arXiv subjects

Daniel Benevides da Costa

Publications and source records attributed to Daniel Benevides da Costa.

At least 19 recordsLinked to original sources

arXiv2026

PRISM: Decision-Centric Predictive Sensing for Cognitive Digital Twins in 6G

Integrated sensing and communication (ISAC) and Digital Twin (DT) technology have emerged as complementary for future wireless networks that require autonomous operations involving continuous interaction between physical and digital worlds. However, existing DT-assisted ISAC frameworks sense continuously and indiscriminately while optimizing only a single task, leaving little room for persistent, multi-domain knowledge or proactive sensing control. This article proposes a Predictive, Reasoning-driven, Intelligent Sensing Module (PRISM) engine that transforms the DT from a passive, domain-specific optimizer into a persistent, network-wide reasoning system. PRISM enables decision-centric predictive perception, proactively directing sensing toward anticipated decisions needs rather than following fixed sensing schedules. Using an illustrative extremely large multiple-input multiple-output (XL-MIMO) deployment scenario with a mixed eMBB, URLLC, and mMTC device population, we show how this principle benefits visibility-region sensing for channel acquisition and supports slice-aware operation. Preliminary simulations, including this deployment scenario and the resulting knowledge error, overhead, and latency results, confirm that this decision-centric approach substantially reduces sensing overhead while preserving decision reliability and latency, supporting the proposed architecture as a practical step toward self-aware, autonomously orchestrated 6G networks.

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arXiv2026

GenAI-Enhanced Digital Twins for Predictive Interference Management in Ultra-Dense Networks

Ultra-dense indoor next-generation networks suffer severe interference from mobility-induced blockages and localized multi-user hotspots that conventional digital twins~(DTs) cannot anticipate. We propose a generative AI~(GenAI)-enhanced DT framework employing a conditional generative adversarial network~(cGAN) with a spatio-temporal generator and PatchGAN discriminator for proactive rare-event channel synthesis. A worst-case zero-forcing~(WC-ZF) beamformer driven by Monte Carlo synthetic trajectories realizes distributionally robust precoding, with control-channel overhead bounded to $\approx$2.1\,kB per 10\,ms slot. Sionna-based simulations confirm a 5--8\,dB median signal-to-interference-plus-noise-ratio (SINR) gain, 60--70\% packet-loss reduction, and 60--85\% closure of the perfect channel state information (CSI) oracle gap within a 2.8--4.1\,ms inference overhead.

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arXiv2026

A Knowledge-Centric Communication For Autonomous Cislunar Networks

Future lunar infrastructure requires communication as a persistent service rather than a mission specific capability. Communication, digital twin, and artificial intelligence (AI) research have advanced autonomy independently while largely assuming that system state can eventually be reconciled with ground truth. Cislunar communication violates this assumption because propagation delay is fundamentally limited by the speed of light, visibility is governed by orbital geometry, and autonomous decisions often precede confirming observations. Here we introduce a knowledge-centric communication framework in which a digital twin continuously integrates delayed observations, communication physics, learned models, uncertainty quantification, and mission objectives into an evolving Operational Knowledge State. To quantify what is known, how uncertain it remains, and how current it is, we define two quantities over the fused Bayesian belief maintained for each communication link: \emph{Knowledge Entropy}, the differential entropy of the posterior belief, and \emph{Knowledge Freshness}, which generalizes Age of Information (AoI) from a single information stream to the temporal validity of the fused Operational Knowledge State. We prove that distributed knowledge fusion systematically improves operational knowledge by reducing uncertainty and demonstrate its importance for mission success through simulations and comparisons with flight data from the Longjiang-2 lunar micro-satellite.

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arXiv2026

Redefining Digital Twins as Predictive Decision Engines for AI-Native Wireless Networks

Future artificial intelligence (AI)-native 6G networks require wireless systems that move beyond reactive optimization toward autonomous, predictive, and continuously adaptive intelligence. Most existing digital twin (DT) frameworks use AI only to improve modeling, data generation, or isolated optimization tasks, leaving the DT itself in a passive, synchronization-only role. This article redefines the DT as a predictive decision engine, which is model-agnostic framework that fuses continuous synchronization, predictive reasoning, autonomous decision-making, and closed-loop wireless control into a single system. Reasoning over synchronized network state, the framework anticipates future conditions and acts autonomously before performance degrades, using generative adversarial networks (GANs), large language models (LLMs), diffusion models, or other learning-based engines interchangeably as the underlying predictor. An illustrative unmanned aerial vehicle (UAV)-assisted non-terrestrial network (NTN) deployment, using a lightweight conditional generative adversarial network (cGAN) as one illustrative predictor, demonstrates the practical effectiveness of the proposed framework by achieving considerable energy savings over reactive baselines while maintaining reliable quality of service (QoS) under highly dynamic conditions.

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arXiv2026

Cognitive Digital Twins for Self-Aware Channel Estimation

Artificial intelligence (AI) and machine learning (ML)-based channel estimators silently degrade when propagation conditions drift from their training distributions. This letter proposes a model-agnostic cognitive digital twin (CDT) framework that combines a variational autoencoder (VAE) with latent activation monitoring to detect distribution drift and autonomously execute \textsc{continue}, \textsc{update}, or \textsc{retire} lifecycle actions without requiring ground-truth channel knowledge. The proposed framework is fully compatible with the AI-native lifecycle management envisioned in 3rd Generation Partnership Project (3GPP). Simulations over various channels demonstrate accurate drift detection and robust channel estimation, consistently outperforming conventional offline-trained deep learning estimators under moderate and severe channel drift.

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arXiv2026

A Generative AI-Enhanced Digital Twin Framework for Proactive Interference Management in Hybrid Near/Far-Field Wireless Systems

The applications of Digital Twins (DT) and Generative AI (GenAI) have demonstrated their capabilities in modeling and learning-based wireless communications. However, their joint potential for proactive wireless system design remains largely underexplored, particularly in extremely large-scale multiple-input multiple-output (XL-MIMO) networks, characterized by hybrid near-field (NF) and far-field (FF) propagation regimes. In this work, we propose an integrated GenAI-enhanced DT framework for proactive interference management in dynamic indoor scenarios. The DT constructs a high-resolution, site-specific virtual replica of the deployment environment, understanding where and why blockage occurs within a realistic 3D representation of the indoor space. Integration of the GenAI module further assists the framework in anticipating and proactively suppressing blockage, rather than reacting after the disruption occurs. Extensive simulation results based on Sionna ray-tracing datasets demonstrate that the proposed framework achieves significant improvements in interference suppression, signal-to-interference-plus-noise ratio (SINR), and outage probability compared to conventional reactive schemes and purely deterministic DT-based approaches.

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arXiv2025

Symbol Detection in Multi-channel Multi-tag Ambient Backscatter Communication Under IQ Imbalance

Ambient backscatter communication (AmBC) offers low-cost and low-power connectivity for Internet of Things (IoT), where a backscatter tag (BT) modulates incident signals transmitted by an ambient radio frequency (RF) source and reflects them to its associated AmBC receiver. In multi-channel multi-tag AmBC, one of major challenges from the aspect of symbol detection is the image channel crosstalk, which is induced by the inevitable in-phase/quadrature (IQ) imbalance. To address this issue, in this paper, we study symbol detection in multi-channel multi-tag AmBC under IQ imbalance. Considering the differential encoding scheme at the BTs, we propose a novel symbol detection model that incorporates IQ imbalance parameters, the presence or absence of both the incident signal and the backscattered signal of the image channel. On this basis, considering an energy difference detector at the AmBC receiver, we derive the closed-form expressions for the bit error rate (BER) as well as the near-optimal detection threshold to minimize BER. However, calculating the near-optimal detection threshold requires prior information, such as the IQ imbalance parameters, the presence probability of the incident signal of the image channel and the backscattered signal of the image channel, the signal power of the ambient RF source, and the noise power, which are typically unknown to the AmBC receiver in practice. To eliminate the need for the prior information, we propose a threshold estimation method using the received samples. Numerical results indicate that under IQ imbalance, directly using the existing method leads to a significant degradation in BER performance. However, this degradation can be effectively mitigated by our derived detection threshold.

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arXiv2025

Movable Antenna-Enhanced Secure Communication: Opportunities, Challenges, and Solutions

The broadcast nature of wireless communication renders it inherently vulnerable to security threats such as jamming and eavesdropping. While traditional array beamforming techniques help to mitigate these threats, they usually incur high hardware and processing costs, particularly in large-scale arrays with fixed-position antennas (FPAs). In contrast, movable antenna (MA) arrays can fully exploit the channel variation in spatial regions by enabling flexible antenna movement, which has emerged as a promising technology for secure communications. This article provides a magazine-type overview of MA-aided secure communications. Specifically, we first illuminate the promising application scenarios for MA-enhanced secure communication systems. Then, we examine the security advantages of MAs over conventional FPA systems, fundamentally stemming from their ability to adjust channel correlations between legitimate users, eavesdroppers, and jammers. Furthermore, we discuss important technical challenges and their potential solutions related to MA hardware architecture, channel acquisition, and antenna position optimization to realize secure transmissions. Finally, several promising directions for MA-aided secure communications are presented to inspire future research.

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arXiv2025

Beamfocusing and Power Allocation for AN-Based PLS in Multiuser XL-MIMO with Multiple Eavesdroppers

This paper investigates the downlink (DL) physical layer security (PLS) in a near-field (NF) extra-large multiple-input multiple-output MIMO (XL-MIMO) system. To enhance the secrecy rate (SR), null-space artificial noise (AN) is transmitted alongside the confidential message, ensuring orthogonality with legitimate user equipment (LUE) channels. The objective is to maximize the minimum SR by optimizing the NF beamfocusing matrix and power allocation between the signal and AN, considering various channel state information (CSI) conditions and transmit power constraints. The proposed approach uses successive convex approximation (SCA) for beamfocusing optimization and golden section search (GSS) for power allocation. The following open questions are addressed: (i) Can AN transmission further enhance SR for multiple LUEs in the presence of multiple eavesdropping user equipment (EUEs)? (ii) Can null-space AN transmission achieve attractive SR performance even without CSI availability for EUEs? Both questions are affirmatively answered and explored in detail, with an algorithm presented for joint beamfocusing design and AN-aided power allocation. The proposed method outperforms state-of-the-art approaches that either omit AN transmission or rely on maximal-ratio transmission (MRT) for beamfocusing.

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arXiv2024

Movable Antenna Aided NOMA: Joint Antenna Positioning, Precoding, and Decoding Design

This paper investigates movable antenna (MA) aided non-orthogonal multiple access (NOMA) for multi-user downlink communication, where the base station (BS) is equipped with a fixed-position antenna (FPA) array to serve multiple MA-enabled users. An optimization problem is formulated to maximize the minimum achievable rate among all the users by jointly optimizing the MA positioning of each user, the precoding matrix at the BS, and the successive interference cancellation (SIC) decoding indicator matrix at the users, subject to a set of constraints including the limited movement area of the MAs, the maximum transmit power of the BS, and the SIC decoding condition. To solve this non-convex problem, we propose a two-loop iterative optimization algorithm that combines the hippopotamus optimization (HO) method with the alternating optimization (AO) method to obtain a suboptimal solution efficiently. Specifically, in the inner loop, the complex-valued precoding matrix and the binary decoding indicator matrix are optimized alternatively by the successive convex approximation (SCA) technique with customized greedy search to maximize the minimum achievable rate for the given positions of the MAs. In the outer loop, each user's antenna position is updated using the HO algorithm, following a novel nature-inspired intelligent optimization framework. Simulation results show that the proposed algorithms can effectively avoid local optimum for highly coupled variables and significantly improve the rate performance of the NOMA system compared to the conventional FPA system as well as other benchmark schemes.

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arXiv2024

Unlocking FAS-RIS Security Analysis with Block-Correlation Model

In this letter, we investigate the security of fluid antenna system (FAS)-reconfigurable intelligent surfaces (RIS) communication systems. The base station (BS) employs a single fixed-position antenna, while both the legitimate receiver and the eavesdropper are equipped with fluid antennas. By utilizing the block-correlation model and the central limit theorem (CLT), we derive approximate expressions for the average secrecy capacity and secrecy outage probability (SOP). Our analysis, validated by simulation results, demonstrates the effectiveness of the block-correlation model in accurately assessing the security performance. Moreover, simulation results reveal that FAS-RIS system significantly outperforms other systems in terms of security, further underscoring its potential in secure communication applications.

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arXiv2024

Sensing-aided Near-Field Secure Communications with Mobile Eavesdroppers

The additional degree of freedom (DoF) in the distance domain of near-field communication offers new opportunities for physical layer security (PLS) design. However, existing works mainly consider static eavesdroppers, and the related study with mobile eavesdroppers is still in its infancy due to the difficulty in obtaining the channel state information (CSI) of the eavesdropper. To this end, we propose to leverage the sensing capability of integrated sensing and communication (ISAC) systems to assist PLS design. To comprehensively study the dynamic behaviors of the system, we propose a Pareto optimization framework, where a multi-objective optimization problem (MOOP) is formulated to simultaneously optimize three key performance metrics: power consumption, number of securely served users, and tracking performance, while guaranteeing the achievable rate of the users with a given leakage rate constraint. A globally optimal design based on the generalized Benders decomposition (GBD) method is proposed to achieve the Pareto optimal solutions. To reduce the computational complexity, we further design a low-complexity algorithm based on zero-forcing (ZF) beamforming and successive convex approximation (SCA). Simulation results validate the effectiveness of the proposed algorithms and reveal the intrinsic trade-offs between the three performance metrics. It is observed that near-field communication offers a favorable beam diffraction effect for PLS, where the energy of the information signal is nulled around the eavesdropper and focused on the users.

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arXiv2024

The α-Lomax Distribution: A Compound Channel Model

In this paper, we propose the α-Lomax distribution as a new compound fading channel model. This new distribution generalizes the recently introduced Lomax fading channel model. It is worth noting that the Lomax distribution is a decreasing function, while the α-Lomax is a unimodal function, offering greater flexibility in modeling wireless fading channels. In particular, we derive closed-form expressions for the probability density function and cumulative distribution function for the instantaneous signal-to-noise ratio (SNR). Additionally, we provide closed-form expressions for several fundamental performance metrics, including outage probability, average bit error rate, and channel capacity. Furthermore, we derive closed-form expression for the average block-length error rate in short-packet communications. Moreover, we fit the PDF of the proposed channel model to empirical data obtained from a device-to-device communication system. We also offer simple and accurate approximations for these expressions in the high SNR regime.

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arXiv2024

A Novel Paradigm Shift for Next-Generation: Symbiotic Backscatter Rate-Splitting Multiple Access Systems

Next-generation wireless networks are projected to empower a broad range of Internet-of-things (IoT) applications and services with extreme data rates, posing new challenges in delivering large-scale connectivity at a low cost to current communication paradigms. Rate-splitting multiple access (RSMA) is one of the most spotlight nominees, conceived to address spectrum scarcity while reaching massive connectivity. Meanwhile, symbiotic communication is said to be an inexpensive way to realize future IoT on a large scale. To reach the goal of spectrum efficiency improvement and low energy consumption, we merge these advances by means of introducing a novel paradigm shift, called symbiotic backscatter RSMA, for the next generation. Specifically, we first establish the way to operate the symbiotic system to assist the readers in apprehending the proposed paradigm, then guide detailed design in beamforming weights with four potential gain-control (GC) strategies for enhancing symbiotic communication, and finally provide an information-theoretic framework using a new metric, called symbiotic outage probability (SOP) to characterize the proposed system performance. Through numerical result experiments, we show that the developed framework can accurately predict the actual SOP and the efficacy of the proposed GC strategies in improving the SOP performance.

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arXiv2024

Active RIS-Aided Massive MIMO With Imperfect CSI and Phase Noise

Active reconfigurable intelligent surface (RIS) has attracted significant attention as a recently proposed RIS architecture. Owing to its capability to amplify the incident signals, active RIS can mitigate the multiplicative fading effect inherent in the passive RIS-aided system. In this paper, we consider an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system in the presence of phase noise at the active RIS. Specifically, we employ a two-timescale scheme, where the beamforming at the base station (BS) is adjusted based on the instantaneous aggregated channel state information (CSI) and the statistical CSI serves as the basis for designing the phase shifts at the active RIS, so that the feedback overhead and computational complexity can be significantly reduced. The aggregated channel composed of the cascaded and direct channels is estimated by utilizing the linear minimum mean square error (LMMSE) technique. Based on the estimated channel, we derive the analytical closed-form expression of a lower bound of the achievable rate. The power scaling laws in the active RIS-aided system are investigated based on the theoretical expressions. When the transmit power of each user is scaled down by the number of BS antennas M or reflecting elements N, we find that the thermal noise will cause the lower bound of the achievable rate to approach zero, as the number of M or N increases to infinity. Moreover, an optimization approach based on genetic algorithms (GA) is introduced to tackle the phase shift optimization problem. Numerical results reveal that the active RIS can greatly enhance the performance of the considered system under various settings.

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arXiv2023

IRS-Aided Overloaded Multi-Antenna Systems: Joint User Grouping and Resource Allocation

This paper studies an intelligent reflecting surface (IRS)-aided multi-antenna simultaneous wireless information and power transfer (SWIPT) system where an $M$-antenna access point (AP) serves $K$ single-antenna information users (IUs) and $J$ single-antenna energy users (EUs) with the aid of an IRS with phase errors. We explicitly concentrate on overloaded scenarios where $K + J > M$ and $K \geq M$. Our goal is to maximize the minimum throughput among all the IUs by optimizing the allocation of resources (including time, transmit beamforming at the AP, and reflect beamforming at the IRS), while guaranteeing the minimum amount of harvested energy at each EU. Towards this goal, we propose two user grouping (UG) schemes, namely, the non-overlapping UG scheme and the overlapping UG scheme, where the difference lies in whether identical IUs can exist in multiple groups. Different IU groups are served in orthogonal time dimensions, while the IUs in the same group are served simultaneously with all the EUs via spatial multiplexing. The two problems corresponding to the two UG schemes are mixed-integer non-convex optimization problems and difficult to solve optimally. We propose efficient algorithms for these two problems based on the big-M formulation, the penalty method, the block coordinate descent, and the successive convex approximation. Simulation results show that: 1) the non-robust counterparts of the proposed robust designs are unsuitable for practical IRS-aided SWIPT systems with phase errors since the energy harvesting constraints cannot be satisfied; 2) the proposed UG strategies can significantly improve the max-min throughput over the benchmark schemes without UG or adopting random UG; 3) the overlapping UG scheme performs much better than its non-overlapping counterpart when the absolute difference between $K$ and $M$ is small and the EH constraints are not stringent.

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arXiv2023

Adaptive Decoding Mechanisms for UAV-enabled Double-Uplink Coordinated NOMA

In this paper, we propose a novel adaptive decoding mechanism (ADM) for the unmanned aerial vehicle (UAV)-enabled uplink (UL) non-orthogonal multiple access (NOMA) communications. Specifically, considering a harsh UAV environment, where ground-to-ground links are regularly unavailable, the proposed ADM overcomes the challenging problem of conventional UL-NOMA systems whose performance is sensitive to the transmitter's statistical channel state information and the receiver's decoding order. To evaluate the performance of the ADM, we derive closed-form expressions for the system outage probability (OP) and system throughput. In the performance analysis section, we provide novel expressions for practical air-to-ground and ground-to-air channels, while taking into account the practical implementation of imperfect successive interference cancellation (SIC) in UL-NOMA. Moreover, the obtained expression can be adopted to characterize the OP of various systems under a Mixture of Gamma (MG) distribution-based fading channels. Next, we propose a sub-optimal Gradient Descent-based algorithm to obtain the power allocation coefficients that result in maximum throughput with respect to each location on UAV's trajectory. To determine the significance of the proposed ADM in nonstationary environments, we consider the ground users and the UAV to move according to the Random Waypoint Mobility (RWM) and Reference Point Group Mobility (RPGM) models, respectively. Accurate formulas for the distance distributions are also provided. Numerical solutions demonstrate that the ADM-enhanced NOMA not only outperforms Orthogonal Multiple Access (OMA), but also improves the performance of UAV-enabled UL-NOMA even in mobile environments.

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arXiv2022

Dual-Polarized Massive MIMO-RSMA Networks: Tackling Imperfect SIC

The polarization domain provides an extra degree of freedom (DoF) for improving the performance of multiple-input multiple-output (MIMO) systems. This paper takes advantage of this additional DoF to alleviate practical issues of successive interference cancellation (SIC) in rate-splitting multiple access (RSMA) schemes. Specifically, we propose three dual-polarized downlink transmission approaches for a massive MIMO-RSMA network under the effects of polarization interference and residual errors of imperfect SIC. The first approach implements polarization multiplexing for transmitting the users' data messages, which removes the need to execute SIC in the reception. The second approach transmits replicas of users' messages in the two polarizations, which enables users to exploit diversity through the polarization domain. The third approach, in its turn, employs the original SIC-based RSMA technique per polarization, and this allows the BS to transmit two independent superimposed data streams simultaneously. An in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities of the three proposed approaches. Accurate approximations for the ergodic sum-rates of the two first schemes are also derived. Simulation results validate the theoretical analysis and confirm the effectiveness of the proposed schemes. For instance, under low to moderate cross-polar interference, the results show that, even under high levels of residual SIC error, our dual-polarized MIMO-RSMA strategies outperform the conventional single-polarized MIMO-RSMA counterpart. It is also shown that the performance of all RSMA schemes is impressively higher than that of single and dual-polarized massive MIMO systems employing non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) techniques.

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