arXiv subjects

Khac-Hoang Ngo

Publications and source records attributed to Khac-Hoang Ngo.

At least 19 recordsLinked to original sources

arXiv2026

Type-Based Unsourced Multiple Access Over Fading Channels in Distributed MIMO With Application to Multi-Target Localization

We consider the problem of type estimation over unsourced multiple access fading channels in distributed multiple-input multiple-output (D-MIMO) systems. Unlike classical unsourced multiple access, type-based unsourced multiple access (TUMA) aims to estimate the type, i.e., the empirical distribution of transmitted messages. We extend our prior work on TUMA over additive white Gaussian channels to fading scenarios in which neither the transmitters nor the receiver have channel state information. To mitigate the impact of path-loss variability, we employ location-based codebook partitioning: users with similar large-scale fading coefficients use the same codebook. The decoder is built on the multisource approximate message passing algorithm proposed by Cakmak et al. (2025), and supports both centralized and distributed implementations. As an application, we demonstrate how TUMA enables efficient communication in a multi-target localization setting, where distributed sensors report to a D-MIMO receiver quantized target positions. We propose a performance cost function that combines localization errors with a misdetection penalty, and use it to characterize how performance depends on the fraction of resources assigned to sensing vs. communication, as well as on the number of bits used to quantize the positions of the targets.

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arXiv2026

Distributed MIMO With Over-the-Air Phase Calibration Integrated Into the TDD Flow

Reciprocity-based, joint coherent downlink beamforming from multiple access points (APs) in distributed multiple-input multiple-output (MIMO) with independent local oscillators (LOs) requires the APs to be periodically phase-calibrated (a.k.a. phase-synchronized or phase-aligned). Such phase calibration can be accomplished by bidirectional over-the-air measurements between the APs. In this paper, we show how such over-the-air measurements can be integrated into the time-division duplexing (TDD) flow by appropriately shifting the uplink/downlink switching points of the TDD slot structure, creating short time segments during which APs can measure on one another. We also show how this technique scales to large networks. Furthermore, we analytically characterize the tradeoff between the amount of resources spent on calibration measurements and the resulting spectral efficiency of the system, when conjugate beamforming or zero-forcing beamforming is used. The results demonstrate the feasibility of distributed MIMO with phase-calibration through over-the-air inter-AP measurements integrated into the TDD flow, and the advantage of this design over schemes with dedicated calibration slots.

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arXiv2026

Minimum Energy per Bit of Unsourced Multiple Access with Location-Based Codebook Partitioning

We derive finite-blocklength bounds on the minimum achievable energy per bit over a Gaussian unsourced multiple access (UMA) channel in the presence of heterogeneous path-loss conditions. We consider a setting in which the path loss is known to the users, which enables the use of location-based codebook partitioning [Çakmak et al., 2025]. Through numerical simulations and a large-system analysis based on the replica method, we quantify the performance gain of this strategy relative to the conventional UMA approach in which all users employ a common codebook.

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arXiv2026

MATTER: Multiscale Attention for Registration Error Regression

Point cloud registration (PCR) is crucial for many downstream tasks, such as simultaneous localization and mapping (SLAM) and object tracking. This makes detecting and quantifying registration misalignment, i.e., PCR quality validation, an important task. All existing methods treat validation as a classification task, aiming to assign the PCR quality to a few classes. In this work, we instead use regression for PCR validation, allowing for a more fine-grained quantification of the registration quality. We also extend previously used misalignment-related features by using multiscale extraction and attention-based aggregation. This leads to accurate and robust registration error estimation on diverse datasets, especially for point clouds with heterogeneous spatial densities. Furthermore, when used to guide a mapping downstream task, our method significantly improves the mapping quality for a given amount of re-registered frames, compared to the state-of-the-art classification-based method.

cs.CV↗

arXiv2026

Type-Based Unsourced Federated Learning With Client Self-Selection

We address the client-selection problem in federated learning over wireless networks under data heterogeneity. Existing client-selection methods often rely on server-side knowledge of client-specific information, thus compromising privacy. To overcome this issue, we propose a client self-selection strategy based solely on the comparison between locally computed training losses and a centrally updated selection threshold. Furthermore, to support robust aggregation of clients' updates over wireless channels, we integrate this client self-selection strategy into the recently proposed type-based unsourced multiple-access framework over distributed multiple-input multiple-output (D-MIMO) networks. The resulting scheme is completely unsourced: the server does not need to know the identity of the clients. Moreover, no channel state information is required, neither at the clients nor at the server side. Simulation results conducted over a D-MIMO wireless network show that the proposed self-selection strategy matches the performance of a comparable state-of-the-art server-side selection method and consistently outperforms random client selection.

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arXiv2025

Practical Bayes-Optimal Membership Inference Attacks

We develop practical and theoretically grounded membership inference attacks (MIAs) against both independent and identically distributed (i.i.d.) data and graph-structured data. Building on the Bayesian decision-theoretic framework of Sablayrolles et al., we derive the Bayes-optimal membership inference rule for node-level MIAs against graph neural networks, addressing key open questions about optimal query strategies in the graph setting. We introduce BASE and G-BASE, tractable approximations of the Bayes-optimal membership inference. G-BASE achieves superior performance compared to previously proposed classifier-based node-level MIA attacks. BASE, which is also applicable to non-graph data, matches or exceeds the performance of prior state-of-the-art MIAs, such as LiRA and RMIA, at a significantly lower computational cost. Finally, we show that BASE and RMIA are equivalent under a specific hyperparameter setting, providing a principled, Bayes-optimal justification for the RMIA attack.

cs.LG↗

arXiv2025

Information Age and Correctness for Energy Harvesting Devices with Random Access

We investigate accuracy and freshness of status updates from a large number of energy-harvesting devices that monitor two-state Markov processes and access the medium using the slotted ALOHA protocol without feedback. Using a Markovian framework, we analyze the average value of a generic state-dependent penalty function that grows whenever there is a state estimation error. The age of incorrect information (AoII) is an example of such penalty function. We propose an accurate and easy-to-compute approximation for the average penalty. Numerical results demonstrate the benefits of optimizing the transmission probabilities according to the process state transitions and current battery levels to minimize the average penalty. Minimizing a state-independent penalty function can be highly suboptimal in terms of average penalty when one of the process states is critical, i.e., entails a high penalty if wrongly estimated. Furthermore, minimizing the average penalty does not guarantee a low probability of misdetecting a critical state period.

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arXiv2025

Breaking the TDD Flow for Over-the-Air Phase Synchronization in Distributed Antenna Systems

Phase synchronization between distributed antenna arrays requires measurements that break the standard time-division duplex (TDD) operation. We present a feasibility study on implementing such synchronization and analyze its impact on the quality of service. Considering two antenna arrays with independent local oscillators (LOs), we propose a modified TDD flow to accommodate the transmission of phase synchronization signals, formulate the phase estimation and compensation problem, and derive the achievable downlink spectral efficiency (SE). Numerical results show that frequent re-estimation of the interarray phase disparity is essential for maximizing SE in systems with low-quality LOs. Furthermore, applying a Kalman filter for phase tracking substantially improves the SE, especially if phase estimation errors are large compared to LOs phase drifts.

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arXiv2025

Noncoherent MIMO Communications: Theoretical Foundation, Design Approaches, and Future Challenges

Noncoherent communication is a promising paradigm for future wireless systems where acquiring accurate channel state information (CSI) is challenging or infeasible. It provides methods to bypass the need for explicit channel estimation in practical scenarios such as high-mobility networks, massive distributed antenna arrays, energy-constrained Internet-of-Things devices, and unstructured propagation environments. This survey provides a comprehensive overview of noncoherent communication strategies in multiple-input multiple-output (MIMO) systems, focusing on recent advances since the early 2000s. We classify noncoherent communication schemes into three main approaches where CSI-free signal recovery is based on subspace detection (i.e., Grassmannian signaling), differential detection, and energy detection, respectively. For each approach, we review the theoretical foundation and design methodologies. We also provide comparative insights into their suitability across different channel models and system constraints, highlighting application scenarios where noncoherent methods offer performance and scalability advantages over traditional coherent communication. Furthermore, we discuss practical considerations of noncoherent communication, including compatibility with orthogonal frequency division multiplexing (OFDM), resilience to hardware impairments, and scalability with the number of users. Finally, we provide an outlook on future challenges and research directions in designing robust and efficient noncoherent systems for next-generation wireless networks.

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arXiv2025

An Achievability Bound for Type-Based Unsourced Multiple Access

We derive an achievability bound to quantify the performance of a type-based unsourced multiple access system -- an information-theoretic model for grant-free multiple access with correlated messages. The bound extends available achievability results for the per-user error probability in the unsourced multiple access framework, where, different from our setup, message collisions are treated as errors. Specifically, we provide an upper bound on the total variation distance between the type (i.e., the empirical probability mass function) of the transmitted messages and its estimate over a Gaussian multiple access channel. Through numerical simulations, we illustrate that our bound can be used to determine the message type that is less efficient to transmit, because more difficult to detect. We finally show that a practical scheme for type estimation, based on coded compressed sensing with approximate message passing, operates approximately 3 dB away from the bound, for the parameters considered in the paper.

cs.IT↗

arXiv2025

Type-Based Unsourced Multiple Access over Fading Channels with Cell-Free Massive MIMO

Type-based unsourced multiple access (TUMA) is a recently proposed framework for type-based estimation in massive uncoordinated access networks. We extend the existing design of TUMA, developed for an additive white Gaussian channel, to a more realistic environment with fading and multiple antennas. Specifically, we consider a cell-free massive multiple-input multiple-output system and exploit spatial diversity to estimate the set of transmitted messages and the number of users transmitting each message. Our solution relies on a location-based codeword partition and on the use at the receiver of a multisource approximate message passing algorithm in both centralized and distributed implementations. The proposed TUMA framework results in a robust and scalable architecture for massive machine-type communications.

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arXiv2025

Sparse Orthogonal Matching Pursuit-based Parameter Estimation for Integrated Sensing and Communications

Accurate parameter estimation such as angle of arrival (AOA) is essential to enhance the performance of integrated sensing and communication (ISAC) in mmWave multiple-input multiple-output (MIMO) systems. This work presents a sensing-aided communication channel estimation mechanism, where the sensing channel shares the same AOA with the uplink communication channel. First, we propose a novel orthogonal matching pursuit (OMP)-based method for coarsely estimating the AOA in a sensing channel, offering improved accuracy compared to conventional methods that rely on rotational invariance techniques. Next, we refine the coarse estimates obtained in the first step by modifying the Space-Alternating Generalized Expectation Maximization algorithm for fine parameter estimation. Through simulations and mathematical analysis, we demonstrate that scenarios with shared AOA achieve a better Cramer-Rao lower bound (CRLB) than those without sharing. This finding highlights the potential of leveraging joint sensing and communication channels to enhance parameter estimation accuracy, particularly in channel or location estimation applications.

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arXiv2025

Timely Status Updates in Slotted ALOHA Networks With Energy Harvesting

We investigate the age of information (AoI) in a scenario where energy-harvesting devices send status updates to a gateway following the slotted ALOHA protocol and receive no feedback. We let the devices adjust the transmission probabilities based on their current battery level. Using a Markovian analysis, we derive analytically the average AoI. We further provide an approximate analysis for accurate and easy-to-compute approximations of both the average AoI and the age-violation probability (AVP), i.e., the probability that the AoI exceeds a given threshold. We also analyze the average throughput. Via numerical results, we investigate two baseline strategies: transmit a new update whenever possible to exploit every opportunity to reduce the AoI, and transmit only when sufficient energy is available to increase the chance of successful decoding. The two strategies are beneficial for low and high update-generation rates, respectively. We show that an optimized policy that balances the two strategies outperforms them significantly in terms of both AoI metrics and throughput. Finally, we show the benefit of decoding multiple packets in a slot using successive interference cancellation and adapting the transmission probability based on both the current battery level and the time elapsed since the last transmission.

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arXiv2025

Protocol Design for Irregular Repetition Slotted ALOHA With Energy Harvesting to Maintain Information Freshness

We investigate an internet-of-things system where energy-harvesting devices send status updates to a common receiver using the irregular repetition slotted ALOHA (IRSA) protocol. Energy shortages in these devices may lead to transmission failures that are unknown to the receiver, disrupting the decoding process. To address this issue, we propose a method for the receiver to perfectly identify such failures. Furthermore, we optimize the degree distribution of the protocol to enhance the freshness of the status updates. Our optimized degree distribution mitigates the adverse effects of potential transmission failures. Numerical results demonstrate that, despite energy-harvesting constraints, IRSA can achieve a level of information freshness comparable to systems with unlimited energy.

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arXiv2024

On Local Mutual-Information Privacy

Local mutual-information privacy (LMIP) is a privacy notion that aims to quantify the reduction of uncertainty about the input data when the output of a privacy-preserving mechanism is revealed. We study the relation of LMIP with local differential privacy (LDP), the de facto standard notion of privacy in context-independent (CI) scenarios, and with local information privacy (LIP), the state-of-the-art notion for context-dependent settings. We establish explicit conversion rules, i.e., bounds on the privacy parameters for an LMIP mechanism to also satisfy LDP/LIP, and vice versa. We use our bounds to formally verify that LMIP is a weak privacy notion. We also show that uncorrelated Gaussian noise is the best-case noise in terms of CI-LMIP if both the input data and the noise are subject to an average power constraint.

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arXiv2024

Secure Aggregation is Not Private Against Membership Inference Attacks

Secure aggregation (SecAgg) is a commonly-used privacy-enhancing mechanism in federated learning, affording the server access only to the aggregate of model updates while safeguarding the confidentiality of individual updates. Despite widespread claims regarding SecAgg's privacy-preserving capabilities, a formal analysis of its privacy is lacking, making such presumptions unjustified. In this paper, we delve into the privacy implications of SecAgg by treating it as a local differential privacy (LDP) mechanism for each local update. We design a simple attack wherein an adversarial server seeks to discern which update vector a client submitted, out of two possible ones, in a single training round of federated learning under SecAgg. By conducting privacy auditing, we assess the success probability of this attack and quantify the LDP guarantees provided by SecAgg. Our numerical results unveil that, contrary to prevailing claims, SecAgg offers weak privacy against membership inference attacks even in a single training round. Indeed, it is difficult to hide a local update by adding other independent local updates when the updates are of high dimension. Our findings underscore the imperative for additional privacy-enhancing mechanisms, such as noise injection, in federated learning.

cs.LG↗

arXiv2024

Type-Based Unsourced Multiple Access

We generalize the type-based multiple access framework proposed by Mergen and Tong (2006) to the case of unsourced multiple access. In the proposed framework, each device tracks the state of a physical/digital process, quantizes this state, and communicates it to a common receiver through a shared channel in an uncoordinated manner. The receiver aims to estimate the type of the states, i.e., the set of states and their multiplicity in the sequence of states reported by all devices. We measure the type estimation error using the Wasserstein distance. Considering an example of multi-target position tracking, we show that type estimation can be performed effectively via approximate message passing. Furthermore, we determine the quantization resolution that minimizes the type estimation error by balancing quantization distortion and communication error.

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arXiv2023

Unsourced Multiple Access with Common Alarm Messages: Network Slicing for Massive and Critical IoT

We investigate the coexistence of massive and critical Internet of Things (IoT) services in the context of the unsourced multiple access (UMA) framework introduced by Polyanskiy (2017), where all users employ a common codebook and the receiver returns an unordered list of decoded codewords. This setup is suitably modified to introduce heterogeneous traffic. Specifically, to model the massive IoT service, a standard message originates independently from each IoT device as in the standard UMA setup. To model the critical IoT service, we assume the generation of alarm messages that are common for all devices. This setup requires a significant redefinition of the error events, i.e., misdetections and false positives. We further assume that the number of active users in each transmission attempt is random and unknown. We derive a random-coding achievability bound on the misdetection and false positive probabilities of both standard and alarm messages on the Gaussian multiple access channel. Using our bound, we demonstrate that orthogonal network slicing enables massive and critical IoT to coexist under the requirement of high energy efficiency. On the contrary, we show that nonorthogonal network slicing is energy inefficient due to the residual interference from the alarm signal when decoding the standard messages.

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