO. With such a scheme, we successfully received a 400-Gb/s dual-polarization constellation-shaped 64-QAM signal over 80-km fibers.
Journal Article
2020-07-20T00:00:00ZLow-Resolution Quantization in Phase Modulated Systems: Optimum Detectors and Error Rate Analysis
http://hdl.handle.net/11343/241991
Gayan, S; Senanayake, R; Inaltekin, H; Evans, J
2020-07-20
Low-Resolution Quantization in Phase Modulated Systems: Optimum Detectors and Error Rate Analysis
This paper studies optimum detectors and error rate analysis for wireless systems with low-resolution quantizers in the presence of fading and noise. A universal lower bound on the average symbol error probability ( SEP ), correct for all M -ary modulation schemes, is obtained when the number of quantization bits is not enough to resolve M signal points. In the special case of M -ary phase shift keying ( M -PSK), the maximum likelihood detector is derived. Utilizing the structure of the derived detector, a general average SEP expression for M -PSK modulation with n -bit quantization is obtained when the wireless channel is subject to fading with a circularly-symmetric distribution. For the Nakagami- m fading, it is shown that a transceiver architecture with n -bit quantization is asymptotically optimum in terms of communication reliability if n≥log2M+1 . That is, the decay exponent for the average SEP is the same and equal to m with infinite-bit and n -bit quantizers for n≥log2M+1 . On the other hand, it is only equal to 12 and 0 for n=log2M and n<log2M , respectively. An extensive simulation study is performed to illustrate the accuracy of the derived results, energy efficiency gains obtained by means of low-resolution quantizers, performance comparison of phase modulated systems with independent in-phase and quadrature channel quantization and robustness of the derived results under channel estimation errors.
Journal Article
2020-07-20T00:00:00ZA Case Study of WiFi Sniffing Performance Evaluation
http://hdl.handle.net/11343/241946
Li, Y; Barthelemy, J; Sun, S; Perez, P; Moran, B
2020
A Case Study of WiFi Sniffing Performance Evaluation
Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal WiFi access points for connection. A probe request, sent automatically in the active scanning mode, consisting of the MAC address of the device expresses an advertisement of its presence. A real-time wireless sniffing system is able to sense WiFi packets and analyse wireless traffic. This provides an opportunity to obtain insights into the interaction between the humans carrying the mobile devices and the environment. Susceptibility to loss of the wireless data transmission is an important limitation on this idea, and this is complicated by the lack of a standard specification for real deployment of WiFi sniffers. In this paper, we present an experimental analysis of sniffing performance under different wireless environments using off-the-shelf products. Our objective is to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment. Our preliminary results show that four main factors affect the sniffing performance: the number of access points and their corresponding operating channels, the signal strength of the access point and the number of devices in the vicinity. In terms of a real field deployment, we propose assignment of one sniffing device to each specific sub-region based on the local access point signal strength and coverage area and fixing the monitoring channel belongs to the local strongest access point.
Journal Article
2020-01-01T00:00:00ZGranger Causality of Gaussian Signals from Quantized Measurements
http://hdl.handle.net/11343/241850
Ahmadi, S; Nair, GN; Weyer, E
2020-03-12
Granger Causality of Gaussian Signals from Quantized Measurements
An approach is proposed for inferring Granger causality between jointly stationary, Gaussian signals from quantized data. First, a necessary and sufficient rank criterion for the equality of two conditional Gaussian distributions is proved. Assuming a partial finite-order Markov property, sufficient conditions are then derived under which Granger causality between them can be reliably inferred from the second order moments of the quantized processes. This approach does not require the statistics of the underlying Gaussian signals to be estimated, or a system model to be identified.
Conference Paper
2020-03-12T00:00:00ZSpatial and sectoral benefit distribution in water-energy system design
http://hdl.handle.net/11343/241725
Gonzalez, JM; Tomlinson, JE; Harou, JJ; Martínez Ceseña, EA; Panteli, M; Bottacin-Busolin, A; Hurford, A; Olivares, MA; Siddiqui, A; Erfani, T; Strzepek, KM; Mancarella, P; Mutale, J; Obuobie, E; Seid, AH; Ya, AZ
2020-07-01
Spatial and sectoral benefit distribution in water-energy system design
The design of water and energy systems has traditionally been done independently or considering simplified interdependencies between the two systems. This potentially misses valuable synergies between them and does not consider in detail the distribution of benefits between different sectors or regions. This paper presents a framework to couple integrated water-power network simulators with multi-objective optimisation under uncertainty to explore the implications of explicitly including spatial topology and interdependencies in the design of multi-sector integrated systems. A synthetic case study that incorporates sectoral dependencies in resource allocation, operation of multi-purpose reservoirs and spatially distributed infrastructure selection in both systems is used. The importance of explicitly modelling the distribution of benefits across different sectors and regions is explored by comparing different spatially aggregated and disaggregated multi-objective optimisation formulations. The results show the disaggregated formulation identifies a diverse set of non-dominated portfolios that enables addressing the spatial and sectoral distribution of benefits, whilst the aggregated formulations arbitrarily induce unintended biases. The proposed disaggregated approach allows for detailed spatial design of interlinked water and energy systems considering their complex regional and sectoral trade-offs. The framework is intended to assist planners in real resource systems where diverse stakeholder groups are mindful of receiving their fair share of development benefits.
Journal Article
2020-07-01T00:00:00ZPV Controller Modification and its Impact on Assisting PV Penetration
http://hdl.handle.net/11343/241724
Mohanan, VAV; Evans, RJ; Mareels, I; Kolluri, RR
2020-06-12
PV Controller Modification and its Impact on Assisting PV Penetration
Large-scale penetration of grid-following inverters into the electricity network presents various technical challenges to grid reliability. It is well-known that the ability of a grid to maintain a stable frequency is inhibited by adding traditional grid-tied photovoltaic (PV) generators. In this work, a detailed model of a simplified grid is presented, and it is shown that the proportion of PV generation and instability are positively correlated. The main instability phenomenon is captured by a Hopf Bifurcation in the field dynamics of the synchronous generator. Such a Hopf bifurcation severely constricts the feasible operating domain of the grid and may hinder normal operation. Modifying traditional grid-tied PV controllers and its impact on grid stability is assessed through small-signal, bifurcation and transient numerical analysis. Traditional PV controllers that are modified to virtual synchronous machine (VSM) type controllers show improvement in system damping. Unlike traditional grid-tied inverters, VSM inverters participate in critical modes of the synchronous generator (SG) and augment the operational domain of the SG+VSM system significantly, more importantly, almost eliminating the need for renewable energy curtailment. A case-study approach is used to present some key results on improvements in damping ratio, feasibility domain and transient stability. Finally, a feasibility domain curve is introduced and discussed in an aim to generalize the overall stability of any such system.
Conference Paper
2020-06-12T00:00:00ZWind Versus Storage Allocation for Price Management in Wholesale Electricity Markets
http://hdl.handle.net/11343/241557
Masoumzadeh, A; Nekouei, E; Alpcan, T
2020-04-01
Wind Versus Storage Allocation for Price Management in Wholesale Electricity Markets
This paper investigates the impacts of installing regulated wind and electricity storage on average price and price volatility in electricity markets. A stochastic bi-level 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 upper-level problem. The operation of strategic/regulated generation, storage, and transmission players is simulated in the lower-level problem using a stochastic (Bayesian) Cournot-based 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 storage-only solution reduces the average price at most by 9.4%, and the wind-only 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 storage-only solution.
Journal Article
2020-04-01T00:00:00ZAn Information Analysis of Iterative Algorithms for Network Utility Maximization and Strategic Games
http://hdl.handle.net/11343/241556
Alpcan, T; Nekouei, E; Nair, GN; Evans, RJ
2019
An Information Analysis of Iterative Algorithms for Network Utility Maximization and Strategic Games
A variety of resource allocation problems on networked systems, for example, those in cyber-physical systems or Internet-of-things applications, require distributed solution methods. Modern distributed algorithms usually require bandwidth-limited 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 well-known primal-dual 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.
Journal Article
2019-01-01T00:00:00ZDetection of Anomalous Communications with SDRs and Unsupervised Adversarial Learning
http://hdl.handle.net/11343/241555
Weerasinghe, S; Erfani, SM; Alpcan, T; Leckie, C; Riddle, J
2019-02-08
Detection of Anomalous Communications with SDRs and Unsupervised Adversarial Learning
Software-defined radios (SDRs) with substantial cognitive (computing) and networking capabilities provide an opportunity for observing radio communications in an area and potentially identifying malicious rogue agents. Assuming a prevalence of encryption methods, a cognitive network of such SDRs can be used as a low-cost and flexible scanner/sensor array for distributed detection of anomalous communications by focusing on their statistical characteristics. Identifying rogue agents based on their wireless communications patterns is not a trivial task, especially when they deliberately try to mask their activities. We address this problem using a novel framework that utilizes adversarial learning, non-linear data transformations to minimize the rogue agent's attempts at masking their activities, and game theory to predict the behavior of rogue agents and take the necessary countermeasures.
Conference Paper
2019-02-08T00:00:00ZA game-theoretic analysis of the adversarial boyd-kuramoto model
http://hdl.handle.net/11343/241554
Demazy, A; Kalloniatis, A; Alpcan, T
2018-01-01
A game-theoretic analysis of the adversarial boyd-kuramoto model
The “Boyd” model, also known as the “OODA loop”, represents the cyclic decision processes of individuals and organisations in a variety of adversarial situations. Combined with the Kuramoto model, which provides a mathematical foundation for describing the behaviour of a set of coupled or networked oscillators, the Boyd-Kuramoto model captures strategic (cyclic) decision making in competitive environments. This paper presents a novel game-theoretic approach to the Boyd-Kuramoto dynamical model in complex and networked systems. A two-player, Red versus Blue, strategic (non-cooperative) game is defined to describe the competitive interactions and individual decision cycles of Red and Blue agent populations. We study the model analytically in the regime of near phase synchrony where linearisation approximations are possible. We find that we can solve for the Nash equilibrium of the game in closed form, and that it only depends on the parameters defining the fixed point of the dynamical system. A detailed numerical analysis of the finite version of the game investigates the behaviour of the underlying networked Kuramoto oscillators and yields a unique, dominant Nash equilibrium solution. The obtained Nash equilibrium is further studied analytically in a region where the underlying Boyd-Kuramoto dynamics are stable. The result suggests that only the fixed point of the dynamical system plays a role, consist with the analytical solution. Finally, the impact of other variations of the Boyd-Kuramoto parameters on the game outcomes are studied numerically, confirming the observations from fixed point approaches. It is observed that many parameters of the Kuramoto model affect the NE solution of the current game formulation much less than initially stipulated, arguably due to the time-scale separation between the underlying Kuramoto model and the static game formulation.
Conference Paper
2018-01-01T00:00:00ZImpact of quantized inter-agent communications on game-theoretic and distributed optimization algorithms
http://hdl.handle.net/11343/241553
Nekouei, E; Alpcan, T; Evans, RJ
2018-01-01
Impact of quantized inter-agent communications on game-theoretic and distributed optimization algorithms
Quantized inter-agent communications in game-theoretic and distributed optimization algorithms generate uncertainty that affects the asymptotic and transient behavior of such algorithms. This chapter uses the information-theoretic notion of differential entropy power to establish universal bounds on the maximum exponential convergence rates of primal-dual and gradient-based Nash seeking algorithms under quantized communications. These bounds depend on the inter-agent data rate and the local behavior of the agents’ objective functions, and are independent of the quantizer structure. The presented results provide trade-offs between the speed of exponential convergence, the agents’ objective functions, the communication bit rates, and the number of agents and constraints. For the proposed Nash seeking algorithm, the transient performance is studied and an upper bound on the average time required to settle inside a specified ball around the Nash equilibrium is derived under uniform quantization. Furthermore, an upper bound on the probability that the agents’ actions lie outside this ball is established. This bound decays double exponentially with time.
Chapter
2018-01-01T00:00:00ZInformation Constrained and Finite-Time Distributed Optimisation Algorithms
http://hdl.handle.net/11343/241552
Philip, BV; Alpcan, T; Jin, J; Palaniswami, M
2017-01-01
Information Constrained and Finite-Time Distributed Optimisation Algorithms
This paper studies the delay-accuracy trade-off for an unconstrained quadratic Network Utility Maximization (NUM) problem, which is solved by a distributed, consensus based, constant step-size, gradient-descent algorithm. Information theoretic tools such as entropy power inequality are used to analyse the convergence rate of the algorithm under quantised inter-agent communication. A finite-time distributed algorithm is proposed to solve the problem under synchronised message passing. For a system with N agents, the algorithm reaches any desired accuracy within 2N iterations, by adjusting the step-size, α. However, if N is quite large or if the agents are constrained by their memory or computational capacities, asymptotic convergence algorithms are preferred to arrive within a permissible neighbourhood of the optimal solution. The analytical tools and algorithms developed shed light to delay-accuracy trade-off required for many real-time IoT applications, e.g., smart traffic control and smart grid. As an illustrative example, we use our algorithm to implement an intersection management application, where distributed computation and communication capabilities of smart vehicles and road side units increase the efficiency of an intersection.
Conference Paper
2017-01-01T00:00:00ZLong-Term Stochastic Planning in Electricity Markets Under Carbon Cap Constraint: A Bayesian Game Approach
http://hdl.handle.net/11343/241551
Masoumzadeh, A; Nekouei, E; Alpcan, T
2016-01-01
Long-Term Stochastic Planning in Electricity Markets Under Carbon Cap Constraint: A Bayesian Game Approach
Carbon price in an electricity market provides incentives for carbon emission abatement and renewable generation technologies. Policies constraining or penalizing carbon emissions can significantly impact the capacity planning decisions of both fossil-fueled and renewable generators. Uncertainties due to intermittency of various renewable generators can also affect the carbon emission policies. This paper proposes a Cournot-based long-term capacity expansion model taking into account carbon cap constraint for a partly concentrated electricity market dealing with stochastic renewables using a Bayesian game. The stochastic game is formulated as a centralized convex optimization problem and solved to obtain a Bayes-Nash Equilibrium (Bayes-NE) point. The stochastic nature of a generic electricity market is illustrated with a set of scenarios for wind availability, in which three generation firms (coal, gas, and wind) decide on their generation and long-term capacity investment strategies. Carbon price is derived as the dual variable of the carbon cap constraint. Embedding the carbon cap constraint in the game indicates more investment on renewable generators and less on fossil-fueled power plants. However, the higher level of intermittency from renewable generation leads to a higher carbon price to meet the cap constraint. This paves the way towards storage technologies and diversification of distributed generation as means to encounter intermittency in renewable generation.
Conference Paper
2016-01-01T00:00:00ZInformation metrics for model selection in function estimation
http://hdl.handle.net/11343/241550
Alpcan, T
2014-01-01
Information metrics for model selection in function estimation
A model selection framework is presented for function estimation under limited information, where only a small set of (noisy) data points are available for inferring the nonconvex unknown function of interest. The framework introduces information-theoretic metrics which quantify model complexity and are used in a multi-objective formulation of the function estimation problem. The intricate relationship between information obtained through observations and model complexity is investigated. The framework is applied to the hyperparameter selection problem in Gaussian Process Regression. As a result of its generality, the framework introduced is applicable to a variety of settings and practical problems with information limitations such as channel estimation, black-box optimisation, and dual control.
Conference Paper
2014-01-01T00:00:00ZCan we measure the difficulty of an optimization problem?
http://hdl.handle.net/11343/241549
Alpcan, T; Everitt, T; Hutter, M
2014-01-01
Can we measure the difficulty of an optimization problem?
Can we measure the difficulty of an optimization problem? Although optimization plays a crucial role in modern science and technology, a formal framework that puts problems and solution algorithms into a broader context has not been established. This paper presents a conceptual approach which gives a positive answer to the question for a broad class of optimization problems. Adopting an information and computational perspective, the proposed framework builds upon Shannon and algorithmic information theories. As a starting point, a concrete model and definition of optimization problems is provided. Then, a formal definition of optimization difficulty is introduced which builds upon algorithmic information theory. Following an initial analysis, lower and upper bounds on optimization difficulty are established. One of the upper-bounds is closely related to Shannon information theory and black-box optimization. Finally, various computational issues and future research directions are discussed.
Conference Paper
2014-01-01T00:00:00ZConvergence Analysis of Quantized Primal-dual Algorithm in Quadratic Network Utility Maximization Problems
http://hdl.handle.net/11343/241548
Nekouei, E; NAIR, G; Alpcan, T
2015
Convergence Analysis of Quantized Primal-dual Algorithm in Quadratic Network Utility Maximization Problems
This paper examines the effect of quantized communications on the convergence behavior of the primal-dual algorithm in quadratic network utility maximization problems with linear equality constraints. In our set-up, it is assumed that the primal variables are updated by individual agents, whereas the dual variables are updated by a central entity, called system, which has access to the parameters quantifying the system-wide constraints. The notion of differential entropy power is used to establish a universal lower bound on the rate of exponential mean square convergence of the primal-dual algorithm under quantized message passing between agents and the system. The lower bound is controlled by the average aggregate data rate under the quantization, the curvature of the utility functions of agents, the number of agents and the number of constraints. An adaptive quantization scheme is proposed under which the primal-dual algorithm converges to the optimal solution despite quantized communications between agents and the system. Finally, the rate of exponential convergence of the primal-dual algorithm under the proposed quantization scheme is numerically studied.
Conference Paper
2015-01-01T00:00:00ZLower Bounds on the Best-Case Complexity of Solving a Class of Non-cooperative Games
http://hdl.handle.net/11343/241547
Nekouei, E; Alpcan, T; Nair, GN; Evans, RJ
2016
Lower Bounds on the Best-Case Complexity of Solving a Class of Non-cooperative Games
This paper studies the complexity of solving the class G of all N-player non-cooperative games with continuous action spaces that admit at least one Nash equilibrium (NE). We consider a distributed Nash seeking setting where agents communicate with a set of system nodes (SNs), over noisy communication channels, to obtain the required information for updating their actions. The complexity of solving games in the class G is defined as the minimum number of iterations required to find a NE of any game in G with ε accuracy. Using information-theoretic inequalities, we derive a lower bound on the complexity of solving the game class G that depends on the Kolmogorov 2ε-capacity of the constraint set and the total capacity of the communication channels. We also derive a lower bound on the complexity of solving games in G which depends on the volume and surface area of the constraint set.
Conference Paper
2016-01-01T00:00:00ZAdversarial Behavior in Network Games
http://hdl.handle.net/11343/241546
Chorppath, AK; Alpcan, T; Boche, H
2015-03-01
Adversarial Behavior in Network Games
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 well-known 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 regression-based 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.
Journal Article
2015-03-01T00:00:00ZPerformance Analysis of Gradient-Based Nash Seeking Algorithms Under Quantization
http://hdl.handle.net/11343/241545
Nekouei, E; Nair, GN; Alpcan, T
2016-12-01
Performance Analysis of Gradient-Based Nash Seeking Algorithms Under Quantization
This paper investigates the impact of quantized inter-agent communications on the asymptotic and transient behavior of gradient-based Nash-seeking algorithms in non-cooperative games. Using the information-theoretic notion of entropy power, we establish a universal lower bound on the asymptotic rate of exponential mean-square convergence to the Nash equilibrium (NE). This bound depends on the inter-agent 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 double-exponentially with time. Finally, we propose an adaptive quantization scheme that allows the gradient algorithm to converge to the NE despite quantized inter-agent communications.
Journal Article
2016-12-01T00:00:00ZGigabit/s Optical Wireless Access and Indoor Networks
http://hdl.handle.net/11343/241530
Nirmalathas, TA; Song, T; Edirisinghe, S; Tian, L; Lim, C; Wong, E; Wang, K; Ranaweera, C; Alameh, K
2020
Gigabit/s Optical Wireless Access and Indoor Networks
Optical wireless networks are being explored as a wireless alternative for provision of multi gigabits/second wireless and this paper presents an overview of recent progress and outstanding challenges. and technologies.
Conference Paper
2020-01-01T00:00:00ZChemical Reactions-based Detection Mechanism for Molecular Communications
http://hdl.handle.net/11343/241503
Cao, TN; Jamali, V; Wicke, W; Yeoh, PL; Zlatanov, N; Evans, J; Schober, R
2020-05
Chemical Reactions-based Detection Mechanism for Molecular Communications
In molecular communications, the direct detection of signaling molecules may be challenging due to the lack of suitable sensors and interference from co-existing substances in the environment. Motivated by examples in nature, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. The underlying reaction-diffusion equations that describe the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed-form. To analyze these molecule concentrations, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables in each time step. We also derive insightful closed-form solutions for a special case. The accuracy of the proposed algorithm is verified by particle-based simulations. Our results show that the concentration of the product molecules has a similar characteristic over time as the concentration of the signaling molecules. We analyze the bit error rate (BER) for a threshold detector and highlight that significant improvements in the BER can be achieved by carefully choosing the molecular probe and optimizing the detection threshold.
Conference Paper
2020-05-01T00:00:00ZCentralized Scheduling with Sum-Rate optimization in Flexible Half-Duplex Networks
http://hdl.handle.net/11343/241462
Dayarathna, S; Razlighi, M; Senanayake, R; Zlatanov, N; Evans, J
2020-05
Centralized Scheduling with Sum-Rate optimization in Flexible Half-Duplex Networks
In this paper, we focus on maximization of the instantaneous sum-rate in flexible half-duplex networks, where nodes have the flexibility to choose to either transmit, receive or be silent in a given time slot. Since the corresponding optimization problem is NP-hard, we design low-cost algorithms that give sub-optimal solutions with good performance. We first consider two existing approximation techniques to simplify the sum-rate optimization problem: arithmetic-geometric means inequality and another utilising the tight lower bound approximation. We then propose a novel pattern search algorithm that performs close to exhaustive search but with significantly lower complexity. Comparing the performance of the proposed algorithm with respect to existing resource allocation techniques, we observe that our proposed algorithm provides significant sum-rate gains.
Conference Paper
2020-05-01T00:00:00ZBinary Power Optimality for Two Link Full-Duplex Network
http://hdl.handle.net/11343/241460
Dayarathna, S; Senanayake, R; Evans, J
2020-05
Binary Power Optimality for Two Link Full-Duplex Network
In this paper, we analyse the optimality of binary power allocation in a network that includes full-duplex communication links. Considering a network with four communicating nodes, two of them operating in half-duplex mode and the other two in full-duplex mode, we prove that binary power allocation is optimum for the full-duplex nodes when maximizing the sum rate. We also prove that, for half-duplex nodes binary power allocation is not optimum in general. However, for the two special cases, 1) the low signal-to-noise-plus-interference (SINR) regime and, 2) the approximation by the arithmetic mean-geometric mean inequality, binary power allocation is optimum for the approximated sum rate even for the half-duplex nodes. We further analyse a third special case using a symmetric network for which the optimum power allocation is binary, under a sufficient condition. Numerical examples are included to illustrate the accuracy of the results.
Conference Paper
2020-05-01T00:00:00ZOn the Exact Outage Probability of 2×2 MIMO-MRC in Correlated Rician Fading
http://hdl.handle.net/11343/241456
Dharmawansa, P; Kahatapitiya, K; Atapattu, S; Tellambura, C
2020-06-19
On the Exact Outage Probability of 2×2 MIMO-MRC in Correlated Rician Fading
This paper addresses a classical problem in random matrix theory-finding the distribution of the maximum eigen-value of the correlated Wishart unitary ensemble. In particular, we derive a new exact expression for the cumulative distribution function (c.d. f.) of the maximum eigen-value of a 2 × 2 correlated non-central Wishart matrix with rank-l mean. By using this new result, we derive the exact outage probability of 2 × 2 multiple-input multiple-output maximum-ratio-combining (MIMO-MRC) in Rician fading with transmit correlation and a strong line-of-sight (LoS) component (rank-l channel mean). We also show that the outage performance is affected by the relative alignment of the eigen-spaces of the mean and correlation matrices. In general, when the LoS path aligns with the least eigenvector of the correlation matrix, in the high transmit signal-to-noise ratio (SNR) regime, the outage gradually improves with the increasing correlation. Moreover, we show that as K (Rician factor) grows large, the outage event can be approximately characterized by the c.d.f. of a certain Gaussian random variable.
Conference Paper
2020-06-19T00:00:00ZTwo-Way Communications via Reconfigurable Intelligent Surface
http://hdl.handle.net/11343/241455
Atapattu, S; Fan, R; Dharmawansa, P; Wang, G; Evans, J
2020-06-19
Two-Way Communications via Reconfigurable Intelligent Surface
The novel reconfigurable intelligent surface (RIS) is an emerging technology which facilitates high spectrum and energy efficiencies in Beyond 5G and 6G wireless communication applications. Against this backdrop, this paper investigates two-way communications via reconfigurable intelligent surfaces (RISs) where two users communicate through a common RIS. We assume that uplink and downlink communication channels between two users and the RIS can be reciprocal. We first obtain the optimal phase adjustment at the RIS. We then derive the exact outage probability and the average throughput in closed-forms for single-element RIS. To evaluate multiple-element RIS, we first introduce a gamma approximation to model a product of Rayleigh random variables, and then derive approximations for the outage probability and the average throughput. For large average signal-to-interference-plus-noise ratio (SINR) \rho, asymptotic analXsis also shows that the outage decreases at the rate (\log(\rho)/\rho) where L is the number of elements, whereas the throughput increases with the rate \log(\rho).
Conference Paper
2020-06-19T00:00:00ZA Game-Theoretic Approach for Non-Cooperative Load Balancing Among Competing Cloudlets
http://hdl.handle.net/11343/241431
Mondal, S; Das, G; Wong, E
2020-02-26
A Game-Theoretic Approach for Non-Cooperative Load Balancing Among Competing Cloudlets
To deliver high performance and reliability to the mobile users in accessing mobile cloud services, the major interest is currently given to the integration of centralized cloud computing and distributed edge computing infrastructures. In such a heterogeneous network ecosystem, multiple cloudlets from different service providers coexist. However, to meet the stringent latency requirements of computation-intensive and mission-critical applications, overloaded cloudlets can offload some of the incoming job requests to their relatively under-loaded neighboring cloudlets. In this paper, we propose a novel economic and non-cooperative game-theoretic model for load balancing among competitive cloudlets. This model aims to maximize the utilities of all the competing cloudlets while meeting the end-to-end latency of the users. We characterize the problem as a generalized Nash equilibrium problem and investigate the existence and uniqueness of a pure-strategy Nash equilibrium. We design a variational inequality based algorithm to compute the pure-strategy Nash equilibrium. We show that all the competing cloudlets are able to maximize their utilities by employing our proposed Nash equilibrium computation offload strategy in both under- and overloaded conditions. We also show through numerical evaluations that our load balancing model outperforms some of the existing game-theoretic load balancing frameworks, especially in a highly overloaded condition.
Journal Article
2020-02-26T00:00:00ZData Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data
http://hdl.handle.net/11343/241334
Lay, US; Pradhan, B; Yusoff, ZBM; Bin Abdallah, AF; Aryal, J; Park, H-J
2019-08-20
Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data
Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.
Journal Article
2019-08-20T00:00:00ZIs backscatter link stronger than direct link in reconfigurable intelligent surface-assisted system?
http://hdl.handle.net/11343/241293
Zhao, W; Wang, G; Atapattu, S; Tsiftsis, TA; Tellambura, C
2020-06-01
Is backscatter link stronger than direct link in reconfigurable intelligent surface-assisted system?
This letter considers integrating a backscatter link with a reconfigurable intelligent surface to enhance backscatter communication while assisting the direct communication. We derive the probability that the backscatter channel dominates in the composite channel. This probability is a useful performance measure to determine the number of reflectors. Since the exact probability lacks a closed-form solution, we develop two approximations by modeling the gain of the backscatter link with a Gaussian or Gamma distribution. We found that these approximations match well with the exact value. Importantly, with a well-designed number of reflectors, the channel gain of the backscatter link may be always stronger than that of the direct one.
Journal Article
2020-06-01T00:00:00Z