arXiv subjects

Indrakshi Dey

Publications and source records attributed to Indrakshi Dey.

At least 19 recordsLinked to original sources

arXiv2026

Identifiability in Quantum State, Process, and Network Tomography

Quantum State Tomography (QST), Quantum Process Tomography (QPT), and Quantum Network Tomography (QNT) are related parameter-estimation problems that aim to reconstruct different physical quantities. QST estimates an unknown quantum state, represented by its density matrix, from the measurement outcomes. QPT characterises an unknown quantum channel using known input states and measurements of the corresponding outputs. QNT, in contrast, aims to infer parameters associated with individual links from end-to-end probe measurements collected at accessible monitor nodes. A key distinction among the three tomography problems lies in the conditions required to achieve identifiability, the ability to determine unknown parameters uniquely from the available measurement statistics. In QST and QPT, the experimenter can choose an Informationally Complete (IC) measurement set. QNT limits the reachable measurements to what topology and monitor placement allow, so the admissible probe paths fix the information available about the link parameters. This work studies all three tomography problems through a common Fisher Information Matrix (FIM). We factorise QNT FIM and show that its rank equals the rank of the path-link incidence matrix at every interior parameter value, so local and global identifiability coincide. We then show that QST and QPT attain full rank under IC settings, QNT loses rank when the probe paths leave link parameters indistinguishable, and increasing the number of copies scales the FIM eigenvalues while leaving its rank fixed.

quant-ph↗

arXiv2026

Identifiability and Estimation Precision in Quantum Network Tomography with Imperfect Bell-State Measurements

We study Quantum Network Tomography (QNT) for end-to-end link-error characterization under imperfect Bell-state measurements (BSMs), where multiplicative coupling between link and measurement parameters makes identifiability non-trivial. For an n-node star network, we design probes that ensure unique identifiability and derive closed-form expressions for the Fisher Information Matrix (FIM) and Maximum Likelihood Estimators (MLEs), and characterize estimation precision through the Cramer-Rao Bound (CRB). The results show that BSM imperfections degrade estimation precision, while the proposed probes maintain nearly stable precision for individual link parameters as the network size increases. Monte Carlo simulations further confirm that the Mean Squared Error (MSE) approaches the CRB with increasing sample size.

quant-ph↗

arXiv2026

Velocity Index Modulation for Movable Antenna Systems

In movable antenna (MA) systems, antenna movement induces Doppler frequency shifts that are conventionally treated as an impairment requiring mitigation. In this paper, we propose \emph{Velocity Index Modulation for Movable Antennas} (VIM-MA), which reframes this Doppler effect as an additional information-bearing degree of freedom. The transmitter selects the antenna movement velocity from a pre-designed discrete codebook, so that the resulting Doppler shift conveys extra index bits beyond those carried by the conventional modulation symbol. Codebook design is formulated as a spectral efficiency maximization over the velocity spacing $δ$ and codebook size $N_v$, subject to an average-information Cramér--Rao-type bound (AIF-CRB) on velocity estimation accuracy, a physical track length constraint, and a spatial channel decorrelation constraint. A logarithmic change of variables renders the problem convex and yields a closed-form solution. We further establish that the peak codebook velocity equals $D_{\max}/T_s$, and that the decorrelation-limited spacing always lies below the Rayleigh Doppler resolution, so that VIM-MA is intrinsically a super-resolution scheme. A covariance-matched detector is derived that requires neither per-path angle knowledge nor channel state information. Simulation results show that the decorrelation-limited codebook, which carries five index bits over a $10λ$ aperture, is attainable only with oracle angle knowledge, whereas the channel-state-free detector is limited to three bits but reaches that payload approximately $10$~dB earlier than position-domain indexing charged a realistic pilot budget.

eess.SP↗

arXiv2026

Learnability, Identifiability, and Monitor Placement in Quantum Network Tomography

Reliable quantum communication requires accurate characterization of the quantum links. This paper studies Quantum Network Tomography (QNT) under limited monitoring resources, where unknown link parameters are inferred from path-based measurements performed at monitor nodes. We introduce a cyclic sequential QNT protocol (CSQP) for arbitrary network topologies and develop a fixed-point learnability framework with an explicit algorithm for estimating link-level Werner parameters. We characterize identifiability through the rank of the path-link incidence matrix and show that the CSQP learnability conditions guarantee full rank and a nonsingular Quantum Fisher Information Matrix (QFIM). Building on this framework, we formulate monitor placement and measurement assignment as an optimization problem whose constraints enforce learnability and identifiability without topology-specific reformulation. Two Integer Linear Programming (ILP) formulations are introduced: Unconstrained QFIM-based formulation (QF), which maximizes QFIM-trace, and monitoring-overhead constrained QFIM formulation (QMF), which maximizes QFIM-trace subject to a per-monitor overhead constraint. Both formulations are evaluated on star and tree networks to compare monitor placements and measurement assignments. The results show that QMF distributes monitoring load evenly across all monitors and provides greater potential for parallel monitoring under resource constraints, while QF is more suitable when estimation information is prioritized, particularly in practical networks with non-uniform link noise.

quant-ph↗

arXiv2026

EBGT: Epistemology-aided Bayesian Game Theory for Uplink Power Control in Stochastically Distributed IoT Tiers

Uplink power control in dense, heterogeneous Internet-of-Things (IoT) tiers is fundamentally limited by incomplete channel-state information (CSI) and mutual interference, while low size, weight, and power (SWaP) devices cannot afford the feedback and computation of conventional distributed schemes. This paper proposes EBGT, an epistemology-aided Bayesian game-theoretic framework for decentralized uplink power minimization in stochastically distributed IoT networks. Interfering users are modeled as spatially random through a Poisson point process (PPP), and each device reasons about its rivals through a two-layer belief hierarchy of inter-epistemic beliefs about opponents and intra-epistemic self-assessment, so that the transmit-power equilibrium is reached without repeated inter-node feedback. We derive a closed-form coverage-probability payoff via stochastic geometry and quantify belief convergence toward equilibrium using the Jensen--Shannon divergence (JSD) of the resulting power distributions. Monte-Carlo simulations validate the analytical coverage expressions and show that EBGT sustains the target coverage probability while reducing transmit power relative to fixed power control (FPC) and stochastic non-cooperative power control (SNCPC) baselines, particularly under stringent SINR and high-density regimes.

cs.GT↗

arXiv2026

Certifying Collective Reasoning in Multi-Agent Systems via Koopman Spectral Analysis

Orchestrated collectives of large language model (LLM) agents that debate and vote are an emerging form of computational intelligence: the intelligent behaviour resides in the \emph{interaction}, not in any single agent. They improve task accuracy, yet remain black boxes at the system level: there is no principled test of convergence, no bound on the rounds needed, and no faithful account of what drove a decision. This paper develops a novel framework based on Koopman operator theory and validates its theoretical guarantees on multi-agent consensus dynamics. Treating the collective as one nonlinear dynamical system on a communication graph, we read its essential behaviour off the spectrum of its Koopman transfer operator, an exact linear representation of the nonlinear dynamics estimated from interaction traces. The spectrum yields three machine-checkable certificates: the sub-dominant eigenvalue $λ_2$ fixes the intrinsic timescale of reasoning and yields a convergence deadline computable \emph{before} the debate runs; its eigenvector names the coherent factions the collective reasons in, and $|λ_2|$ certifies when that explanation is valid; and the leading spectral coordinates form a compressed, auditable message basis. On an attention-consensus model, the deadline tracks observed convergence with log--log correlation $0.93$ and bounds it in 96\% of 24 configurations; attribution is exact whenever the spectrum certifies metastability; eight of 32 coordinates preserve the decision at 99.7\% fidelity; and a certificate learned from 15 debates held on 60/60 held-out debates. The study runs in minutes on a CPU, making spectral certification a practical layer for trustworthy collective reasoning.

cs.MA↗

arXiv2026

A Conductance Based Amygdala Model of Threat Processing in Anxiety and Depression

Anxiety and depressive disorders are increasingly viewed as dysregulations along continuous stress-regulatory dimensions. However, existing computational approaches seldom connect interpretable circuit level mechanisms to autonomic physiology. Methods: This study develops a mechanistic framework that links amygdala dysregulation to cardiovascular stress responses for digital phenotyping and clinical interpretation. We formulated a compact, nine equation, conductance based model of the amygdala hypothalamus cardiovascular pathway. The framework extends Hodgkin Huxley formalism with three clinically grounded modulators: coping capacity, perceived stress load, and prefrontal regulatory strength. A slow, history-dependent internal state, adaptive thresholding, graded threat acknowledgement, and baroreflex coupled hypothalamic integration were used to generate heart rate and blood pressure trajectories. Results: Distinct strong, moderate, and weak regulatory regimes emerged as stable operating states of a single closed-loop system. Robust analyses showed that stochastic variability and parameter perturbation preserved regime separation, while ablation studies identified the adaptive threshold as the principal mechanism driving quantitative regime separation. Simulated cardiovascular responses remained within reported stress physiology ranges. Furthermore, external evaluation across three independent datasets supported robust agreement with real world, stress related autonomic patterns. Conclusion: A compact, mechanistic model can jointly link psychometric modulators, amygdala excitability, and downstream cardiovascular output within a single, interpretable framework. Significance: This work provides a computationally tractable basis for mechanism informed digital phenotyping, patient specific stress monitoring, and future digital twin approaches for mental health decision support.

eess.SY↗

arXiv2026

Degeneracy-Aware Resource Allocation for Resilient 6G RAN

Heterogeneous 6G radio access networks (RANs) must allocate resources reliably under interference, latency limits, imperfect channel state information (CSI), and architectural diversity. We propose a degeneracy-aware resource allocation (DG-RA) framework that casts multi-architecture orchestration as a probabilistic game and, unlike single-solution optimization, deliberately favors allocations realizable by many structurally distinct yet performance-equivalent strategy profiles. Resilience is quantified across three layers through Degeneracy-Weighted Path Robustness (DWPR), Functional Substitution Score (FSS), and an Algorithmic Resilience Quotient (ARQ). Across centralized (C-RAN), open (O-RAN), virtualized (V-RAN), and hybrid RAN architectures, and benchmarked against a fractional-programming optimizer, DG-RA matches the state-of-the-art throughput and outage at the static operating point, then exploits its equivalence set to recover $\sim$$99\%$ of throughput from a resource-unit failure with a single switch, where a single-solution optimizer needs tens of iterations to re-converge. The results recast degeneracy not as a rate booster but as a precomputed resilience reserve for disruption-tolerant 6G orchestration.

eess.SP↗

arXiv2026

Advances in Wavelet Denoising for Communication Signals: From Parameter Selection Toward Data-Driven Optimization

Wavelet denoising suppresses nonstationary, impulsive, and interference-like disturbances in communication signals, but its effectiveness depends on jointly selecting the transform family, mother wavelet, decomposition level, thresholding rule, and shrinkage function. This review synthesises studies published during 2020--2025 across ten sources using a PRISMA-aligned protocol and classifies them by parameter-selection focus and application domain. The evidence shows a shift from fixed empirical choices toward similarity-, sparsity-, entropy-, energy-, sub-band-SNR-, and task-loss-driven selection, while revealing limited communication-specific validation. To address this gap, DWT, SWT, and WPT are benchmarked for OFDM denoising under impulsive noise using SNR gain, MSE, BER, EVM, real-time feasibility, Friedman and Wilcoxon tests, efficiency-index ranking, and embedded DSP/FPGA constraints. Results show that improved waveform fidelity does not necessarily translate into better hard-decision performance, motivating receiver-level validation. A Unified Decision Framework is therefore developed and validated on synthetic pilot-aided OFDM channel estimation and measured IEEE 802.11n channels using BER, EVM, NMSE, and SNR gain. The selected configuration significantly outperforms fixed-parameter wavelet and classical baselines $\left(p < 10^{-11}\right)$, achieves the lowest estimation error, generalises to held-out data, adapts to channel conditions, and supports extension to deep-unfolding architectures.

eess.SP↗

arXiv2026

Agent-Based Modeling of Low-Emission Fertilizer Adoption for Dairy Farm Decarbonisation using Empirical Farm Data

To understand complex system dynamics in dairy farming requires tools that capture farm heterogeneity, social interactions, and cumulative environmental impacts. This study proposes an agent-based modelling(ABM) framework to simulate nitrogen management and low-emission fertiliser adoption across 295 Irish dairy farms over a 15-year period. Using empirical data, the model replicates farm communication through a social network, where adoption probabilities are driven by social contagion, farm-scale factors, and policy interventions such as subsidies and carbon taxes. The framework computes sectoral greenhouse gas emissions, cumulative abatement, and private-social costs, with Monte Carlo and sensitivity analyses quantifying uncertainty. The model achieved high predictive accuracy (R2 = 0.979, RMSE = 0.0274) and was validated against observed adoption data using a Kolmogorov-Smirnov test (D = 0.2407, p < 0.001). Adoption dynamics were fitted to Rogers logistic curves, reproducing a realistic saturation plateau (91%) while acknowledging structural laggard effects. By conceptualizing decarbonization as a socio-technical evolution rather than a purely monetary calculation, this study establishes an exploratory policy framework for evaluating the diffusion of climate strategies prior to implementation.

cs.AI↗

arXiv2026

Drift-Aware RL-based Wavelet Denoising for Network-Traffic Anomaly Detection

Traffic-utilisation measurements for network monitoring are corrupted by additive noise and statistical drift: time-dependent change in the signal's mean, variance, distributional shape, or tail behaviour. Static wavelet denoising, calibrated under stationary independent and identically distributed (i.i.d.) Gaussian assumptions, becomes mismatched under drift and, at moderate-to-high signal-to-noise ratio (SNR), over-suppresses useful structure and degrades monitoring decisions. We propose a drift-aware framework treating adaptive wavelet denoising as a preprocessing layer optimised for two tasks: anomaly detection, recovering the multi-scale transient load bursts that noise and drift obscure, and capacity estimation, recovering the operational required capacity $C_{95}$ (95th percentile of utilisation). Because localised bursts are multi-scale structure a wavelet preserves but a low-pass filter removes, detection discriminates denoiser families. A four-detector gate (Page-Hinkley, variance-ratio, Jensen-Shannon, Anderson-Darling) determines when to invoke a learned policy, and a Proximal Policy Optimization agent selects a per-window wavelet configuration over a mixed discrete-continuous action space. Unlike prior work, the reward is downstream task utility, not reconstruction fidelity. The denoiser is benchmarked, per drift type and input SNR, against a low-pass moving-average filter, VisuShrink, SureShrink, BayesShrink, and a Wiener filter. Defining the anomaly target on the clean signal and the drift gate on the corruption keeps both stages non-circular.

eess.SP↗

arXiv2026

Dyadic-Chaotic Lifting S-Boxes for Enhanced Physical-Layer Security within 6G Networks

Sixth-Generation (6G) wireless networks will interconnect billions of resource-constrained devices and time-critical services, where classical, fixed, and heavy cryptography strains latency and energy budgets and struggles against large-scale, pre-computation attacks. Physical-Layer Security (PLS) is therefore pivotal to deliver lightweight, information-theoretic protection, but still requires strong, reconfigurable confusion components that can be diversified per slice, session, or device to blunt large-scale precomputation and side-channel attacks. In order to address the above requirement, we introduce the first-ever chaos-lifted substitution box (S-box) for PLS that couples a $β$-transformation-driven dynamical system with dyadic conditional sampling to generate time-varying, seedable 8-bit permutations on demand. This construction preserves uniformity via ergodicity, yields full 8-bit bijections, and supports on-the-fly diversification across sessions. The resulting S-box attains optimal algebraic degree 7 on every output bit and high average nonlinearity 102.5 (85% of the 8-bit bound), strengthening resistance to algebraic and linear cryptanalysis. Differential and linear profiling report max DDT entry 10 (probability 0.039) and max linear probability 0.648, motivating deployment within a multi-round cipher with a strong diffusion layer, where the security-to-efficiency trade-off is compelling. Our proposed reconfigurable, lightweight S-box directly fulfills key PLS requirements of 6G networks by delivering fast, hardware-amenable confusion components with built-in agility against evolving threats.

cs.CR↗

arXiv2026

Macro--Micro Decision-Making in 6G Networks: An Agent-Based Framework for the Resource-Fungibility Landscape Resource-Fungibility Landscape

A defining feature of 6G networks is that performance depends not only on the quantity of available resources (e.g., spectrum, antennas, cache memory, compute, and fronthaul bandwidth) but also on their \emph{fungibility}, i.e., the ability of one resource to substitute for another under changing conditions. We argue that the fungibility landscape of a distributed 6G system is governed by two coupled decision scales: \emph{micro} decisions made locally by agents and \emph{macro} outcomes that emerge at the network level. Existing distributed-optimization approaches largely conflate these scales. To address this gap, we develop an agent-based-modeling (ABM) framework that separates macro and micro decisions through three operator-controllable macro choices, three micro hyperparameters, and three structural metrics. We establish six key results: (i) a two-timescale decomposition theorem, (ii) a structural-metric basis theorem, (iii) a macro--micro design rule with closed-form factorization of the emergent breakdown threshold, (iv) a fungibility--resilience monotonicity proposition, (v) a connectivity--substitutability duality theorem, and (vi) a multi-application generalization proposition. Numerical results visualize the macro fungibility landscape and the micro decision-sensitivity region for a representative 6G deployment.

eess.SP↗

arXiv2026

Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI)

Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocation strategies degrade both predictive performance and human-centric properties such as fairness and explainability. This paper presents AURORA-AI, an Adaptive Utility-driven Resource Orchestration framework for Resilient AI that unifies Hamilton-Jacobi-Bellman feedback control, Lyapunov-based stability monitoring, and a fairness-aware composite utility into a single closed-loop policy.The framework continuously redistributes computational budget across a population of heterogeneous AI models so that the global utility, defined jointly over predictive performance, demographic parity, cost, latency, robustness, and interpretability, remains maximised under disruption. The framework is evaluated in a stress-rich discrete-time simulation that concurrently injects demographic bias shocks, gradual concept drift, and abrupt black-swan disruptions, and is compared against five established controllers including Static, Round Robin, Greedy, LinUCB, and a deep reinforcement-learning agent based on Proximal Policy Optimisation. AURORA-AI achieves immediate recovery from the black-swan event compared to eighty-eight time steps for the Static baseline and twenty-two for Proximal Policy Optimisation, lifts the alpha-quantile and the super-quantile by twenty-nine and twenty-five percent respectively, simultaneously reduces the mean and maximum demographic parity gap, and increases the fraction of Lyapunov-stable operating steps. These results indicate that fairness-aware adaptive orchestration grounded in stability theory is a practical and theoretically motivated path toward resilient human-centric AI deployment.

cs.AI↗

arXiv2026

Degeneracy-Aware Resilient Resource Allocation in Cell-Free Cache-Aided MU-MIMO Networks

Cell-free cache-aided multi-user multiple-input-multiple-output (MIMO) (CF-CA-MU-MIMO) networks improve spectral efficiency through coded multicast delivery and distributed spatial multiplexing, but their distributed architecture introduces vulnerabilities to jamming, cache-aware eavesdropping, Byzantine corruption, and pilot-contamination attacks. This paper develops a degeneracy-aware resilient framework based on four vulnerability-mode partitions (subfile, edge node, multicast stream, and user) and three attack-aware structural metrics: Degeneracy-Weighted Path Robustness (DWPR$^{\mathrm{att}}$), trust-aware Functional Substitution Score (FSS$^{\mathrm{trust}}$), and a robust degeneracy index ($D_k^{\mathrm{rob}}$). These metrics are incorporated into a fully decentralized consensus-based agent framework (DC-ABM) using trust-weighted trimmed-mean aggregation and adaptive trust evolution. Five theoretical results are established: (i) a tight top-mass concentration lemma, (ii) matching memory--rate--resilience achievability and converse bounds, (iii) a robust-degeneracy bound with outage characterization, (iv) a secrecy--cache coupling theorem, and (v) a Byzantine-robust mean-square convergence result with an explicit breakdown threshold $f_{\max}$. Simulations validate the analytical bounds and demonstrate $1.8\times$ to $3\times$ faster convergence than distributed alternating direction method of multipliers (ADMM), multi-agent reinforcement learning (MARL)/graph neural network (GNN)-based control, and Su--Vaidya consensus while maintaining throughput up to the predicted threshold $f_{\max}\approx0.19$.

eess.SP↗

arXiv2026

Optimal Illumination via Joint Movement and Phase Optimization for Movable Antenna-RIS Configuration

Reconfigurable intelligent surfaces (RIS) enable programmable control of wireless propagation but remain vulnerable to persistent deep fades in static deployments. This paper introduces a Movable Antenna-enhanced RIS (MA-RIS) architecture where antenna elements physically reposition to sample independent spatial channels, enabling mobility-induced diversity. We model antenna motion using a Stochastic Differential Equation (SDE) framework capturing controlled drift and environmental diffusion. It^o calculus-based analysis characterizes steady-state antenna distributions, spatial decorrelation, and outage probability, revealing fundamental trade-offs between control strength and mobility randomness. To maximize long-term SNR while accounting for control overhead, we propose an overhead-aware Two-timescale framework separating slow antenna trajectory control from fast phase adaptation. The stochastic optimal control problem is solved via predictive approximation of the Hamilton-Jacobi-Bellman (HJB) formulation, enabling real-time implementation. Simulations validate theoretical predictions: the Two-timescale strategy achieves up to 36 dB steady-state SNR with remarkable stability, outperforming position-only control by up to 15 dB and uncontrolled baselines by over 30 dB. Despite experiencing a lower SNR than Active RIS, the proposed approach delivers up to 16 times higher energy efficiency (EE) across varying system scales, establishing a new paradigm of mobility-enabled channel adaptation for resilient wireless systems.

eess.SP↗

arXiv2026

UAV-based Energy-Efficient Data Collection in Smart Grids with ISAC QoS Guarantees

Dynamic line rating (DLR) is a methodology that requires timely monitoring data to determine the real-time ampacity of power lines. However, DLR monitoring devices (MD) are vulnerable to connectivity disruptions, leading to missing or delayed data. Although unmanned aerial vehicles (UAV) can enable resilient data collection from MD, their limited onboard energy challenges timely monitoring over extended transmission corridors with flight hazards. This paper proposes a cooperative UAV-based data collection framework with integrated sensing and communication (ISAC) to support timely DLR updates. In this framework, ISAC is employed to maintain the sensing and communication quality required for safe and cooperative UAV data collection. Accordingly, a joint energy minimization problem is formulated over UAV trajectories and collection scheduling under ISAC constraints. To solve it, a hybrid algorithm combining deep reinforcement learning (DRL) and semidefinite relaxation (SDR) is proposed, where DRL optimizes the trajectory and collection scheduling, while SDR is used to handle the non-convex ISAC constraints. Simulation results show that the proposed scheme reduces energy consumption by up to 34.6% compared with offline benchmarks and by about 2.2% compared with the separated sensing-and-communication baseline, while satisfying the minute-level timescale requirement of DLR.

eess.SP↗

arXiv2026

Adaptive RSMA-OMA for Resilient MIMO Networks Under Imperfect CSI and SIC

This paper addresses the challenge of power control in Rate-Splitting Multiple Access (RSMA) systems for downlink Multi-Input Multi-Output (MIMO) networks under practical impairments such as spatial correlation, imperfect Channel State Information (CSI), and residual Successive Interference Cancellation (SIC) errors. We propose a novel degeneracyaware framework that adaptively adjusts the power allocation between the common and private streams, ensuring optimal performance despite CSI uncertainty and imperfect SIC. Our approach incorporates a dynamic switching mechanism between RSMA and Orthogonal Multiple Access (OMA) to maintain system feasibility and resilience in the face of these impairments. Extensive analytical and simulation results demonstrate that the proposed framework significantly enhances power efficiency, mitigates outage probability, and improves overall system robustness, making RSMA a viable and efficient solution for modern wireless networks with realistic CSI and SIC conditions.

eess.SP↗