arXiv subjects

Israel Leyva-Mayorga

Publications and source records attributed to Israel Leyva-Mayorga.

At least 19 recordsLinked to original sources

arXiv2026

Rate-Splitting-Inspired Uplink ISAC: A Rate-Region Analysis

Integrated sensing and communication (ISAC) enables sensing and communication (S&C) functionalities to share spectrum, hardware, and signal-processing resources, but the resulting inter-functionality interference creates a fundamental receiver-design challenge in uplink operation. To this end, rate-splitting (RS)-inspired ISAC has been proposed as a flexible approach to inter-functionality interference management, whereby communication interference during sensing is partially decoded and cancelled and partially treated as noise. In this work, we characterize the Pareto-optimal communication-rate (CR)-sensing-rate (SR) rate region of RS-inspired uplink ISAC by jointly optimizing the sensing illumination and communication-message split. Closed-form CR and SR expressions are derived while accounting for residual sensing-echo interference caused by target-response estimation uncertainty. We analytically prove that the resulting RS-inspired region contains the non-orthogonal multiple access (NOMA)-inspired endpoint-order time-sharing region. We further show that, under fixed sensing illumination, residual sensing-echo interference can break the containment of the orthogonal multiple access (OMA)-inspired region by both the RS- and NOMA-inspired regions. Joint sensing-illumination optimization enlarges these non-orthogonal achievable regions and recovers the maximum CR at the zero-SR endpoint. High-signal-to-noise ratio (SNR) and near-field large-array analyses characterize the asymptotic behaviour, and numerical results validate the analysis under both near- and far-field propagation. This is the first work to characterize the Pareto-optimal rate region of RS-inspired uplink ISAC.

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arXiv2026

Rate-Splitting--Inspired Bistatic OFDM-ISAC

Achieving effective uplink bistatic ISAC over an OFDM waveform gives rise to challenging interference structures. These are mostly due to unequal direct- and echo-path contributions and Doppler-induced ICI, rendering orthogonal resource separation and fixed SIC strategies inadequate. To address this problem, we propose a RS-inspired framework where the transmitter splits each communication message into a robust and a supplementary stream, which are jointly superposed over a sensing signal. Furthermore, we present the design of a staged sensing-communication receiver. Based on this framework, we derive tractable per-subcarrier SINR expressions and establish the relation between sensing accuracy and communication reliability based on the Fisher information. Building on these, we formulate a joint power-allocation problem for SE maximization under sensing-performance and power constraints. The resulting non-convex formulation is solved using convex surrogates and fractional programming. Numerical results demonstrate that, compared to NOMA-inspired baselines, the proposed framework provides more effective IFI management and improved robustness to Doppler-induced ICI.

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arXiv2026

Statistical Analysis for Energy-Efficient Satellite Edge Computing with Latency Guarantees

Being able to provide latency guarantees for orbital edge computing applications through Low Earth Orbit (LEO) satellite constellations is a major milestone for their integration into 5G and 6G networks. However, achieving this is fundamentally challenged by the inherent randomness in both communication and computing latency, driven by complex network dynamics, satellite motion, and hardware variability. In this paper, we perform a statistical analysis of the latency of satellite edge computing using representative computing hardware and an object detection algorithm running on a satellite image dataset. The resulting model captures the trade-off between data availability and estimation uncertainty, enabling data-driven optimization methods to meet latency targets with statistical guarantees while minimizing energy consumption. Our results show that parametric estimation and quantile regression for the execution time of the image processing algorithms can be effectively combined with models for the communication latency to select an optimal GPU clock frequency. This achieves a 95% probability of meeting a $500$ ms end-to-end deadline while reducing energy consumption by more than 50% compared to a baseline that relies on a Chebyshev-Cantelli inequality to bound execution-time quantiles. The proposed framework is generalizable across satellite edge computing workloads and hardware platforms.

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arXiv2026

Cooperative Multi-Static Target Localization for ISAC in Cluttered Industrial IoT Networks

In this paper, we propose a novel integrated sensing and communications (ISAC) framework for collaborative multi-static target localization in dense Industrial Internet-of-Things (IIoT) environments in the presence of environmental clutter. We first develop a lightweight temporal clutter-suppression learning method to mitigate persistent reflections. Building on this, we propose an iterative localization algorithm that integrates two key components introduced in this work: a sampling-based field-of-view-aware initialization (SFI) scheme and an empirical position error bound (PEB) scheme, which together adaptively identify the most informative subset of sensing nodes. A reliability-aware weighted least-squares estimator is then employed to fuse range and angle-of-arrival measurements from the selected sensing receivers for target localization. Numerical results demonstrate rapid convergence of the proposed method, reducing the localization RMSE by nearly two orders of magnitude within six sensing iterations to about 45 cm, while significantly outperforming all considered benchmarks under the same sensing-resource budget.

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arXiv2026

Edge Intelligence for Satellite-based Earth Observation: Scheduling Image Acquisition and Processing

Modern Earth Observation (EO) missions generate massive volumes of imagery that challenge existing downlink and ground-processing capabilities, particularly for time-critical applications. This work investigates how a low Earth orbit (LEO) satellite constellation equipped with heterogeneous edge computing resources can enable real-time semantic processing of data acquired by EO satellites. We introduce an energy-aware framework that optimizes the use of resources accounting for data acquisition, computing, and communication constraints. Although we focus on maritime surveillance, the formulation is task-agnostic and accommodates a broad class of semantic and goal-oriented inference problems. Specifically, we formulate two coupled optimization problems: (i) observation scheduling, which selects image acquisition opportunities while accounting for turbulence-induced image degradation and energy budget, and (ii) processing scheduling, which allocates semantic workloads across onboard and ground processors. We evaluate these mechanisms for the task of detection and localization of vessels, for which we quantify the benefits of turbulence-aware observation scheduling for preserving image quality and experimentally characterize the execution-time distribution of YOLOv8 on different computing platforms. Results demonstrate that task- and turbulence-aware observation scheduling can significantly improve the quality and quantity of observed targets. Furthermore, cooperative edge processing within the constellation substantially reduces power consumption compared to traditional downlink-centric architectures. These findings highlight the potential of distributed edge intelligence to enhance the responsiveness and autonomy of future satellite-based EO systems.

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arXiv2026

Initialization and Rate-Quality Functions for Generative Network Layer Protocols

Generative AI (GenAI) creates full content based on compact encodings. While GenAI has been used for applications where the generated content is returned to the encoding sender, it can also extend the capacity of communication networks by transmitting compact encodings through capacity-limited links, then generating and forwarding approximations from the GenAI node to the destination. This poses the challenge of evaluating approximation quality as a function of the rate between the source and GenAI node, while accounting for the communication overhead of learning. We present a method- and modality-agnostic initialization protocol for learning rate-quality functions in GenAI-aided networks, defining three variants: source-, node-, and destination-oriented, each with different messaging flows based on where quality is measured. The protocol augments node discovery protocols (e.g., MCP, A2A) when sources lack confidence in advertised model performance. We illustrate operation via a minimum estimation budget calculated using a distribution-free tolerance limit , and validate using a case study on image transmission under quality constraints. Results confirm the calculated budget meets the target quality requirement, with positive gains over JPEG after around 20 post-learning transmissions for a perceptual metric and more than 100 for a goal-oriented metric, providing a practical foundation for GenAI-based network compression.

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arXiv2025

Tunable Gaussian Pulse for Delay-Doppler ISAC

Integrated sensing and communication (ISAC) for next-generation networks targets robust operation under high mobility and high Doppler spread, leading to severe inter-carrier interference (ICI) in systems based on orthogonal frequency-division multiplexing (OFDM) waveforms. Delay--Doppler (DD)-domain ISAC offers a more robust foundation under high mobility, but it requires a suitable DD-domain pulse-shaping filter. The prevailing DD pulse designs are either communication-centric or static, which limits adaptation to non-stationary channels and diverse application demands. To address this limitation, this paper introduces the tunable Gaussian pulse (TGP), a DD-native, analytically tunable pulse shape parameterized by its aspect ratio $ \gamma $, chirp rate $ \alpha_c $, and phase coupling $ \beta_c $. On the sensing side, we derive closed-form Cram\'er--Rao lower bounds (CRLBs) that map $ (\gamma,\alpha_c,\beta_c) $ to fundamental delay and Doppler precision. On the communications side, we show that $ \alpha_c $ and $ \beta_c $ reshape off-diagonal covariance, and thus inter-symbol interference (ISI), without changing received power, isolating capacity effects to interference structure rather than power loss. A comprehensive trade-off analysis demonstrates that the TGP spans a flexible operational region from the high capacity of the Sinc pulse to the high precision of the root raised cosine (RRC) pulse. Notably, TGP attains near-RRC sensing precision while retaining over $ 90\% $ of Sinc's maximum capacity, achieving a balanced operating region that is not attainable by conventional static pulse designs.

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arXiv2025

Policy Gradient Algorithms for Age-of-Information Cost Minimization

Recent developments in cyber-physical systems have increased the importance of maximizing the freshness of the information about the physical environment. However, optimizing the access policies of Internet of Things devices to maximize the data freshness, measured as a function of the Age-of-Information (AoI) metric, is a challenging task. This work introduces two algorithms to optimize the information update process in cyber-physical systems operating under the generate-at-will model, by finding an online policy without knowing the characteristics of the transmission delay or the age cost function. The optimization seeks to minimize the time-average cost, which integrates the AoI at the receiver and the data transmission cost, making the approach suitable for a broad range of scenarios. Both algorithms employ policy gradient methods within the framework of model-free reinforcement learning (RL) and are specifically designed to handle continuous state and action spaces. Each algorithm minimizes the cost using a distinct strategy for deciding when to send an information update. Moreover, we demonstrate that it is feasible to apply the two strategies simultaneously, leading to an additional reduction in cost. The results demonstrate that the proposed algorithms exhibit good convergence properties and achieve a time-average cost within 3% of the optimal value, when the latter is computable. A comparison with other state-of-the-art methods shows that the proposed algorithms outperform them in one or more of the following aspects: being applicable to a broader range of scenarios, achieving a lower time-average cost, and requiring a computational cost at least one order of magnitude lower.

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arXiv2025

ISAC-Powered Distributed Matching and Resource Allocation in Multi-band NTN

Scalability is a major challenge in non-geostationary orbit (NGSO) satellite networks due to the massive number of ground users sharing the limited sub-6 GHz spectrum. Using K- and higher bands is a promising alternative to increase the accessible bandwidth, but these bands are subject to significant atmospheric attenuation, notably rainfall, which can lead to degraded performance and link outages. We present an integrated sensing and communications (ISAC)-powered framework for resilient and efficient operation of multi-band satellite networks. It is based on distributed mechanisms for atmospheric sensing, cell-to-satellite matching, and resource allocation (RA) in a 5G Non-Terrestrial Network (NTN) wide-area scenario with quasi-Earth fixed cells and a beam hopping mechanism. Results with a multi-layer multi-band constellation with satellites operating in the S- and K-bands demonstrate the benefits of our framework for ISAC-powered multi-band systems, which achieves 73% higher throughput per user when compared to single S- and K-band systems.

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arXiv2025

Toward ISAC-empowered subnetworks: Cooperative localization and iterative node selection

This paper tackles the sensing-communication trade-off in integrated sensing and communication (ISAC)-empowered subnetworks for mono-static target localization. We propose a low-complexity iterative node selection algorithm that exploits the spatial diversity of subnetwork deployments and dynamically refines the set of sensing subnetworks to maximize localization accuracy under tight resource constraints. Simulation results show that our method achieves sub-7 cm accuracy in additive white Gaussian noise (AWGN) channels within only three iterations, yielding over 97% improvement compared to the best-performing benchmark under the same sensing budget. We further demonstrate that increasing spatial diversity through additional antennas and subnetworks enhances sensing robustness, especially in fading channels. Finally, we quantify the sensing-communication trade-off, showing that reducing sensing iterations and the number of sensing subnetworks improves throughput at the cost of reduced localization precision.

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arXiv2025

Wireless Memory Approximation for Energy-efficient Task-specific IoT Data Retrieval

The use of Dynamic Random Access Memory (DRAM) for storing Machine Learning (ML) models plays a critical role in accelerating ML inference tasks in the next generation of communication systems. However, periodic refreshment of DRAM results in wasteful energy consumption during standby periods, which is significant for resource-constrained Internet of Things (IoT) devices. To solve this problem, this work advocates two novel approaches: 1) wireless memory activation and 2) wireless memory approximation. These enable the wireless devices to efficiently manage the available memory by considering the timing aspects and relevance of ML model usage; hence, reducing the overall energy consumption. Numerical results show that our proposed scheme can realize smaller energy consumption than the always-on approach while satisfying the retrieval accuracy constraint.

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arXiv2025

Leveraging Generative AI for large-scale prediction-based networking

The traditional role of the network layer is to create an end-to-end route, through which the intermediate nodes replicate and forward the packets towards the destination. This role can be radically redefined by exploiting the power of Generative AI (GenAI) to pivot towards a prediction-based network layer, which addresses the problems of throughput limits and uncontrollable latency. In the context of real-time delivery of image content, the use of GenAI-aided network nodes has been shown to improve the flow arriving at the destination by more than 100%. However, to successfully exploit GenAI nodes and achieve such transition, we must provide solutions for the problems which arise as we scale the networks to include large amounts of users and multiple data modalities other than images. We present three directions that play a significant role in enabling the use of GenAI as a network layer tool at a large scale. In terms of design, we emphasize the need for initialization protocols to select the prompt size efficiently. Next, we consider the use case of GenAI as a tool to ensure timely delivery of data, as well as an alternative to traditional TCP congestion control algorithms.

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arXiv2025

1Q: First-Generation Wireless Systems Integrating Classical and Quantum Communication

We introduce the concept of 1Q, the first wireless generation of integrated classical and quantum communication. 1Q features quantum base stations (QBSs) that support entanglement distribution via free-space optical links alongside traditional radio communications. Key new components include quantum cells, quantum user equipment (QUEs), and hybrid resource allocation spanning classical time-frequency and quantum entanglement domains. Several application scenarios are discussed and illustrated through system design requirements for quantum key distribution, blind quantum computing, and distributed quantum sensing. A range of unique quantum constraints are identified, including decoherence timing, fidelity requirements, and the interplay between quantum and classical error probabilities. Protocol adaptations extend cellular connection management to incorporate entanglement generation, distribution, and handover procedures, expanding the Quantum Internet to the cellular wireless.

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arXiv2025

To Share, or Not to Share: A Study on GEO-LEO Systems for IoT Services with Random Access

The increasing number of satellite deployments, both in the low and geostationary Earth orbit exacerbates the already ongoing scarcity of wireless resources when targeting ubiquitous connectivity. For the aim of supporting a massive number of IoT devices characterized by bursty traffic and modern variants of random access, we pose the following question: Should competing satellite operators share spectrum resources or is an exclusive allocation preferable? This question is addressed by devising a communication model for two operators which serve overlapping coverage areas with independent IoT services. Analytical approximations, validated by Monte Carlo simulations, reveal that spectrum sharing can yield significant throughput gains for both operators under certain conditions tied to the relative serviced user populations and coding rates in use. These gains are sensitive also to the system parameters and may not always render the spectral coexistence mutually advantageous. Our model captures basic trade-offs in uplink spectrum sharing and provides novel actionable insights for the design and regulation of future 6G non-terrestrial networks.

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arXiv2025

Coexistence of Radar and Communication with Rate-Splitting Wireless Access

Future wireless networks are envisioned to facilitate the seamless coexistence of communication and sensing functionalities, thereby enabling the much-touted integrated sensing and communication (ISAC) paradigm. A key challenge in ISAC is managing inter-functionality interference while maintaining a balanced performance trade-off. In this work, we propose a rate-splitting (RS)-inspired approach to address this challenge in an uplink ISAC scenario, where a base station (BS) serves an uplink communication user while detecting a radar target. We derive inner bounds on the ergodic data information rate for the communication user and the ergodic radar estimation information rate for the sensing target. A closed-form solution is also derived for the optimal power split in RS that maximizes the communication user's performance. Compared to orthogonal multiple access (OMA)- and non-orthogonal multiple access (NOMA)-inspired approaches, the proposed approach achieves a more favorable sensing-communication trade-off by virtue of the decoding order flexibility introduced through splitting the communication message. Notably, this is the first work to employ an RS-inspired strategy as a general framework for non-orthogonal coexistence of sensing and communication, extending its applicability beyond traditional digital-only settings.

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arXiv2025

Saving Energy with Relaxed Latency Constraints: A Study on Data Compression and Communication

With the advent of edge computing, data generated by end devices can be pre-processed before transmission, possibly saving transmission time and energy. On the other hand, data processing itself incurs latency and energy consumption, depending on the complexity of the computing operations and the speed of the processor. The energy-latency-reliability profile resulting from the concatenation of pre-processing operations (specifically, data compression) and data transmission is particularly relevant in wireless communication services, whose requirements may change dramatically with the application domain. In this paper, we study this multi-dimensional optimization problem, introducing a simple model to investigate the tradeoff among end-to-end latency, reliability, and energy consumption when considering compression and communication operations in a constrained wireless device. We then study the Pareto fronts of the energy-latency trade-off, considering data compression ratio and device processing speed as key design variables. Our results show that the energy costs grows exponentially with the reduction of the end-to-end latency, so that considerable energy saving can be obtained by slightly relaxing the latency requirements of applications. These findings challenge conventional rigid communication latency targets, advocating instead for application-specific end-to-end latency budgets that account for computational and transmission overhead.

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arXiv2025

Energy Management and Wake-up for IoT Networks Powered by Energy Harvesting

The rapid growth of the Internet of Things (IoT) presents sustainability challenges such as increased maintenance requirements and overall higher energy consumption. This motivates self-sustainable IoT ecosystems based on Energy Harvesting (EH). This paper treats IoT deployments in which IoT devices (IoTDs) rely solely on EH to sense and transmit information about events/alarms to a base station (BS). The objective is to effectively manage the duty cycling of the IoTDs to prolong battery life and maximize the relevant data sent to the BS. The BS can also wake up specific IoTDs if extra information about an event is needed upon initial detection. We propose a K-nearest neighbors (KNN)-based duty cycling management to optimize energy efficiency and detection accuracy by considering spatial correlations among IoTDs' activity and their EH process. We evaluate machine learning approaches, including reinforcement learning (RL) and decision transformers (DT), to maximize information captured from events while managing energy consumption. Significant improvements over the state-ofthe-art approaches are obtained in terms of energy saving by all three proposals, KNN, RL, and DT. Moreover, the RL-based solution approaches the performance of a genie-aided benchmark as the number of IoTDs increases.

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arXiv2025

Dual-Mode Wireless Devices for Adaptive Pull and Push-Based Communication

This paper introduces a dual-mode communication framework for wireless devices that integrates query-driven (pull) and event-driven (push) transmissions within a unified time-frame structure. Devices typically respond to information requests in pull mode, but if an anomaly is detected, they preempt the regular response to report the critical condition. Additionally, push-based communication is used to proactively send critical data without waiting for a request. This adaptive approach ensures timely, context-aware, and efficient data delivery across different network conditions. To achieve high energy efficiency, we incorporate a wake-up radio mechanism and we design a tailored medium access control (MAC) protocol that supports data traffic belonging to the different communication classes. A comprehensive system-level analysis is conducted, accounting for the wake-up control operation and evaluating three key performance metrics: the success probability of anomaly reports (push traffic), the success probability of query responses (pull traffic) and the total energy consumption. Numerical results characterize the system's behavior and highlight the inherent trade-off between push and pull success probabilities as a function of allocated communication resources. Our analysis demonstrates that the proposed approach achieves up to a 42% reduction in energy consumption per served packet compared to traditional approaches, while maintaining reliable support for both communication paradigms.

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