arXiv subjects

Onel L. A. López

Publications and source records attributed to Onel L. A. López.

At least 19 recordsLinked to original sources

arXiv2026

Toward Proactive RF Charging Scheduling: Generative AI for Decision Support

Radio frequency wireless power transfer (RF-WPT) is an enabling technology for supporting uninterrupted communications in future Internet of Things systems by reducing the need for battery replacement and mitigating battery-waste-related issues. For large-scale RF-WPT deployment, one of the main challenges is the scheduler-level resource allocation. Specifically, the RF charger must decide how much energy to deliver, when, and to whom, under limited charging resources, incomplete receiver-side information, and uncertain near-future charging conditions. This article positions generative artificial intelligence (GenAI) as a promising tool for this setting because it can foresee multiple plausible charging scenarios conditioned on coarse operational context and receiver-side information. We propose GenAI to act as an uncertainty-aware support layer for the RF-WPT scheduler rather than as a standalone forecasting or decision-making tool. To this end, we first revisit the main challenges of RF-WPT scheduling, and discuss how major GenAI families can support uncertainty-aware charging decisions by generating scenario-based inputs for downstream tasks. We then present a case study showing that distribution-aware prediction can improve robust charging decisions over deterministic, ensemble, and non-learning baselines, particularly under risk-sensitive objectives. Finally, we outline key open challenges and future research directions.

eess.SY↗

arXiv2026

Assessing Fixed-Batch Reporting for Deadline-constrained Inference in Intermittently Powered IoT

Energy-harvesting Internet of Things (IoT) devices must decide whether to transmit each observation immediately or accumulate several observations before reporting to the edge. Early reports make information from initial observations available sooner at the edge but require more transmission actions, whereas larger batches save reports while delaying that information and retaining more state. We develop a unified analytical framework to compare this fixed-batching choice in scenarios with repeated, deadline-constrained inference cycles. Every configuration processes the same ordered observations and yields the same final posterior if all reports arrive, enabling a controlled comparison of the persistent state, actions, and energy costs induced by batching. Using reductions in Bayesian risk under the continuous ranked probability score (CRPS), we express long-run timely value exactly as a statistical-value-weighted sum of expected decision-weighted report availabilities. This separates the inferential value of observations from their availability at application-relevant decision times. We construct exact Markov-reward models accounting jointly for intermittent harvesting, finite storage, energy carry-over, unreliable delivery, retransmissions, and deadlines. We prove that the long-run regime is well defined and derive exact batch-size comparisons, timing benchmarks, and a decomposition of harvesting-law effects into within-cycle and carry-over contributions. Directly evaluable Gaussian and Gaussian-mixture belief specializations show that the preferred batch size can change with the prior, the decision-time profile, link reliability, harvested-energy statistics, and accumulation cost.

eess.SY↗

arXiv2026

Energy-Neutral Coverage Optimization by Joint Deployment and Scheduling in Ambient IoT Devices with Directional Sensing

Ambient IoT (A-IoT) devices rely on energy harvesting and duty cycling to sustain operation, thereby fundamentally changing collaborative sensing compared with traditional always-ON sensor networks. In this paper, we study the joint deployment and sensing scheduling of A-IoT devices equipped with directional sensing. We explore four solution strategies: (i) a grid deployment with static duty cycling, (ii) a centralized policy-gradient reinforcement learning (RL) approach that begins with a grid deployment and learns energy-aware device relocation and duty-cycling policies, (iii) a mixed-integer linear programming (LP) approach that couples static deployment design with duty-cycle allocation, and (iv) a hybrid LP+RL that combines optimization-based initialization with learning-based refinement. Using representative A-IoT use cases, we evaluate coverage as a function of device density, field-of-view, and maximum feasible duty cycle, determined by harvested energy and device consumption. Numerical results indicate that the proposed LP+RL and RL policies consistently outperform both the grid baseline and the LP-based method, achieving up to 2x higher mean effective coverage in low and medium energy harvesting (EH) regimes. In contrast, the standalone LP method remains limited by its conservative static duty cycle allocation under tight EH constraints. Moreover, the structured initialization of the LP+RL method substantially accelerates convergence, reducing the total offline optimization time by up to 10x compared to the standalone RL.

eess.SY↗

arXiv2026

Power from Space: Coordinated Satellite Charging for Off-Grid Wireless Systems

Satellite-enabled wireless power transfer (WPT) may be a transformative solution for charging Internet of Things (IoT) devices in off-grid scenarios where traditional technologies struggle to efficiently meet urgent energy demands. In this article, we review the advantages and limitations of microwave-based long-distance charging for satellite-enabled WPT. We then introduce our vision of coordinated space-based WPT, where multiple satellites jointly serve networks of ground devices. Potential use cases are presented highlighting application requirements. We evaluate the average received power at the target locations using two coordination schemes and perform a statistical characterization of the power spillover on undesired locations. We also shed light on the performance of inter-satellite laser WPT for different operating distances and transmit-receive apertures of the peer satellites. Moreover, we explore the integration of metasurfaces on satellite apertures and ground networks to boost energy conversion efficiency, scalability, and beam management. Finally, we outline relevant challenges and research directions towards implementing our vision.

eess.SP↗

arXiv2026

Holographic Beamforming for Range-Doppler Sidelobe Suppression in OFDM-ISAC

This paper investigates range--Doppler (RD) sidelobe suppression in an integrated sensing and communications system with a reconfigurable holographic surface (RHS). We jointly design the digital feed precoders and RHS amplitudes to minimize the integrated RD sidelobe level subject to transmit-power, target-illumination, and communication constraints. For this, we develop an alternating successive convex approximation method updating both variable blocks through quadratic subproblems. Numerical results reveal diminishing returns from additional feeds, while increasing the aperture remains more effective and allows the RHS design to reduce sidelobe level.

eess.SP↗

arXiv2026

Beam Selection for Delay-Doppler Visibility in Multi-Target MIMO-OFDM Sensing

This paper studies leakage-aware beam selection for multi-target multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) sensing. We focus on ensuring that each hypothesized target remains detectable at its own delay-Doppler (DD) bin despite leakage from other targets. For this, we derive a visibility metric that separates the desired focused power from pairwise leakage caused by the finite-grid OFDM ambiguity kernel, receive angular coupling, and transmit codebook gains. Then, we formulate a max-min beam-selection and power-allocation problem in which the sensing signal over the fixed OFDM time-frequency grid is transmitted by a limited number of selected beams. The resulting problem is solved via a bisection-based mixed-integer successive convex approximation framework. A time-sharing formulation is also presented as a globally solvable benchmark. Numerical results show that the proposed pairwise leakage-aware design achieves higher worst-target visibility than the time-sharing, global leakage-suppression, illumination-based, and random beam-selection baselines, especially when targets are close in angle or DD.

eess.SP↗

arXiv2026

MDP-based Energy-aware Task Scheduling for Battery-less IoT

Battery-less Internet of Things (IoT) devices rely on ambient energy harvesting and therefore require scheduling policies that jointly account for energy intermittency and hard timing constraints. This challenge is especially acute in periodic monitoring applications, where a sensing--computing--transmitting task chain must be completed within each reporting cycle. In this paper, we formulate this problem within a setting characterized by independently and identically distributed (i.i.d.) energy arrivals as a long-term average-reward Markov decision process (MDP) that explicitly captures capacitor-voltage evolution, task ordering, permissible start windows, and safe-execution requirements. We further propose rewards that promote reliable task completion while penalizing risky low-energy execution. We prove that the considered MDP is unichain and that the optimal stationary policy has a threshold structure, which leads to an optimal stationary threshold-based (OSTB) scheduler. To account for more realistic energy sources, we additionally study a correlated harvesting model based on a finite-state Markov process and show that the proposed framework can be applied to this richer setting under conservative sufficient conditions. Finally, numerical results show that OSTB outperforms representative baselines in terms of long-term full-chain completion rate, power failures, and latency, particularly when harvested energy is scarce.

eess.SY↗

arXiv2026

Sequential Monte Carlo for Resilient Networks: Assessment, Mitigation, and Generative Modeling

Resilience is becoming crucial for future wireless networks, which must withstand, adapt to, and recover from rare but potentially cascading disruptions. This paper develops a sequential Monte Carlo (SMC) simulation framework for such systems, in which resilience failures are formulated as path-dependent rare events arising from staged degradation and delayed recovery, and are decomposed into semantically interpretable levels defined by a reaction coordinate. Building on this structure, we present a fixed-level splitting approach with budget-aware population control, enabling efficient estimation of rare non-recovery probabilities. We discuss the potential reuse of SMC checkpoints as representative near-critical states for policy evaluation and simulation-based selection. We further extend the methodology to learned stochastic simulation by using generative sequence models as restartable surrogates within data-driven digital twins. We showcase the framework in a delay-critical wireless network use case, where SMC substantially improves over standard Monte Carlo in rare-event regimes with both physical and learned simulators.

eess.SY↗

arXiv2026

Discrete Diffusion for Codebook-Based Beam Candidate Generation

Millimeter-wave (mmWave) communication enables high data rates through large bandwidths and highly directional beamforming, but its sensitivity to blockage and mobility makes reliable beam alignment a central challenge. Limited-probing beam management is a fundamental problem in codebook-based mmWave systems, where only a small subset of beams can be evaluated simultaneously, and the serving decision is restricted to the probed set. Under mobility and noisy feedback, this leads to a sequential and partially observable decision problem in which performance depends critically on the quality of the proposed beam candidates. In this paper, we consider limited-probing beam management and develop a history-conditioned discrete denoising diffusion probabilistic model for beam candidate generation. The proposed method learns from logged probing histories a conditional distribution over promising beam indices, which is then used to construct probing candidates online. Numerical analysis shows that the proposed approach consistently achieves better signal-to-noise ratio, beam-miss probability, and conditional probe regret under tight probing budgets compared with strong learning-based and discriminative baselines. The gains are especially pronounced in low-probing regimes, where accurate candidate generation is most critical.

eess.SP↗

arXiv2025

Echo-Conditioned Denoising Diffusion Probabilistic Models for Multi-Target Tracking in RF Sensing

In this paper, we consider a dynamic radio frequency sensing system aiming to spatially track multiple targets over time. We develop a conditional denoising diffusion probabilistic model (C-DDPM)-assisted framework that learns the temporal evolution of target parameters by leveraging the noisy echo observations as conditioning features. The proposed framework integrates a variational autoencoder (VAE) for echo compression and utilizes classifier-free guidance to enhance conditional denoising. In each transmission block, VAE encodes the received echo into a latent representation that conditions DDPM to predict future target states, which are then used for codebook beam selection. Simulation results show that the proposed approach outperforms classical signal processing, filtering, and deep learning benchmarks. The C-DDPM-assisted framework achieves significantly lower estimation errors in both angle and distance tracking, demonstrating the potential of generative models for integrated sensing and communications.

eess.SP↗

arXiv2025

Context-awareness for Dependable Low-Power IoT

Dependability is the ability to consistently deliver trusted and uninterrupted service in the face of operational uncertainties. Ensuring dependable operation in large-scale, energy-constrained Internet of Things (IoT) deployments is as crucial as challenging, and calls for context-aware protocols where context refers to situational or state information. In this paper, we identify four critical context dimensions for IoT networks, namely energy status, information freshness, task relevance, and physical/medium conditions, and show how each one underpins core dependability attributes. Building on these insights, we propose a two-step protocol design framework that incorporates operation-specific context fields. Through three representative use cases, we demonstrate how context awareness can significantly enhance system dependability while imposing only minimal control-plane overhead.

eess.SY↗

arXiv2025

On the Deployment of Multiple Radio Stripes for Large-Scale Near-Field RF Wireless Power Transfer

This paper investigates the deployment of radio stripe systems for indoor radio-frequency (RF) wireless power transfer (WPT) in line-of-sight near-field scenarios. The focus is on environments where energy demand is concentrated in specific areas, referred to as 'hotspots', spatial zones with higher user density or consistent energy requirements. We formulate a joint clustering and radio stripe deployment problem that aims to maximize the minimum received power across all hotspots. To address the complexity, we decouple the problem into two stages: i) clustering for assigning radio stripes to hotspots based on their spatial positions and near-field propagation characteristics, and ii) antenna element placement optimization. In particular, we propose four radio stripe deployment algorithms. Two are based on general successive convex approximation (SCA) and signomial programming (SGP) methods. The other two are shape-constrained solutions where antenna elements are arranged along either straight lines or regular polygons, enabling simpler deployment. Numerical results show that the proposed clustering method converges effectively, with Chebyshev initialization significantly outperforming random initialization. The optimized deployments consistently outperform baseline benchmarks across a wide range of frequencies and radio stripe lengths, while the polygon-shaped deployment achieves better performance compared to other approaches. Meanwhile, the line-shaped deployment demonstrates an advantage under high boresight gain settings, benefiting from increased spatial diversity and broader angular coverage.

eess.SP↗

arXiv2025

6G Resilience -- White Paper

6G must be designed to withstand, adapt to, and evolve amid prolonged, complex disruptions. Mobile networks' shift from efficiency-first to sustainability-aware has motivated this white paper to assert that resilience is a primary design goal, alongside sustainability and efficiency, encompassing technology, architecture, and economics. We promote resilience by analysing dependencies between mobile networks and other critical systems, such as energy, transport, and emergency services, and illustrate how cascading failures spread through infrastructures. We formalise resilience using the 3R framework: reliability, robustness, resilience. Subsequently, we translate this into measurable capabilities: graceful degradation, situational awareness, rapid reconfiguration, and learning-driven improvement and recovery. Architecturally, we promote edge-native and locality-aware designs, open interfaces, and programmability to enable islanded operations, fallback modes, and multi-layer diversity (radio, compute, energy, timing). Key enablers include AI-native control loops with verifiable behaviour, zero-trust security rooted in hardware and supply-chain integrity, and networking techniques that prioritise critical traffic, time-sensitive flows, and inter-domain coordination. Resilience also has a techno-economic aspect: open platforms and high-quality complementors generate ecosystem externalities that enhance resilience while opening new markets. We identify nine business-model groups and several patterns aligned with the 3R objectives, and we outline governance and standardisation. This white paper serves as an initial step and catalyst for 6G resilience. It aims to inspire researchers, professionals, government officials, and the public, providing them with the essential components to understand and shape the development of 6G resilience.

eess.SP↗

arXiv2025

"Iridescent" Reflective Tags to Enable Radar-based Orientation Estimation

Accurate orientation estimation of objects can aid in scene understanding in many applications. In this paper, we consider use cases where passive tags could be deployed to assist radar systems in estimating object orientation. Towards that end, we propose the concept of passive iridescent reflective tags that selectively reflect different wavelengths in different directions. We propose a conceptual tag design based on leaky-wave antennas. We develop a framework for signal modeling and orientation estimation with a multi-tone radar. We analyze the impact of imperfect tag location information, revealing that it minimally impacts orientation estimation accuracy. To reduce estimator complexity, we propose a radiation pointing angle-based estimator with near-optimal performance. We derive its feasible orientation estimation region and show that it depends mainly on the system bandwidth. Monte Carlo simulations validate our analytical results while evincing that the low-complexity estimator achieves near-optimal accuracy and that its feasible orientation estimation region closely matches that of the other estimators. Finally, we show that the optimal number of tones increases with the sensing time under a power budget constraint, multipath effects may be negligible, signal-to-noise ratio gains rise with the number of tones, and many radar antennas can hurt estimation performance when the signal contains very few tones.

eess.SP↗

arXiv2025

Evaluating Task Execution Performance Under Energy Measurement Overhead

Energy-awareness for adapting task execution behavior can bring several benefits in terms of performance improvement in energy harvesting (EH) Internet of Things (IoT) devices. However, the energy measurement cost of acquiring energy information, which is traditionally ignored, can potentially neutralize or even reverse the potential benefits. This paper highlights operational parameters, such as energy measurement frequency and task execution frequency, which can be tuned to improve the task execution performance of an EH-IoT device. To this end, we consider energy-blind (EB) and energy-aware (EA) task decision approaches and compare their task completion rate performance. We show that, for specific hardware design parameters of an EH-IoT device, there exists an optimal energy measurement/task execution frequency that can maximize the task completion rate in both approaches. Moreover, if these parameters are not chosen appropriately, then energy measurement costs can cause EA scheduling to underperform compared to EB scheduling.

eess.SP↗

arXiv2025

Foundations for Energy-Aware Zero-Energy Devices: From Energy Sensing to Adaptive Protocols

Zero-energy devices (ZEDs) are key enablers of sustainable Internet of Things networks by operating solely on harvested ambient energy. Their limited and dynamic energy budget calls for protocols that are energy-aware and intelligently adaptive. However, designing effective energy-aware protocols for ZEDs requires theoretical models that realistically reflect device constraints. Indeed, existing approaches often oversimplify key aspects such as energy information (EI) acquisition, task-level variability, and energy storage dynamics, limiting their practical relevance and transferability. This article addresses this gap by offering a structured overview of the key modeling components, trade-offs, and limitations involved in energy-aware ZED protocol design. For this, we dissect EI acquisition methods and costs, characterize core operational tasks, analyze energy usage models and storage constraints, and review representative protocol strategies. Moreover, we offer design insights and guidelines on how ZED operation protocols can leverage EI, often illustrated through selected in-house examples. Finally, we outline key research directions to inspire more efficient and scalable protocol solutions for future ZEDs.

eess.SY↗

arXiv2025

Sense-then-Charge: Wireless Power Transfer to Unresponsive Devices with Unknown Location

This paper explores a multi-antenna dual-functional radio frequency (RF) wireless power transfer (WPT) and radar system to charge multiple unresponsive devices. We formulate a beamforming problem to maximize the minimum received power at the devices without prior location and channel state information (CSI) knowledge. We propose dividing transmission blocks into sensing and charging phases. First, the location of the devices is estimated by sending sensing signals and performing multiple signal classification and least square estimation on the received echo. Then, the estimations are used for CSI prediction and RF-WPT beamforming. Simulation results reveal that there is an optimal number of blocks allocated for sensing and charging depending on the system setup. Our sense-then-charge (STC) protocol can outperform CSI-free benchmarks and achieve near-optimal performance with a sufficient number of receive antennas and transmit power. However, STC struggles if using insufficient antennas or power as device numbers grow.

eess.SP↗

arXiv2025

Beyond Diagonal Reconfigurable Intelligent Surfaces for Multi-Carrier RF Wireless Power Transfer

Radio frequency (RF) wireless power transfer (WPT) is promising for promoting sustainability in future wireless systems, but its low end-to-end power transfer efficiency is a critical challenge. For this, reconfigurable intelligent surfaces (RISs) can be leveraged to enhance efficiency by providing nearly passive beamforming gains. Beyond diagonal (BD) RIS is a new RIS variant offering greater performance benefits than traditional diagonal RIS (D-RIS), though its potential for RF-WPT remains unexplored. Motivated by this, we consider a single-input single-output BD-RIS-aided RF-WPT system and we formulate a joint beamforming and waveform optimization problem aiming to maximize the harvested power at the receiver. We propose an optimization framework relying on successive convex approximation, alternating optimization, and semi-definite relaxation. Numerical results show that increasing the number of transmit sub-carriers or RIS elements improves the harvested power. We verify by simulation that BD-RIS leads to the same performance as D-RIS under far-field line-of-sight conditions (in the absence of mutual coupling), while it outperforms D-RIS as the non-line-of-sight components become dominant.

eess.SP↗