arXiv subjects

David González G

Publications and source records attributed to David González G.

5 recordsLinked to original sources

arXiv2026

Data-aided Channel Estimation and Sensing With Sparse Bayesian Learning for AFDM-ISAC System

Affine frequency division multiplexing (AFDM) has emerged as a promising waveform for next-generation integrated sensing and communication (ISAC) systems. However, it becomes challenging to improve spectral efficiency while simultaneously obtaining accurate channel and sensing-related parameters, particularly in doubly-dispersive channels with fractional delays and fractional Doppler shifts. To tackle this challenge, by formulating the channel estimation task as a multiple measurement vector (MMV) off-grid sparse recovery problem, we propose a data-aided grid-evolution sparse Bayesian learning (D-GESBL) scheme for channel estimation and sensing under a superimposed pilot framework. Specifically, we develop an efficient data-aided iterative receiver, in which reliably decoded data symbols are fed back as additional pseudo-pilot information to assist channel estimation and sensing. To mitigate off-grid mismatch and improve the overall estimation accuracy, we develop a grid evolution procedure that iteratively adjusts the virtual grids in the discrete affine Fourier (DAF) domain according to the estimated off-grid components. Furthermore, by integrating the generalized approximate message passing (GAMP) algorithm into the proposed SBL framework, we also develop a low-complexity data-aided GAMP-based grid-evolution SBL (D-GAMP-GESBL) algorithm. Finally, the numerical results validate the effectiveness of our proposed schemes and demonstrate their superiority over existing state-of-the-art methods.

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arXiv2026

Full Duplex ISAC with Cluster Ray Targets: Parameter Estimation and Beamforming

This work studies a full-duplex integrated sensing and communication (ISAC) resolution framework for spatially distributed systems. Conventional high-resolution methods, such as MUSIC, fail to localize distributed targets because the signal subspace is full rank, even in the single-distributed-target setting. In an effort to resolve this, we propose a two-stage estimator, which successfully resolve multiple distributed targets and outperforms several baseline schemes without incurring any additional computational complexity. Our first-stage estimator uses the Fast Fourier transform to estimate the coarse spectrum, while in the second stage, we apply the Gauss-Newton method to fine-tune the angular estimates. Apart from this, we also propose an optimization framework for designing an adaptive beamformer capable of synthesizing both wide and directed beams to cover the full extent of the targets while also fulfilling data rate requirements of multiple users. The beamformer also meets the data-rate requirements of multiple users, maintaining quality of service. Simulation results demonstrate a threefold improvement in spread estimation under low signal-to-noise ratio (SNR) conditions and a twofold improvement for low-spread targets.

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arXiv2026

Quadrature Over-the-Air-Computing for Multimodal Dual-Stream Signal Processing

We propose a novel quadrature over-the-air computing (Q-OTAC) framework that enables the simultaneously computation of two independent functions and/or data stream within a single transmission. In contrast to conventional OTAC schemes, where a single function is computed by treating each complex signal as a single component, the proposed Q-OTAC exploits both in-phase and quadrature (IQ) components of a complex signal, encoding two distinct functions and/or data streams at the edge devices (EDs) and employing a novel low-complexity IQ-decoupled combiner at the access point (AP) to independently recover each stream, which effectively doubles the computation rate. A key strength of this framework lies in its simplicity and broad compatibility: the extension into the quadrature domain is conceptually straightforward, yet remakably powerful, allowing seamless integration into existing OTAC techniques. Simulation results validate the effectiveness of this approach, including the first demonstration of dual-function aggregation (e.g., parallel summation and product), highlighting the potential of Q-OTAC for enabling multi-modal and high-efficiency beyond fifth generation (B5G) applications.

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arXiv2025

A Flexible Design Framework for Integrated Communication and Computing Receivers

We propose a framework to design integrated communication and computing (ICC) receivers capable of simultaneously detecting data symbols and performing over-the-air computing (AirComp) in a manner that: a) is systematically generalizable to any nomographic function, b) scales to a massive number of user equipments (UEs) and edge devices (EDs), c) supports the computation of multiple independent functions (streams), and d) operates in a multi-access fashion whereby each transmitter can choose to transmit either data symbols, computing signals or both. For the sake of illustration, we design the proposed multi-stream and multi-access method under an uplink setting, where multiple single-antenna UEs/EDs simultaneously transmit data and computing signals to a single multiple-antenna base station (BS)/access point (AP). Under the communication functionality, the receiver aims to detect all independent communication symbols while treating the computing streams as aggregate interference which it seeks to mitigate; and conversely, under the computing functionality, to minimize the distortion over the computing streams while minimizing their mutual interference as well as the interference due to data symbols. To that end, the design leverages the Gaussian belief propagation (GaBP) framework relying only on element-wise scalar operations coupled with closed-form combiners purpose-built for the AirComp operation, which allows for its use in massive settings, as demonstrated by simulation results incorporating up to 200 antennas and 300 UEs/EDs. The efficacy of the proposed method under different loading conditions is also evaluated, with the performance of the scheme shown to approach fundamental limiting bounds in the under/fully loaded cases.

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arXiv2024

Tone Reservation-Based PAPR Reduction Using Manifold Optimization for OFDM-ISAC Systems

We consider the peak-to-average power ratio (PAPR) reduction challenge of orthogonal frequency division multiplexing (OFDM) systems utilizing tone reservation (TR) under a sensing-enabling constraint, such that the signals placed in the reserved tones (RTs) can be exploited for Integrated Sensing and Communication (ISAC). To that end, the problem is first cast as an unconstrained manifold optimization problem, and then solved via an iterative projected gradient descent algorithm assisted by an approximation of the infinity norm. Simulation results show that the proposed method, while maintaining a level of PAPR reduction similar to state of the art (SotA), not only has lower computational complexity but also outperforms the alternatives in terms of sensing performance.

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