arXiv subjects

Iván Alexander Morales Sandoval

Publications and source records attributed to Iván Alexander Morales Sandoval.

10 recordsLinked to original sources

arXiv2026

Bistatic Integrated Sensing and Communications with Flexible Intelligent Metasurfaces

We propose a novel doubly-dispersive (DD) multiple-input multiple-output (MIMO) channel model incorporating flexible intelligent metasurfaces (FIMs), suitable for integrated sensing and communications (ISAC) in high-mobility scenarios. We show how the proposed FIM-parameterized DD (FPDD) channel model extends to multicarrier waveforms known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM). Leveraging this model, we formulate an achievable rate maxi-mization problem with a sensing constraint for all waveforms and solve it via gradient ascent with closed-form gradients. Numerical results indicate that FIM technology significantly impacts the achievable rate, with careful parametrization essential for strong ISAC performance across all waveforms.

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arXiv2026

Low-complexity Leakage Minimization Beamforming for Large-scale Multi-user Cell-Free Massive MIMO

We propose a low-complexity beamforming (BF) scheme for secrecy-rate maximization in multi-user (MU) cell-free massive multiple-input multiple-output (CF-mMIMO) systems, where legitimate users may act as non-colluding eavesdroppers of one another. To this end, we formulate an information leakage minimization problem and cast it into a tractable difference-of-convex algorithmic (DCA) form by leveraging fractional programming (FP). The resulting non-convex problem is solved through a concave-convex procedure (CCP)-based beamformer update, and an additional row-wise coordinate descent method (CDM) implementation is introduced to avoid explicit matrix inversion in the dominant linear-solve step. Additionally, we consider both direct transmit (TX)-BF and beyond-diagonal reconfigurable intelligent surface (BD-RIS)-assisted operation by defining an equivalent channel between each access point and user that combines the direct and reflective intelligent surface (RIS)-assisted propagation components. Simulation results show that the proposed secrecy-enhancement via leakage minimization (SecLM)-BF framework achieves secrecy and sum-rate performance close to state-of-the-art (SotA) semidefinite programming (SDP)-based benchmarks, as well as FP-based benchmarks, while providing a scalable inversion-free implementation for large-scale secure CF-mMIMO deployments.

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arXiv2026

Fractional Programming and Manifold Optimization for Reciprocal BD-RIS Scattering Matrix Design

We investigate the problem of maximizing the sum-rate performance of a beyond-diagonal reconfigurable intelligent surface (BD-RIS)-aided multi-user (MU)-multiple-input single-output (MISO) system using fractional programming (FP) techniques. More specifically, we leverage the Lagrangian Dual Transform (LDT) and Quadratic Transform (QT) to derive an equivalent objective function which is then solved iteratively via a manifold optimization framework. It is shown that these techniques reduce the complexity of the optimization problem for the scattering matrix solution, while also providing notable performance gains compared to state-of-the-art (SotA) methods under the same system conditions. Simulation results confirm the effectiveness of the proposed method in improving sum-rate performance.

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arXiv2026

Reciprocal Beyond Diagonal Reconfigurable Intelligent Surface: Distributed Scattering Matrix Design and MIMO Beamforming via Fractional Programming and Manifold Optimization

We consider the optimization of beyond diagonal reconfigurable intelligent surface (BD-RIS)-aided multi-user (MU) cell-free (CF)-massive multiple-input multiple-output (mMIMO) systems, where the propagation environment design achieved scattering matrix optimization is complemented by developing an efficient base station (BS) beamforming (BF) scheme that effectively exploits the latter ``engineered'' channel. In particular, we describe a fractional programming (FP) method, which based on the equivalent channel incorporating a reciprocal BD-RIS (RBD-RIS) parameterized by existing scattering matrix design methods, yielding the correspondingly optimized multiple-input multiple-output (MIMO) BF weights. The proposed approach decomposes the transmit (TX) beamformer into multiple sum-rate maximization (SRM) sub-beamformers, each satisfying an independent power-constraint, such that distributed MIMO-BF scenarios can be optimally handled. Although the proposed SRM-MIMO-BF framework is independent of the specific scattering matrix design, extending the BD-RIS-aided system model to the CF-mMIMO setting requires the design of a corresponding beamforming matrix. In this context, this work investigates the impact of beamforming in reconfigurable intelligent surface (RIS)-aided systems. Simulation results demonstrate that the proposed method for designing the MIMO-BF weights, when combined with the previously developed design of reciprocal BD-RIS (RBD-RIS) scattering matrices, outperforms existing BD-RIS-aided state-of-the-art (SotA) schemes employing existing MIMO-BF techniques, indicating that the whole contribution is more than the sum of the parts.

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arXiv2026

Reciprocal Beyond-Diagonal Reconfigurable Intelligent Surface (BD-RIS): Scattering Matrix Design via Manifold Optimization

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) are emerging as a transformative technology in wireless communications, enabling enhanced performance and quality of service (QoS) of wireless systems in harsh urban environments due to their relatively low cost and advanced signal processing capabilities. Generally, BD-RIS systems are employed to improve robustness, increase achievable rates, and enhance energy efficiency of wireless systems in both direct and indirect ways. The direct way is to produce a favorable propagation environment via the design of optimized scattering matrices, while the indirect way is to reap additional improvements via the design of multiple-input multiple-output (MIMO) beamformers that further exploit the latter "engineered" medium. In this article, the problem of sum-rate maximization via BD-RIS is examined, with a focus on feasibility, namely low-complexity physical implementation, by enforcing reciprocity in the BD-RIS design in a manner that adheres to the geometry of the manifold of symmetric matrices. To that end, the sum-rate objective is transformed into a quadratic function via fractional programming (FP), augmented via the also quadratic reciprocity constraint in the form of a regularization term, while the unitary constraint is dealt with via a manifold optimization framework. Simulation results demonstrate the effectiveness of the proposed method in outperforming current state-of-the-art (SotA) approaches in terms of sum-rate maximization.

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arXiv2025

Optimized Sparse Network Coverage via L1-norm Minimization

The selection of nodes that can serve as cluster heads, local sinks and gateways is a critical challenge in distributed sensor and communication networks. This paper presents a novel framework for identifying a minimal set of nexus nodes to ensure full network coverage while minimizing cost. By formulating the problem as a convex relaxation of the NP-hard set cover problem, we integrate the graph theoretic centrality measures of node degree and betweenness centrality into a cost function optimized via a relaxed L1-norm minimization. The proposed approach is applicable to static and dynamic network scenarios and does not require location or distance estimation. Through simulations across various graph models and dynamic conditions, it is shown that the method achieves faster execution times (lower complexity) and competitive sparsity compared to classical greedy and genetic algorithms (GA), offering a robust, distributed, and cost-efficient node selection solution.

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arXiv2025

Flexible Intelligent Metasurfaces in High-Mobility MIMO Integrated Sensing and Communications

We propose a novel doubly-dispersive (DD) multiple-input multiple-output (MIMO) channel model incorporating flexible intelligent metasurfaces (FIMs), which is suitable for integrated sensing and communications (ISAC) in high-mobility scenarios. We then discuss how the proposed FIM-parameterized DD (FPDD) channel model can be applied in a logical manner to ISAC waveforms that are known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM). Leveraging the proposed model, we formulate an achievable rate maximization problem with a strong sensing constraint for all the aforementioned waveforms, which we then solve via a gradient ascent algorithm with closed-form gradients presented as a bonus. Our numerical results indicate that the achievable rate is significantly impacted by the emerging FIM technology with careful parametrization essential in obtaining strong ISAC performance across all waveforms suitable to mitigating the effects of DD channels.

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arXiv2025

Metasurfaces-Integrated Doubly-Dispersive MIMO: Channel Modeling and Optimization

The doubly-dispersive (DD) channel structure has played a pivotal role in wireless communications, particularly in high-mobility scenarios and integrated sensing and communications (ISAC), due to its ability to capture the key fading effects experienced by a transmitted signal as it propagates through a dynamic medium. However, extending the DD framework to multiple-input multiple-output (MIMO) systems, especially in environments artificially enhanced by reconfigurable intelligent surfaces (RISs) and stacked intelligent metasurfaces (SIM), remains a challenging open problem. In this chapter, a novel metasurfaces-parametrized DD (MPDD) channel model that integrates an arbitrary number of RISs, while also incorporating SIM at both the transmitter and receiver is introduced. Next, the application of this model to some key waveforms optimized for DD environments -- namely orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM) -- is discussed. Finally, the programmability of the proposed model is highlighted through an illustrative application, demonstrating its potential for enhancing waveform performance in SIM-assisted wireless systems.

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arXiv2025

Doubly-Dispersive MIMO Channels with Stacked Intelligent Metasurfaces: Modeling, Parametrization, and Receiver Design

Introduced with the advent of statistical wireless channel models for high mobility communications and having a profound role in communication-centric (CC) integrated sensing and communications (ISAC), the doubly-dispersive (DD) channel structure has long been heralded as a useful tool enabling the capture of the most important fading effects undergone by an arbitrary time-domain transmit signal propagating through some medium. However, the incorporation of this model into multiple-input multiple-output (MIMO) system setups, relying on the recent paradigm-shifting transceiver architecture based on stacked intelligent metasurfaces (SIM), in an environment with reconfigurable intelligent surfaces (RISs) remains an open problem due to the many intricate details that have to be accounted for. In this paper, we fill this gap by introducing a novel DD MIMO channel model that incorporates an arbitrary number of RISs in the ambient, as well as SIMs equipping both the transmitter and receiver. We then discuss how the proposed metasurfaces-parametrized DD (MPDD) channel model can be seamlessly applied to waveforms that are known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM), with each having their own inherent advantages and disadvantages. An illustrative application of the programmable functionality of the proposed model is finally presented to showcase its potential for boosting the performance of the aforementioned waveforms. Our numerical results indicate that the design of waveforms suitable to mitigating the effects of DD channels is significantly impacted by the emerging SIM technology.

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arXiv2025

Parametrized Stacked Intelligent Metasurfaces for Bistatic Integrated Sensing and Communications

We consider stacked intelligent metasurfaces (SIMs) as a tool to improve the performance of bistatic integrated sensing and communications (ISAC) schemes. To that end, we optimize the SIMs and design a radar parameter estimation (RPE) scheme aimed at enhancing radar sensing capabilities as well as communication performance under ISAC-enabling waveforms known to perform well in doubly-dispersive (DD) channels. The SIM optimization is done via a min-max problem formulation solved via steepest ascent with closed-form gradients, while the RPE is carried out via a compressed sensing-based probabilistic data association (PDA) algorithm. Our numerical results indicate that the design of waveforms suitable to mitigating the effects of DD channels is significantly impacted by the emerging SIM technology.

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