arXiv subjects

Visa Koivunen

Publications and source records attributed to Visa Koivunen.

At least 19 recordsLinked to original sources

arXiv2026

Unified Evaluation Methodology for AI-Native Integrated Sensing and Communication

Integrated Sensing and Communication (ISAC) couples radio sensing, data transmission, and control actions within a single closed-loop system. When Artificial Intelligence (AI)-driven policies adapt sensing and communication online across a variety of sensing tasks and objectives, end-to-end performance is shaped not only by waveform and channel conditions but also by inference latency, uncertainty, environmental dynamics, and hardware non-idealities, leading to fundamental trade-offs between sensing accuracy, communication reliability, and resource overhead. This manuscript presents a unified system architecture and evaluation methodology for AI-native ISAC, defined as ISAC in which learning-based agents adapt sensing, communication, and actuation policies online under uncertainty. We formalize the design space of closed-loop ISAC, propose a three-stage validation pipeline from bounds and feasibility analysis, through high-fidelity digital-twin simulation, to preliminary over-the-air validation, and provide a minimal reporting checklist that links technical Key Performance Indicators (KPIs) (e.g., data rate, SINR, target detection, parameter estimation, track quality, localization error, outage, latency, overhead, and energy per decision) to application-level Key Value Indicators (KVIs) (e.g., availability and mission effectiveness). Two representative instantiations, specifically Unmanned Aerial Vehicle (UAV)-based outdoor and Reconfigurable Intelligent Surface (RIS)-enabled indoor coverage extensions, are used to illustrate how to structure reproducible baselines and comparable evidence across heterogeneous deployments, helping bridge the gap between theoretical ISAC gains and deployment-ready performance claims.

cs.NI↗

arXiv2026

Adaptive Sequential Change Detection using Mixtures of Predictive Distributions

This paper studies the problem of detecting a change in the distribution of a sequence of independent observations when the post-change distribution is unknown. We propose a novel change detection algorithm, termed Predictive-Mixture CuSum (PM-CuSum), which combines predictive distributions constructed from sliding windows of different lengths within a CuSum recursion. The predictive distributions are aggregated using adaptive weights based on their recent predictive performance. We show that PM-CuSum achieves first-order asymptotic optimality under mild conditions, and that its asymptotic delay bound has a smaller remainder order than what is achieved procedures using a single fixed (even oracle) window. Numerical simulations demonstrate that PM-CuSum performs well compared to existing methods. Moreover, it is demonstrated that forming likelihood ratios using full predictive distributions can substantially improve performance compared to plug-in likelihoods.

math.ST↗

arXiv2026

Multi-stream Quickest Change Detection: Foundations and Recent Advances

This paper provides an overview of recent developments in quickest change detection (QCD) for high-dimensional multi-sensor systems, with an emphasis on settings involving structural constraints and limited sensing resources. Classical QCD methodologies, while well understood in low-dimensional and fully observed regimes, face significant challenges when extended to modern applications characterized by large-scale data, constrained sampling or communication, and heterogeneous signal structures. We review key approaches for handling high dimensionality, including methods that exploit sparsity, and other forms of signal heterogeneity. Additionally, we discuss sampling constraints, where observations must be selected or acquired sequentially under resource limitations. Multi-stream applications can require making multiple detections, for example when detecting changes separately in different streams. The underlying assumptions on probability models, the types of changes taking place, commonly used decision-making criteria, performance indices, and error types are described. We also briefly discuss the application of machine learning in cases where the underlying probability models are not known or there is a need to select which sensors should monitor the phenomena because of the large scale of the system.

math.ST↗

arXiv2026

Sparse Sensor Arrays for Active Sensing: Models, Configurations and Applications

This chapter focuses on active sensing using sparse arrays. In active sensing applications, such as radar, sonar, wireless communications, and medical ultrasound, a collection of sensors probes the environment by emitting self-generated energy. A key benefit of such active multi-sensor arrays is their ability to focus and steer energy in desired directions by beamforming on transmit. Sparse sensor arrays offer several advantages over conventional uniform arrays, including improved resolution using fewer physical sensors and the capability to identify more scatterers than sensors. This is facilitated by the effective transmit-receive virtual array known as the sum co-array, which can have many more virtual sensors than the number of physical transmit or receive sensors. Herein, we focus on the design of low-redundancy sparse array configurations and on employing transmit-receive (Tx-Rx) beamforming using sparse arrays. We discuss the optimal, but computationally intractable Minimum-redundancy array, and a scalable symmetric array framework, which extends many well-known passive sparse array geometries to the active case. We also examine mitigating side lobes arising from spatial undersampling by a synthetic beamforming method known as image addition. We briefly present approaches for finding the physical beamforming weights synthesizing a desired Tx-Rx beampattern, and consider related spatio-temporal trade-offs. We conclude by discussing selected applications of sparse arrays in active sensing.

eess.SP↗

arXiv2025

DoA Estimation with Sparse Arrays: Effects of Antenna Element Patterns and Nonidealities

This paper studies the effects of directional antenna element complex gain patterns and nonidealities in direction of arrival (DoA) estimation. We compare sparse arrays and classical uniform linear arrays, harnessing EM simulation tools to accurately model the electromagnetic behavior of both patch and Vivaldi antenna element including mutual coupling effects. We show that with sparse array configurations, the performance impacts are significant in terms of DoA estimation accuracy and operable SNR ranges. Specifically, in the scenarios considered, both the usage of directional antenna elements and a sparse array result in over 90% reduction in average direction finding error, compared to a uniform omnidirectional array with the same number of elements (in this case eight), when estimating the directions of two sources using the MUSIC algorithm. For a fixed angular RMSE, the improvements in array sensitivity are shown to yield a 4 to 15-fold increase in one-way coverage distance (assuming free-space path loss). Among the studied options, the best performance was obtained using sparse arrays with either patch or Vivaldi elements for field of views of 100$^\circ$ or 120$^\circ$, respectively.

eess.SP↗

arXiv2025

Beyond-Diagonal RIS: Adversarial Channels and Optimality of Low-Complexity Architectures

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) have recently gained attention as an enhancement to conventional RISs. BD-RISs allow optimizing not only the phase, but also the amplitude responses of their discrete surface elements by introducing adjustable inter-element couplings. Various BD-RIS architectures have been proposed to optimally trade off between average performance and complexity of the architecture. However, little attention has been paid to worst-case performance. This paper characterizes novel sets of adversarial channels for which certain low-complexity BD-RIS architectures have suboptimal performance in terms of received signal power at an intended communications user. Specifically, we consider two recent BD-RIS models: the so-called group-connected and tree-connected architecture. The derived adversarial channel sets reveal new surprising connections between the two architectures. We validate our analytical results numerically, demonstrating that adversarial channels can cause a significant performance loss. Our results pave the way towards efficient BD-RIS designs that are robust to adversarial propagation conditions and malicious attacks.

eess.SP↗

arXiv2025

The Adaptive $τ$-Lasso: Robustness and Oracle Properties

This paper introduces a new regularized version of the robust $τ$-regression estimator for analyzing high-dimensional datasets subject to gross contamination in the response variables and covariates. The resulting estimator, termed adaptive $τ$-Lasso, is robust to outliers and high-leverage points. It also incorporates an adaptive $\ell_1$-norm penalty term, which enables the selection of relevant variables and reduces the bias associated with large true regression coefficients. More specifically, this adaptive $\ell_1$-norm penalty term assigns a weight to each regression coefficient. For a fixed number of predictors $p$, we show that the adaptive $τ$-Lasso has the oracle property, ensuring both variable-selection consistency and asymptotic normality. Asymptotic normality applies only to the entries of the regression vector corresponding to the true support, assuming knowledge of the true regression vector support. We characterize its robustness by establishing the finite-sample breakdown point and the influence function. We carry out extensive simulations and observe that the class of $τ$-Lasso estimators exhibits robustness and reliable performance in both contaminated and uncontaminated data settings. We also validate our theoretical findings on robustness properties through simulations. In the face of outliers and high-leverage points, the adaptive $τ$-Lasso and $τ$-Lasso estimators achieve the best performance or match the best performances of competing regularized estimators, with minimal or no loss in terms of prediction and variable selection accuracy for almost all scenarios considered in this study. Therefore, the adaptive $τ$-Lasso and $τ$-Lasso estimators provide attractive tools for a variety of sparse linear regression problems, particularly in high-dimensional settings and when the data is contaminated by outliers and high-leverage points.

stat.ML↗

arXiv2025

Quickest Change Detection for Multiple Data Streams Using the James-Stein Estimator

The problem of quickest change detection is studied in the context of detecting an arbitrary unknown mean-shift in multiple independent Gaussian data streams. The James-Stein estimator is used in constructing detection schemes that exhibit strong detection performance both asymptotically and non-asymptotically. Our results indicate that utilizing the James-Stein estimator in the recently developed window-limited CuSum test constitutes a uniform improvement over its typical maximum likelihood variant. That is, the proposed James-Stein version achieves a smaller detection delay simultaneously for all possible post-change parameter values and every false alarm rate constraint, as long as the number of parallel data streams is greater than three. Additionally, an alternative detection procedure that utilizes the James-Stein estimator is shown to have asymptotic detection delay properties that compare favorably to existing tests. The second-order asymptotic detection delay term is reduced in a predefined low-dimensional subspace of the parameter space, while second-order asymptotic minimaxity is preserved. The results are verified in simulations, where the proposed schemes are shown to achieve smaller detection delays compared to existing alternatives, especially when the number of data streams is large.

math.ST↗

arXiv2024

Generative Deep Synthesis of MIMO Sensing Waveforms with Desired Transmit Beampattern

This paper develops a generative deep learning model for the synthesis of multiple-input multiple-output (MIMO) active sensing waveforms with desired properties, including constant modulus and a user-defined beampattern. The proposed approach is capable synthesizing unique phase codes of on-the-fly, which has the potential to reduce interference between co-existing active sensing systems and facilitate Low Probability of Intercept/Low Probability of Detection (LPI/LPD) radar operation. The paper extends our earlier work on synthesis of approximately orthogonal MIMO phase codes by introducing flexible control over the transmit beampatterns. The developed machine learning method employs a conditional Wasserstein Generative Adversarial Network (GAN) structure. The main benefits of the method are its ability to discover new waveforms on-demand (post training) and generate demanding beampatterns at lower computational complexity compared to structured optimization approaches.

eess.SP↗

arXiv2024

Multicarrier ISAC: Advances in Waveform Design, Signal Processing and Learning under Non-Idealities

This paper addresses the topic of integrated sensing and communications (ISAC) in 5G and emerging 6G wireless networks. ISAC systems operate within shared, congested or even contested spectrum, aiming to deliver high performance in both wireless communications and radio frequency (RF) sensing. The expected benefits include more efficient utilization of spectrum, power, hardware (HW) and antenna resources. Focusing on multicarrier (MC) systems, which represent the most widely used communication waveforms, it explores the co-design and optimization of waveforms alongside multiantenna transceiver signal processing for communications and both monostatic and bistatic sensing applications of ISAC. Moreover, techniques of high practical relevance for overcoming and even harnessing challenges posed by non-idealities in actual transceiver implementations are considered. To operate in highly dynamic radio environments and target scenarios, both model-based structured optimization and learning-based methodologies for ISAC systems are covered, assessing their adaptability and learning capabilities under real-world conditions. The paper presents trade-offs in communication-centric and radar-sensing-centric approaches, aiming for an optimized balance in densely used spectrum.

eess.SP↗

arXiv2024

Causal Influence in Federated Edge Inference

In this paper, we consider a setting where heterogeneous agents with connectivity are performing inference using unlabeled streaming data. Observed data are only partially informative about the target variable of interest. In order to overcome the uncertainty, agents cooperate with each other by exchanging their local inferences with and through a fusion center. To evaluate how each agent influences the overall decision, we adopt a causal framework in order to distinguish the actual influence of agents from mere correlations within the decision-making process. Various scenarios reflecting different agent participation patterns and fusion center policies are investigated. We derive expressions to quantify the causal impact of each agent on the joint decision, which could be beneficial for anticipating and addressing atypical scenarios, such as adversarial attacks or system malfunctions. We validate our theoretical results with numerical simulations and a real-world application of multi-camera crowd counting.

cs.LG↗

arXiv2023

On the Fusion Strategies for Federated Decision Making

We consider the problem of information aggregation in federated decision making, where a group of agents collaborate to infer the underlying state of nature without sharing their private data with the central processor or each other. We analyze the non-Bayesian social learning strategy in which agents incorporate their individual observations into their opinions (i.e., soft-decisions) with Bayes rule, and the central processor aggregates these opinions by arithmetic or geometric averaging. Building on our previous work, we establish that both pooling strategies result in asymptotic normality characterization of the system, which, for instance, can be utilized to derive approximate expressions for the error probability. We verify the theoretical findings with simulations and compare both strategies.

cs.LG↗

arXiv2022

On the Impact of Phase Noise on Monostatic Sensing in OFDM ISAC Systems

Phase noise (PN) can become a major bottleneck for integrated sensing and communications (ISAC) systems towards 6G wireless networks. In this paper, we consider an OFDM ISAC system with oscillator imperfections and investigate the impact of PN on monostatic sensing performance by performing a misspecified Cramér-Rao bound (MCRB) analysis. Simulations are carried out under a wide variety of operating conditions with regard to SNR, oscillator type (free-running oscillators (FROs) and phase-locked loops (PLLs)), 3-dB bandwidth of the oscillator spectrum, PLL loop bandwidth and target range. The results provide valuable insights on when PN leads to a significant degradation in range and/or velocity accuracy, establishing important guidelines for hardware and algorithm design in 6G ISAC systems.

eess.SP↗

arXiv2022

Monostatic Sensing with OFDM under Phase Noise: From Mitigation to Exploitation

We consider the problem of monostatic radar sensing with orthogonal frequency-division multiplexing (OFDM) joint radar-communications (JRC) systems in the presence of phase noise (PN) caused by oscillator imperfections. We begin by providing a rigorous statistical characterization of PN in the radar receiver over multiple OFDM symbols for free-running oscillators (FROs) and phase-locked loops (PLLs). Based on the delay-dependent PN covariance matrix, we derive the hybrid maximum-likelihood (ML)/maximum a-posteriori (MAP) estimator of the deterministic delay-Doppler parameters and the random PN, resulting in a challenging high-dimensional nonlinear optimization problem. To circumvent the nonlinearity of PN, we then develop an iterated small angle approximation (ISAA) algorithm that progressively refines delay-Doppler-PN estimates via closed-form updates of PN as a function of delay-Doppler at each iteration. Moreover, unlike existing approaches where PN is considered to be purely an impairment that has to be mitigated, we propose to exploit PN for resolving range ambiguity by capitalizing on its delay-dependent statistics (i.e., the range correlation effect), through the formulation of a parametric Toeplitz-block Toeplitz covariance matrix reconstruction problem. Simulation results indicate quick convergence of ISAA to the hybrid Cramér-Rao bound (CRB), as well as its remarkable performance gains over state-of-the-art benchmarks, for both FROs and PLLs under various operating conditions, while showing that the detrimental effect of PN can be turned into an advantage for sensing.

eess.SP↗

arXiv2022

Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data

In this paper, we address the problem of conducting statistical inference in settings involving large-scale data that may be high-dimensional and contaminated by outliers. The high volume and dimensionality of the data require distributed processing and storage solutions. We propose a two-stage distributed and robust statistical inference procedures coping with high-dimensional models by promoting sparsity. In the first stage, known as model selection, relevant predictors are locally selected by applying robust Lasso estimators to the distinct subsets of data. The variable selections from each computation node are then fused by a voting scheme to find the sparse basis for the complete data set. It identifies the relevant variables in a robust manner. In the second stage, the developed statistically robust and computationally efficient bootstrap methods are employed. The actual inference constructs confidence intervals, finds parameter estimates and quantifies standard deviation. Similar to stage 1, the results of local inference are communicated to the fusion center and combined there. By using analytical methods, we establish the favorable statistical properties of the robust and computationally efficient bootstrap methods including consistency for a fixed number of predictors, and robustness. The proposed two-stage robust and distributed inference procedures demonstrate reliable performance and robustness in variable selection, finding confidence intervals and bootstrap approximations of standard deviations even when data is high-dimensional and contaminated by outliers.

stat.ML↗

arXiv2022

Bayesian Quickest Detection of Propagating Spatial Events

Rapid detection of spatial events that propagate across a sensor network is of wide interest in many modern applications. In particular, in communications, radar, IoT, environmental monitoring, and biosurveillance, we may observe propagating fields or particles. In this paper, we propose Bayesian sequential single and multiple change-point detection procedures for the rapid detection of such phenomena. Using a dynamic programming framework we derive the structure of the optimal single-event quickest detection procedure, which minimizes the average detection delay (ADD) subject to a false alarm probability upper bound. The multi-sensor system configuration is arbitrary and sensors may be mobile. In the rare event regime, the optimal procedure converges to a more practical threshold test on the posterior probability of the change point. A convenient recursive computation of this posterior probability is derived by using the propagation characteristics of the spatial event. The ADD of the posterior probability threshold test is analyzed in the asymptotic regime, and specific analysis is conducted in the setting of detecting random Gaussian signals affected by path loss. Then, we show how the proposed procedure is easy to extend for detecting multiple propagating spatial events in parallel in a multiple hypothesis testing setting. A method that provides strict false discovery rate (FDR) control is proposed. In the simulation section, it is demonstrated that exploiting the spatial properties of the event decreases the ADD compared to procedures that do not utilize this information, even under model mismatch.

math.ST↗

arXiv2022

Multiple Hypothesis Testing Framework for Spatial Signals

The problem of identifying regions of spatially interesting, different or adversarial behavior is inherent to many practical applications involving distributed multisensor systems. In this work, we develop a general framework stemming from multiple hypothesis testing to identify such regions. A discrete spatial grid is assumed for the monitored environment. The spatial grid points associated with different hypotheses are identified while controlling the false discovery rate at a pre-specified level. Measurements are acquired using a large-scale sensor network. We propose a novel, data-driven method to estimate local false discovery rates based on the spectral method of moments. Our method is agnostic to specific spatial propagation models of the underlying physical phenomenon. It relies on a broadly applicable density model for local summary statistics. In between sensors, locations are assigned to regions associated with different hypotheses based on interpolated local false discovery rates. The benefits of our method are illustrated by applications to spatially propagating radio waves.

eess.SP↗

arXiv2022

Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing

Radar and modern communication systems are both evaluating towards higher frequency bands and massive antenna arrays, thus increasing their similarities in terms of hardware structure, channel characteristics, and signal processing pipelines. To suppress the cross-system interference caused by communications and radar systems with shared spectral and hardware resources, the co-design philosophy, wherein the communications and radar/sensing systems can operate in parallel with jointly optimized performance, has drawn substantial attention from both academia and industry. In this paper, we propose a nullspace-based joint precoder-decoder design for spectrum sharing between multicarrier radar and multiuser multicarrier communication systems, by employing the maximizing signal interference noise ratio (max-SINR) criterion and interference alignment (IA) constraints. By projecting the cross-system interference to the designed null spaces, a maximum degree of freedom upper bound for the $K+1$-radar-communication-user interference channel can be achieved. Our simulation studies demonstrate that interference can be practically fully canceled in both communication and radar systems. This leads to improved detection performance in radar and a higher rate in communication subsystems. A significant performance gain over a nullspace-based precoder-only design is also obtained.

eess.SP↗