arXiv subjects

Narayan Mandayam

Publications and source records attributed to Narayan Mandayam.

At least 19 recordsLinked to original sources

arXiv2026

Age and Stability Trade-offs in Remote Monitoring Systems

Timely information is important in a wide variety of Internet of Things (IoT) services in which a shared server must manage two competing tasks: (i) processing a queue of jobs, and (ii) generating status updates to a remote monitor. This creates a fundamental trade-off between queue stability and data freshness. In this work, we model this scheduling decision as a Markov Decision Process (MDP) with the objective of minimizing a weighted sum of the average Age of Information (AoI) and the average queue length. We show that the optimal scheduling strategy is a queue-dependent age threshold which is monotonic. The shape of the switching curve differs according to different priority regimes. Finally, we compare the optimal MDP policy against heuristic policies.

eess.SY↗

arXiv2025

Over-the-Air Transmission of Zak-OTFS on mmWave Communications Testbed

Millimeter-wave (mmWave) communication offers vast bandwidth for next-generation wireless systems but faces severe path loss, Doppler effects, and hardware impairments. Orthogonal Time Frequency Space (OTFS) modulation has emerged as a robust waveform for high-mobility and doubly dispersive channels, outperforming OFDM under strong Doppler. However, the most studied multicarrier OTFS (MC-OTFS) is not easily predictable because the input-output (I$/$O) relation is not given by (twisted) convolution. Recently, the Zak-transform based OTFS (Zak-OTFS or OTFS 2$.$0) was proposed, which provides a single domain delay Doppler (DD) processing framework with predictable I$/$O behavior. This paper presents one of the first over-the-air (OTA) demonstrations of Zak-OTFS at mmWave frequencies. We design a complete Zak-OTFS based mmWave OTA system featuring root-raised-cosine (RRC) filtering for enhanced DD-domain predictability, higher-order modulations up to 16-QAM, and a low-overhead preamble for synchronization. A comprehensive signal model incorporating carrier frequency offset (CFO) and timing impairments is developed, showing these effects can be jointly captured within the effective DD-domain channel. Experimental validation on the COSMOS testbed confirms the feasibility and robustness of Zak-OTFS under realistic mmWave conditions, highlighting its potential for efficient implementations in beyond-5G and 6G systems.

eess.SP↗

arXiv2024

Blotto on the Ballot: A Ballot Stuffing Blotto Game

We consider the following Colonel Blotto game between parties $P_1$ and $P_A.$ $P_1$ deploys a non negative number of troops across $J$ battlefields, while $P_A$ chooses $K,$ $K < J,$ battlefields to remove all of $P_1$'s troops from the chosen battlefields. $P_1$ has the objective of maximizing the number of surviving troops while $P_A$ wants to minimize it. Drawing an analogy with ballot stuffing by a party contesting an election and the countermeasures by the Election Commission to negate that, we call this the Ballot Stuffing Game. For this zero-sum resource allocation game, we obtain the set of Nash equilibria as a solution to a convex combinatorial optimization problem. We analyze this optimization problem and obtain insights into the several non trivial features of the equilibrium behavior. These features in turn allows to describe the structure of the solutions and efficient algorithms to obtain then. The model is described as ballot stuffing game in a plebiscite but has applications in security and auditing games. The results are extended to a parliamentary election model. Numerical examples illustrate applications of the game.

cs.GT↗

arXiv2024

Timely Offloading in Mobile Edge Cloud Systems

Future real-time applications like smart cities will use complex Machine Learning (ML) models for a variety of tasks. Timely status information is required for these applications to be reliable. Offloading computation to a mobile edge cloud (MEC) can reduce the completion time of these tasks. However, using the MEC may come at a cost such as related to use of a cloud service or privacy. In this paper, we consider a source that generates time-stamped status updates for delivery to a monitor after processing by the mobile device or MEC. We study how a scheduler must forward these updates to achieve timely updates at the monitor but also limit MEC usage. We measure timeliness at the monitor using the age of information (AoI) metric. We formulate this problem as an infinite horizon Markov decision process (MDP) with an average cost criterion. We prove that an optimal scheduling policy has an age-threshold structure that depends on how long an update has been in service.

eess.SY↗

arXiv2024

Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G

Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces. While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks. Such tools struggle to cope with the non-trivial challenges of the network environment and the growing demands of emerging use cases. In this paper, we revisit the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems. These systems acquire common sense by exploiting different cognitive abilities such as perception, analogy, and reasoning, that enable them to generalize and deal with unforeseen scenarios. Towards developing the components of such a system, we start by showing how the perception module can be built through abstracting real-world elements into generalizable representations. These representations are then used to create a world model, founded on principles of causality and hyper-dimensional (HD) computing, that aligns with intuitive physics and enables analogical reasoning, that define common sense. Then, we explain how methods such as integrated information theory play a role in the proposed intent-driven and objective-driven planning methods that maneuver the AGI-native network to take actions. Next, we discuss how an AGI-native network can enable use cases related to human and autonomous agents: a) analogical reasoning for next-generation DTs, b) synchronized and resilient experiences for cognitive avatars, and c) brain-level metaverse experiences like holographic teleportation. Finally, we conclude with a set of recommendations to build AGI-native systems. Ultimately, we envision this paper as a roadmap for the beyond 6G era.

cs.AI↗

arXiv2023

High-speed sensing of RF signals with phase change materials

RF radiation spectrum is central to wireless and radar systems among numerous high-frequency device technologies. Here, we demonstrate sensing of RF signals in the technologically relevant 2.4 GHz range utilizing vanadium dioxide (VO2), a quantum material that has garnered significant interest for its insulator-to-metal transition. We find the electrical resistance of both stoichiometric as well as off-stoichiometric vanadium oxide films can be modulated with RF wave exposures from a distance. The response of the materials to the RF waves can be enhanced by either increasing the power received by the sample or reducing channel separation. We report a significant ~73% drop in resistance with a 5 {\mu}m channel gap of the VO2 film at a characteristic response time of 16 microseconds. The peak sensitivity is proximal to the phase transition temperature boundary that can be engineered via doping and crystal chemistry. Dynamic sensing measurements highlight the films' rapid response and broad-spectrum sensitivity. Engineering electronic phase boundaries in correlated electron systems could offer new capabilities in emerging communication technologies.

physics.app-ph↗

arXiv2022

Privacy Leakage in Discrete Time Updating Systems

A source generates time-stamped update packets that are sent to a server and then forwarded to a monitor. This occurs in the presence of an adversary that can infer information about the source by observing the output process of the server. The server wishes to release updates in a timely way to the monitor but also wishes to minimize the information leaked to the adversary. We analyze the trade-off between the age of information (AoI) and the maximal leakage for systems in which the source generates updates as a Bernoulli process. For a time slotted system in which sending an update requires one slot, we consider three server policies: (1) Memoryless with Bernoulli Thinning (MBT): arriving updates are queued with some probability and head-of-line update is released after a geometric holding time; (2) Deterministic Accumulate-and-Dump (DAD): the most recently generated update (if any) is released after a fixed time; (3) Random Accumulate-and-Dump (RAD): the most recently generated update (if any) is released after a geometric waiting time. We show that for the same maximal leakage rate, the DAD policy achieves lower age compared to the other two policies but is restricted to discrete age-leakage operating points.

eess.SY↗

arXiv2021

Tale of Seven Alerts: Enhancing Wireless Emergency Alerts (WEAs) to Reduce Cellular Network Usage During Disasters

In weather disasters, first responders access dedicated communication channels different from civilian commercial channels to facilitate rescues. However, rescues in recent disasters have increasingly involved civilian and volunteer forces, requiring civilian channels not to be overloaded with traffic. We explore seven enhancements to the wording of Wireless Emergency Alerts (WEAs) and their effectiveness in getting smartphone users to comply, including reducing frivolous mobile data consumption during critical weather disasters. We conducted a between-subjects survey (N=898), in which participants were either assigned no alert (control) or an alert framed as Basic Information, Altruism, Multimedia, Negative Feedback, Positive Feedback, Reward, or Punishment. We find that Basic Information alerts resulted in the largest reduction of multimedia and video services usage; we also find that Punishment alerts have the lowest absolute compliance. This work has implications for creating more effective WEAs and providing a better understanding of how wording can affect emergency alert compliance.

cs.HC↗

arXiv2020

Modeling the Impact of 5G Leakage on Weather Prediction

The 5G band allocated in the 26 GHz spectrum referred to as 3GPP band n258, has generated a lot of anxiety and concern in the meteorological data forecasting community including the National Oceanic and Atmospheric Administration (NOAA). Unlike traditional spectrum coexistence problems, the issue here stems from the leakage of n258 band transmissions impacting the observations of passive sensors (e.g. AMSU-A) operating at 23.8 GHz on weather satellites used to detect the amount of water vapor in the atmosphere, which in turn affects weather forecasting and predictions. In this paper, we study the impact of 5G leakage on the accuracy of data assimilation based weather prediction algorithms by using a first order propagation model to characterize the effect of the leakage signal on the brightness temperature (atmospheric radiance) and the induced noise temperature at the receiving antenna of the passive sensor (radiometer) on the weather observation satellite. We then characterize the resulting inaccuracies when using the Weather Research and Forecasting Data Assimilation model (WRFDA) to predict temperature and rainfall. For example, the impact of 5G leakage of -20dBW to -15dBW on the well-known Super Tuesday Tornado Outbreak data set, affects the meteorological forecasting up to 0.9 mm in precipitation and 1.3 °C in 2m-temperature. We outline future directions for both improved modeling of 5G leakage effects as well as mitigation using cross-layer antenna techniques coupled with resource allocation.

eess.SP↗

arXiv2019

Learning End-User Behavior for Optimized Bidding in HetNets: Impact on User/Network Association

We study the impact of end-user behavior on service provider (SP) bidding and user/network association in a HetNet with multiple SPs while considering the uncertainty in the service guarantees offered by the SPs. Using Prospect Theory (PT) to model end-user decision making that deviates from expected utility theory (EUT), we formulate user association with SPs as a multiple leader Stackelberg game where each SP offers a bid to each user that includes a data rate with a certain probabilistic service guarantee and at a given price, while the user chooses the best offer among multiple such bids. We show that when users underweight the advertised service guarantees of the SPs (a behavior observed under uncertainty), the rejection rate of the bids increases dramatically which in turn decreases the SPs utilities and service rates. To overcome this, we propose a two-stage learning-based optimized bidding framework for SPs. In the first stage, we use a support vector machine (SVM) learning algorithm to predict users' binary decisions (accept/reject bids), and then in the second stage we cast the utility-optimized bidding problem as a Markov Decision Problem (MDP) and propose a reinforcement learning-based dynamic programming algorithm to efficiently solve it. Simulation results and computational complexity analysis validate the efficiency of our proposed model.

eess.SY↗

arXiv2018

Optimizing Throughput Performance in Distributed MIMO Wi-Fi Networks using Deep Reinforcement Learning

This paper explores the feasibility of leveraging concepts from deep reinforcement learning (DRL) to enable dynamic resource management in Wi-Fi networks implementing distributed multi-user MIMO (D-MIMO). D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously. This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks: (i) channel assignment of D-MIMO groups, and (ii) deciding how to cluster access points to form D-MIMO groups, in order to maximize user throughput performance. These problems are known to be NP-Hard and only heuristic solutions exist in literature. We construct a DRL framework through which a learning agent interacts with a D-MIMO Wi-Fi network, learns about the network environment, and is successful in converging to policies which address the aforementioned problems. Through extensive simulations and on-line training based on D-MIMO Wi-Fi networks, this paper demonstrates the efficacy of DRL in achieving an improvement of 20% in user throughput performance compared to heuristic solutions, particularly when network conditions are dynamic. This work also showcases the effectiveness of DRL in meeting multiple network objectives simultaneously, for instance, maximizing throughput of users as well as fairness of throughput among them.

cs.IT↗

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↗

arXiv2017

Managing Price Uncertainty in Prosumer-Centric Energy Trading: A Prospect-Theoretic Stackelberg Game Approach

In this paper, the problem of energy trading between smart grid prosumers, who can simultaneously consume and produce energy, and a grid power company is studied. The problem is formulated as a single-leader, multiple-follower Stackelberg game between the power company and multiple prosumers. In this game, the power company acts as a leader who determines the pricing strategy that maximizes its profits, while the prosumers act as followers who react by choosing the amount of energy to buy or sell so as to optimize their current and future profits. The proposed game accounts for each prosumer's subjective decision when faced with the uncertainty of profits, induced by the random future price. In particular, the framing effect, from the framework of prospect theory (PT), is used to account for each prosumer's valuation of its gains and losses with respect to an individual utility reference point. The reference point changes between prosumers and stems from their past experience and future aspirations of profits. The followers' noncooperative game is shown to admit a unique pure-strategy Nash equilibrium (NE) under classical game theory (CGT) which is obtained using a fully distributed algorithm. The results are extended to account for the case of PT using algorithmic solutions that can achieve an NE under certain conditions. Simulation results show that the total grid load varies significantly with the prosumers' reference point and their loss-aversion level. In addition, it is shown that the power company's profits considerably decrease when it fails to account for the prosumers' subjective perceptions under PT.

cs.GT↗

arXiv2017

Resilient Critical Infrastructure: Bayesian Network Analysis and Contract-Based Optimization

Instilling resilience in critical infrastructure (CI) such as dams or power grids is a major challenge for tomorrow's cities and communities. Resilience, here, pertains to a CI's ability to adapt or rapidly recover from disruptive events. In this paper, the problem of optimizing and managing the resilience of CIs is studied. In particular, a comprehensive two-fold framework is proposed to improve CI resilience by considering both the individual CIs and their collective contribution to an entire system of multiple CIs. To this end, a novel analytical resilience index is proposed to measure the effect of each CI's physical components on its probability of failure. In particular, a Markov chain defining each CI's performance state and a Bayesian network modeling the probability of failure are introduced to infer each CI's resilience index. Then, to maximize the resilience of a system of CIs, a novel approach for allocating resources, such as drones or maintenance personnel, is proposed. In particular, a comprehensive resource allocation framework, based on the tools of contract theory, is proposed enabling the system operator to optimally allocate resources, such as, redundant components or monitoring devices to each individual CI based on its economic contribution to the entire system. The optimal solution of the contract-based resilience resource allocation problem is analytically derived using dynamic programming. The proposed framework is then evaluated using a case study pertaining to hydropower dams and their interdependence to the power grid. Simulation results, within the case study, show that the system operator can economically benefit from allocating the resources while dams have a 60% average improvement over their initial resilience indices.

math.OC↗

arXiv2017

Smart Routing of Electric Vehicles for Load Balancing in Smart Grids

Electric vehicles (EVs) are expected to be a major component of the smart grid. The rapid proliferation of EVs will introduce an unprecedented load on the existing electric grid due to the charging/discharging behavior of the EVs, thus motivating the need for novel approaches for routing EVs across the grid. In this paper, a novel gametheoretic framework for smart routing of EVs within the smart grid is proposed. The goal of this framework is to balance the electricity load across the grid while taking into account the traffic congestion and the waiting time at charging stations. The EV routing problem is formulated as a noncooperative game. For this game, it is shown that selfish behavior of EVs will result in a pure-strategy Nash equilibrium with the price of anarchy upper bounded by the variance of the ground load induced by the residential, industrial, or commercial users. Moreover, the results are extended to capture the stochastic nature of induced ground load as well as the subjective behavior of the owners of EVs as captured by using notions from the behavioral framework of prospect theory. Simulation results provide new insights on more efficient energy pricing at charging stations and under more realistic grid conditions.

cs.GT↗

arXiv2017

Coverage and Rate Analysis of Super Wi-Fi Networks Using Stochastic Geometry

Recent regulatory changes proposed by the Federal Communications Commission (FCC) permitting unlicensed use of television white space (TVWS) channels present new opportunities for designing wireless networks that make efficient use of this spectrum. The favorable propagation characteristics of these channels and their widespread availability, especially in rural areas, make them well-suited for providing broadband services in sparsely populated regions where economic factors hinder deployment of such services on licensed spectrum. In this context, this paper explores the deployment of an outdoor Wi-Fi-like network operating in TVWS channels, referred to commonly as a Super Wi-Fi network. Since regulations governing unlicensed use of these channels allow (a) mounting fixed devices up to a height of 30 m and operation at transmit powers of up to 4 W EIRP, and (b) operation at transmit powers of up to 100 mW EIRP for portable devices, such networks can provide extended coverage and higher rates than traditional Wi-Fi networks. However, these gains are subject to the viability of the uplink from the portable devices (clients) to the fixed devices (access points (AP)) because of tighter restrictions on transmit power of clients compared to APs. This paper leverages concepts from stochastic geometry to study the performance of such networks with specific focus on the effect of (a) transmit power asymmetry between APs and clients and its impact on uplink viability and coverage, and (b) the interplay between height and transmit power of APs in determining the network throughput. Such an analysis reveals that (a) maximum coverage of no more than 700 m is obtained even when APs are deployed at 30 m height, and (b) operating APs at transmit power of more than 1 W is beneficial only at sparse deployment densities when rate is prioritized over coverage.

cs.IT↗

arXiv2016

Stochastic Games for Smart Grid Energy Management with Prospect Prosumers

In this paper, the problem of smart grid energy management under stochastic dynamics is investigated. In the considered model, at the demand side, it is assumed that customers can act as prosumers who own renewable energy sources and can both produce and consume energy. Due to the coupling between the prosumers' decisions and the stochastic nature of renewable energy, the interaction among prosumers is formulated as a stochastic game, in which each prosumer seeks to maximize its payoff, in terms of revenues, by controlling its energy consumption and demand. In particular, the subjective behavior of prosumers is explicitly reflected into their payoff functions using prospect theory, a powerful framework that allows modeling real-life human choices. For this prospect-based stochastic game, it is shown that there always exists a stationary Nash equilibrium where the prosumers' trading policies in the equilibrium are independent of the time and their histories of the play. Moreover, a novel distributed algorithm with no information sharing among prosumers is proposed and shown to converge to an $\epsilon$-Nash equilibrium. On the other hand, at the supply side, the interaction between the utility company and the prosumers is formulated as an online optimization problem in which the utility company's goal is to learn its optimal energy allocation rules. For this case, it is shown that such an optimization problem admits a no-regret algorithm meaning that regardless of the actual outcome of the game among the prosumers, the utility company can follow a strategy that mitigates its allocation costs as if it knew the entire demand market a priori. Simulation results show the convergence of the proposed algorithms to their predicted outcomes and present new insights resulting from prospect theory that contribute toward more efficient energy management in the smart grids.

cs.GT↗

arXiv2016

Reading Between the Pixels: Photographic Steganography for Camera Display Messaging

We exploit human color metamers to send light-modulated messages less visible to the human eye, but recoverable by cameras. These messages are a key component to camera-display messaging, such as handheld smartphones capturing information from electronic signage. Each color pixel in the display image is modified by a particular color gradient vector. The challenge is to find the color gradient that maximizes camera response, while minimizing human response. The mismatch in human spectral and camera sensitivity curves creates an opportunity for hidden messaging. Our approach does not require knowledge of these sensitivity curves, instead we employ a data-driven method. We learn an ellipsoidal partitioning of the six-dimensional space of colors and color gradients. This partitioning creates metamer sets defined by the base color at the display pixel and the color gradient direction for message encoding. We sample from the resulting metamer sets to find color steps for each base color to embed a binary message into an arbitrary image with reduced visible artifacts. Unlike previous methods that rely on visually obtrusive intensity modulation, we embed with color so that the message is more hidden. Ordinary displays and cameras are used without the need for expensive LEDs or high speed devices. The primary contribution of this work is a framework to map the pixels in an arbitrary image to a metamer pair for steganographic photo messaging.

cs.CV↗