Department of Electrical Engineering and Computer Science

Permanent URI for this collectionhttps://hdl.handle.net/10315/30511

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Now showing 1 - 18 of 18

Access status: Open Access ,
Over-the-Air FEEL with Integrated Sensing: Joint Scheduling and Beamforming Design
(Institute of Electrical and Electronics Engineers, 2025-01-22) Asaad, Saba; Wang, Ping; Tabassum, Hina
Employing wireless systems with dual sensing and communications functionalities is becoming critical in next generation of wireless networks. In this paper, we propose a robust design for over-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We first derive novel expressions for the Cramér-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure radar sensing and model aggregation quality, respectively. Then, we develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while keeping the sensing and communication quality, determined respectively in terms of Cramér-Rao bound and achievable downlink rate, in a desired range. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm used widely for sparse recovery in the literature of compressed sensing. The proposed algorithm uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality of the system. It further invokes alternating optimization scheme to iteratively update the downlink beamforming and uplink post-processing by marginally optimizing them in each iteration. Convergence and complexity analysis of the proposed algorithm is presented. Numerical evaluations on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our proposed algorithm. The results show that by leveraging accurate sensing, the target echoes on the uplink signal can be effectively suppressed, ensuring the quality of model aggregation to remain intact despite the interference.
Access status: Open Access ,
Generalized Multi-Objective Reinforcement Learning With Envelope Updates in URLLC-Enabled Vehicular Networks
(Institute of Electrical and Electronics Engineers, 2025-06-17) Yan, Zijiang; Tabassum, Hina
We develop a novel multi-objective reinforcement learning (MORL) framework to jointly optimize wireless network selection and autonomous driving policies in a multi-band vehicular network operating on conventional sub-6 GHz spectrum and Terahertz frequencies. The proposed framework is designed to (i) maximize the traffic flow and minimize collisions by controlling the vehicle's motion dynamics (i.e., speed and acceleration), and (ii) enhance the ultra-reliable low-latency communication (URLLC) while minimizing handoffs (HOs). We cast this problem as a multi-objective Markov Decision Process (MOMDP) and develop solutions for both predefined and unknown preferences of the conflicting objectives. Specifically, we develop a novel envelope MORL solution which develops policies that address multiple objectives with unknown preferences to the agent. While this approach reduces reliance on scalar rewards, policy effectiveness varying with different preferences is a challenge. To address this, we apply a generalized version of the Bellman equation and optimize the convex envelope of multi-objective Q values to learn a unified parametric representation capable of generating optimal policies across all possible preference configurations. Following an initial learning phase, our agent can execute optimal policies under any specified preference or infer preferences from minimal data samples. Numerical results validate the efficacy of the envelope-based MORL solution and demonstrate interesting insights related to the inter-dependency of vehicle motion dynamics, HOs, and the communication data rate. The proposed policies enable autonomous vehicles (AVs) to adopt safe driving behaviors with improved connectivity.
Access status: Open Access ,
Resource Allocation in Cooperative Mid-Band/THz Networks in the Presence of Mobility
(Institute of Electrical and Electronics Engineers, 2025-10-08) Saeidi, Mohammad Amin; Tabassum, Hina
This paper develops a comprehensive framework to investigate and optimize the downlink performance of cooperative multi-band networks (MBNs) operating on upper mid-band (UMB) and terahertz (THz) frequencies, where base stations (BSs) in each band cooperatively serve users. The framework captures sophisticated features such as near-field channel modeling, fully and partially connected antenna architectures, and users’ mobility. First, we consider joint user association and hybrid beamforming optimization to maximize the system sum-rate, subject to power constraints, maximum cluster size of cooperating BSs, and users’ quality-of-service (QoS) constraints. By leveraging fractional programming FP and majorization-minimization techniques, an iterative algorithm is proposed to solve the non-convex optimization problem. We then consider handover (HO)-aware resource allocation for moving users in a cooperative UMB/THz MBN. Two HO-aware resource allocation methods are proposed. The first method focuses on maximizing the HO-aware system sum-rate subject to HO-aware QoS constraints. Using Jensen’s inequality and properties of logarithmic functions, the non-convex optimization problem is tightly approximated with a convex one and solved. The second method addresses a multi-objective optimization problem to maximize the system sum-rate, while minimizing the total number of HOs. Numerical results demonstrate the efficacy of the proposed algorithms, cooperative UMB/THz MBN over stand-alone THz networks, as well as the critical importance of accurate near-field modeling in extremely large antenna arrays. Moreover, the proposed HO-aware resource allocation methods effectively mitigate the impact of HOs, enhancing performance in the considered system.
Access status: Open Access ,
EMForecaster: A Deep Learning Framework for Time Series Forecasting in Wireless Networks With Distribution-Free Uncertainty Quantification
(Institute of Electrical and Electronics Engineers, 2025-07-24) Mootoo, Xavier Stephen; Tabassum, Hina; Chiaraviglio, Luca
With the recent advancements in wireless technologies, forecasting electromagnetic field (EMF) exposure has become increasingly critical to enable proactive network spectrum and power allocation, as well as network deployment planning. In this paper, we develop a deep learning (DL)-empowered time series forecasting framework referred to as EMForecaster. The proposed DL architecture employs patching to process temporal patterns at multiple scales, complemented by reversible instance normalization and mixing operations along both temporal and patch dimensions for efficient feature extraction. We then augment EMForecaster with a conformal prediction mechanism, which is independent of the data distribution, to enhance the trustworthiness of model predictions through uncertainty quantification of forecasts. In particular, the conformal prediction mechanism ensures that the ground truth lies within a prediction interval with target error rate α, where 1−α is referred to as coverage. However, a trade-off exists, as increasing coverage often results in wider prediction intervals. To address this challenge, we propose a new metric referred to as Trade-off Score, that balances the trustworthiness of the forecast (i.e., coverage) and the width of prediction interval. Our empirical evaluation demonstrates that EMForecaster achieves superior performance across diverse EMF datasets, spanning both short-term and long-term prediction horizons. In point forecasting tasks, EMForecaster substantially outperforms current state-of-the-art DL approaches, showing improvements of 53.97% over the Transformer architecture and 38.44% over the average of all baseline models. In terms of conformal prediction performance, EMForecaster exhibits excellent balance between prediction interval width and coverage, as measured by the coverage-width tradeoff score. This balance is comparable to DLinear's performance while showing marked improvements of 24.73% over the average baseline and 49.17% over the Transformer architecture.
Access status: Open Access ,
Method and apparatus for distributing traffic load between different communication cells
(2023-11-21) Wu, Di; Kang, Jikun; Xu, Yi Tian; Li, Jimmy; Jenkin, Michael R; Liu, Xue; Chen, Xi; Dudek, Gregory Lewis; Park, Intaik; Lee, Taesop
An apparatus distributing communication load over a plurality of communication cells may select action centers from random cell reselection values, based on a standard deviation of an internet protocol (IP) throughout over the plurality of communication cells; input a first vector indicating a communication state of a communication system and a second vector indicating the standard deviation of the IP throughout of the plurality of communication cells, to a neural network to output a sum of the action centers and offsets as cell reselection parameters; and transmit the cell reselection parameters to the communication system to enable a base station of the communication system to perform a cell reselection based on the cell reselection parameters.
Access status: Open Access ,
System and method for rendering of an animated avatar
(2019-11-09) Jenkin, Michael R; Tarawneh, Enas
There are provided systems and methods for rendering of an animated avatar. An embodiment of the method includes: determining a first rendering time of a first clip as approximately equivalent to a predetermined acceptable rendering latency, a first playing time of the first clip determined as approximately the first rendering time multiplied by a multiplicative factor; rendering the first clip; determining a subsequent rendering time for each of one or more subsequent clips, each subsequent rendering time is determined to be approximately equivalent to the predetermined acceptable rendering latency plus the total playing time of the preceding clips, each subsequent playing time is determined to be approximately the rendering time of the respective subsequent clip multiplied by the multiplicative factor; and rendering the one or more subsequent clips.
Access status: Open Access ,
Exploiting Reward Machines with Deep Reinforcement Learning in Continuous Action Domains
(Springer Cham, 2023-09-07) Haolin Sun; Lesperance, Yves
In this paper, we address the challenges of non-Markovian rewards and learning efficiency in deep reinforcement learning (DRL) in continuous action domains by exploiting reward machines (RMs) and counterfactual experiences for reward machines (CRM). RM and CRM were proposed by Toro Icarte et al. A reward machine can decompose a task, convey its high-level structure to an agent, and support certain non-Markovian task specifications. In this paper, we integrate state-of-the-art DRL algorithms with RMs to enhance learning efficiency. Our experimental results demonstrate that Soft Actor-Critic with counterfactual experiences for RMs (SAC-CRM) facilitates faster learning of better policies, while Deep Deterministic Policy Gradient with counterfactual experiences for RMs (DDPG-CRM) is slower, achieves lower rewards, but is more stable. Option-based Hierarchical Reinforcement Learning for reward machines (HRM) and Twin Delayed Deep Deterministic (TD3) with CRM generally underperform compared to SAC-CRM and DDPG-CRM. This work contributes to the ongoing development of more efficient and robust DRL approaches by leveraging the potential of RMs in practical problem-solving scenarios.
Access status: Open Access ,
Large-scale, touch-sensitive video display
(2000-09-12) Jenkin, Michael R; Tsotsos, John K
A video surface is constructed by adjoining a large number of flat screen display devices together. Each screen on this surface is controlled by its own computer processor and these processors are networked together. Superimposed over this surface is a tiling of transparent touch-sensitive screens which allow for user input. The resulting display is thin, has a very high resolution, appears to be a single large screen to the user, and is capable of supporting many different types of human-machine interaction.
Access status: Open Access ,
Decentralized Topology Reconfiguration in Multiphase Distribution Networks
(IEEE Transactions on Signal and Information Processing over Networks, 2019-02) Srikantha, P.; Liu, J.
The cyber-physical nature of the modern power grid allows active power entities to exchange information signals with one another to make intelligent local actuation decisions. Exacting effective coordination amongst these cyber-enabled entities by way of strategic signal exchanges is essential for accommodating highly fluctuating power components (e.g., renewables, electric vehicles, etc.) that are becoming prevalent in today's electric grid. As such, in this paper, we present a novel decentralized topology reconfiguration algorithm for the distribution network (DN) that allows the system to adapt in real time to unexpected perturbations and/or congestions to restore balance in loads across the feeder and improve the DN voltage profile. For this, individual agents residing in DN buses iteratively exchange signals with neighbouring nodes to infer the current state (e.g., power balance and voltage) of the system and utilize this information to make local line switching decisions. Strong convergence properties and optimality conditions of the proposed algorithm are established via theoretical studies evoking potential games and discrete concavity. Comparative simulation studies conducted on realistic DNs showcase the practical properties of the proposed algorithm.
Access status: Open Access ,
Hierarchical Signal Processing for Tractable Power Flow Management in Electric Grid Networks
(IEEE Transactions on Signal and Information Processing over Networks, 2018-07) srikantha, P.; Kundur, D.
Rapid advancements in smart grid technologies have brought about the proliferation of intelligent and actuating power system components such as distributed generation, storage, and smart appliance units. Capitalizing fully on the potential benefits of these systems for sustainable and economical power generation, management, and delivery is currently a significant challenge due to issues of scalability, intermittency, and heterogeneity of the associated networks. In particular, vertically integrated and centralized power system management is no longer tractable for optimally coordinating these diverse devices at large scale while also accounting for the underlying complex physical grid constraints. To address these challenges, we propose a hierarchical signal processing framework for optimal power flow management whereby the cyber-physical network relationships of the modern grid are leveraged to enable intelligent decision-making by individual devices based on local constraints and external information. Decentralized and distributed techniques based on convex optimization and game theoretic constructs are employed for information exchanges and decision-making at each tier of the proposed framework. It is shown via theoretical and simulation studies that our technique allows for the seamless integration of power components into the grid with low computational and communication overhead while maintaining optimal, sustainable, and feasible grid operations.
Access status: Open Access ,
Stealthy Black-box Attacks on Deep Learning Non-intrusive Load Monitoring Models
(IEEE Transactions on Smart Grid, 2021-03) Srikantha, P.; Wang, J.
With the advent of the advanced metering infrastructure, electricity usage data is being continuously generated at large volumes by smart meters vastly deployed across the modern power grid. Electric power utility companies and third party entities such as smart home management solution providers gain significant insights into these datasets via machine learning (ML) models. These are then utilized to perform active/passive power demand management that fosters economical and sustainable electricity usage. Although ML models are powerful, these remain vulnerable to adversarial attacks. A novel stealthy black-box attack construction model is proposed that targets deep learning models utilized to perform non-intrusive load monitoring based on smart meter data. These attacks are practical as there is no assumption of the knowledge of training data, internal parameters, and architecture of the targeted ML model. The profound impact of the proposed stealthy attack constructions on energy analytics and decision-making processes is shown through comprehensive theoretical, practical, and comparative analysis. This work sheds light on vulnerabilities of ML models in the smart grid context and provides valuable insights for securely accommodating increasing prevalence of artificial intelligence in the modern power grid.
Access status: Open Access ,
A Data-Driven Approach for Generating Synthetic Load Patterns and Usage Habits
(IEEE Transactions on Smart Grid, 2020-07) Pirathayini, Srikantha; S.E. Kababji
Today's electricity grid is rapidly evolving to become highly connected and automated. These advancements have been mainly attributed to the ubiquitous communication/computational capabilities in the grid and the Internet of Things paradigm that is steadily permeating modern society. Another trend is the recent resurgence of machine learning which is especially timely for smart grid applications. However, a major deterrent in effectively utilizing machine learning algorithms is the lack of labelled training data. We overcome this issue in the specific context of smart meter data by proposing a flexible framework for generating synthetic labelled load (e.g., appliance) patterns and usage habits via a non-intrusive novel data-driven approach. We leverage on recent developments in generative adversarial networks (GAN) and kernel density estimators (KDE) to eliminate model-based assumptions that otherwise result in biases. The ensuing synthetic datasets resemble real datasets and lend to rich and diverse training/testing platforms for developing effective machine learning algorithms pertaining to consumer-side energy applications. Theoretical and practical studies presented in this paper highlight the viability and superior performance of the proposed framework.
Access status: Open Access ,
Optimal Decentralized Microgrid Coordination via the Schur’s Complement and S-Procedure
(IEEE Transactions on Smart Grid, 2019-06) Srikantha, Pirathayini; M. Mallick
The evolving landscape of the electricity sector along with increasing environmental concerns necessitate modern power grids to be more efficient, sustainable, and adaptive. Microgrids are typically composed of distributed energy sources which have great potential for enabling energy independence, sustainability, and flexibility. However, practical difficulties that deter the widespread deployment of microgrids include the unpredictability of local generation sources (e.g., renewables) and the lack of inertia that is naturally present in systems containing bulk synchronous plants. In this paper, we propose a near real-time microgrid coordination algorithm that allows actuating components to adapt to changing system conditions. We account for the electrical dependencies and limits in microgrid systems by constructing voltage/current balance relations in the dq0 frame and applying strategic decompositions to invoke the Schur's complement and S-procedure that allow for zero duality gap. We highlight the convergence, feasibility, and scalability features of the proposed decentralized algorithm via theoretical and comparative practical simulation studies.
Access status: Open Access ,
Hidden Convexities in Decentralized Coordination for the Distribution Networks
(IEEE Transactions on Power Systems, 2020-05) Srikantha, Pirathayini; M. Mallick
The modern power grid is undergoing unprecedented levels of transformations due to the rising prevalence of diverse power entities, cyber-enablement of grid components and energy deregulations. In this paper, we focus on distribution networks (DNs) to enable the seamless plug-and-play coordination of actuating cyber-enabled power entities for cost-effective and feasible system operations. The proposed distributed algorithm empowers individual cyber-physical agents residing in active power nodes with the ability to iteratively compute local actuation setpoints by exchanging information with neighbouring entities. The main contribution of this work is the identification of hidden convexities in the original non-convex optimal power flow (OPF) formulation for the DN via strategic decomposition and strong duality principles. These eliminate the need for OPF relaxations/approximations. Strong convergence and feasibility results are presented via theoretical analysis and practical simulation studies conducted on realistic systems.
Access status: Open Access ,
A Novel Distributed and Stealthy Attack on Active Distribution Networks and a Mitigation Strategy
(IEEE Transactions on Industrial Informatics, 2019-05) Srikantha, Pirathayini; J. Liu; J. Samarabandu
Rapid advances in smart devices tremendously facilitate our day-to-day lives. However, these can be exploited remotely via existing cyber vulnerabilities to cause disruption at the physical infrastructure level. In this paper, we discover a novel distributed and stealthy attack that uses malicious actuation of a large number of small-scale loads residing within a distribution network (DN). This attack is capable of cumulatively violating the underlying operational system limits, leading to widespread and prolonged disruptions. A key element of this attack is the efficient use of attack resources, planned via Stackelberg games. To mitigate this type of an attack, we propose a countermeasure strategy which adaptively suppresses adverse effects of the attack when detected in a timely manner. The effectiveness of the proposed mitigation strategy is demonstrated via theoretical convergence studies, practical evaluations, and comparisons with the state-of-the-art strategies using realistic load flow and DN infrastructure models.
Access status: Open Access ,
Web Service Composition as a Planning Task: Experiments using Knowledge-Based Planning
(Jun-04) Martínez, Erick; Lespérance, Yves
Motivated by the problem of automated Web service composition (WSC), in this paper, we present some empirical evidence to validate the effectiveness of using knowledge-based planning techniques for solving WSC problems. In our experiments we utilize the PKS (Planning with Knowledge and Sensing) planning system which is derived from a generalization of STRIPS. In PKS, the agent’s (incomplete) knowledge is represented by a set of databases and actions are modelled as revisions to the agent’s knowledge state rather than the state of the world. We argue that, despite the intrinsic limited expressiveness of this approach, typical WSC problems can be specified and solved at the knowledge level. We show that this approach scales relatively well under changing conditions (e.g. user constraints). Finally, we discuss implementation issues and propose some architectural guidelines within the context of an agent-oriented framework for inter-operable, intelligent, multi-agent systems for WSC and provisioning.
Access status: Open Access ,
IG-JADE-PKSlib: An Agent-Based Framework for Advanced Web Service Composition and Provisioning
(Jul-04) Martínez, Erick; Lespérance, Yves
In this paper we describe an agent-based infrastructure and toolkit to develop inter-operable, intelligent, multiagent systems for Web service composition (WSC) and provisioning. Our toolkit is realized through an interface library (IG-JADE-PKSlib) that combines state of the art agent-based and planning technologies (i.e., the IndiGolog model-based agent programming language, the JADE agent platform, and the PKS planning system). We show that each of these tools has its strengths and weaknesses, but combined together, they provide a very powerful toolkit. We argue that this infrastructure is particularly well suited for developing next generation Web services (WS) applications.
Access status: Open Access ,
Software Verification Tools
(© P. H. Roosen-Runge, 1999, 2003, 2007, 2008) Roosen-Runge, Peter
This text explores the problem of verifying software in terms of a set of simple tools which can be used to symbolically evaluate and prove properties of pieces of programs, represented either abstractly in functional terms, or by actual text. The tools can be thought of as roughly analogous to spelling and style checkers in word-processing; they reduce the labor of finding errors and provide some semi-automated aids to making corrections.

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