arXiv subjects

Musa Furkan Keskin

Publications and source records attributed to Musa Furkan Keskin.

At least 19 recordsLinked to original sources

arXiv2026

Phase-Coherent Doppler-Only Sensing for D-MIMO ISAC

This paper studies phase-coherent Doppler-only sensing for bandwidth-limited sub-6 GHz D-MIMO ISAC. We consider a multistatic system with one transmitter and multiple synchronized single-antenna receivers, and develop noncoherent and coherent maximum-likelihood estimators together with their position error bounds. The analysis shows that noncoherent sensing relies only on Doppler and cannot localize static targets, whereas phase-coherent processing exploits carrier phase to provide additional position information. Simulations confirm the bounds and show that coherent processing significantly improves localization accuracy in the considered narrowband setting.

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arXiv2026

Task-Aware Beamforming for Semantic Wireless Localization

Many semantic localization tasks require determining a contextually meaningful region or state rather than minimizing the error of a full Cartesian position estimate. In such settings, aggregate localization accuracy need not align with the accuracy of the semantic decision. This paper develops a Cramér--Rao bound (CRB)-based framework that uses the local geometry of a known semantic map to guide uplink receive beamforming. Using local boundary crossing as a surrogate for semantic misclassification, we derive normal information maximization (NIM), which minimizes the CRB of the position component along the local semantic boundary normal. Under the considered single-path line-of-sight model, an optimal receive codebook can be restricted to the subspace spanned by the matched steering vector and its angular derivative, reducing the design to an allocation of measurement resources between matched and derivative spatial modes. We derive the resulting allocation for a general smooth boundary, with geofencing and intrusion detection arising as radial and tangential limiting cases. The framework is further extended to multi-region semantic maps through a distance-normalized minimax criterion and to uncertain prior locations through worst-case and prior-weighted robust formulations. Numerical results with finite-slot implementations and observation-level Monte Carlo simulations using profile maximum-likelihood (ML) estimation show that NIM allocates measurements according to the task-relevant boundary geometry and can substantially reduce semantic error relative to the classic squared position error bound (SPEB) design in the considered scenarios. The empirical results also closely follow the local CRB-based boundary-crossing approximation in the studied operating regime.

cs.IT↗

arXiv2026

Frugal Radio SLAM: Coherent Imaging with One Subcarrier and One Snapshot in Distributed MIMO

We study uplink radio simultaneous localization and mapping (SLAM) in a pre-calibrated phase-coherent distributed MIMO (D-MIMO) system at a minimal sensing-resource operating point: a single-antenna user equipment (UE) transmits one narrowband pilot, and each single-antenna access points (APs) provides one complex observation. We formulate coherent matched-filter imaging over a spatial grid, where the UE, virtual UEs associated with planar reflectors, and point scatterers share an equivalent-source representation. A greedy detect-and-cancel procedure separates these components. Simulations show that, at the considered noise level, weak-target recovery is limited primarily by cancellation residuals and model saturation, rather than by the noise floor.

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arXiv2026

Radio Imaging and Resource Allocation in Frugal Multistatic D-MIMO ISAC Systems

Emerging integrated sensing and communication (ISAC) systems based on distributed MIMO (D-MIMO) enable radio imaging by exploiting spatial diversity across multiple access points (APs). However, joint sensing and communication introduce mutual interference between communication and sensing signals. In this paper, we propose a downlink D-MIMO ISAC framework that allocates orthogonal subcarriers to sensing and communication to eliminate inter-function interference. We consider a phase-coherent architecture in which single-antenna APs serve communication user equipment (UEs) while constructing a reflectivity image of the environment. We develop a two-timescale resource allocation framework that minimizes reconstructed-image entropy subject to a communication spectral-efficiency (SE) constraint. The proposed design follows a communication-centric policy, where imaging uses the resources left after satisfying the communication requirement. The long-timescale optimization (LTO) determines AP modes and subcarrier assignment, including the partitioning between communication and sensing and the allocation of sensing subcarriers among transmit APs, using synthetic scenarios with random UE and target distributions. The short-timescale optimization (STO) adapts the communication-sensing power-splitting factor to preserve the SE constraint in the current scenario. Numerical results show that the proposed orthogonal subcarrier allocation achieves a superior sensing-communication trade-off compared with conventional superposition, where sensing and communication share the same subcarriers. Finally, we evaluate coherent and non-coherent imaging receivers and identify the synchronization regimes in which each approach provides better imaging and localization performance.

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arXiv2026

Dual-Orthogonality Waveforms for Integrated Communication and Imaging in Dynamic Multipath Channels

Dual-Orthogonality waveforms are multi-antenna signaling schemes that enforce mutual orthogonality across transmit channels and over a prescribed set of delay shifts. By relaxing strict time orthogonality to the physically admissible propagation region, they preserve full-band operation per transmit antenna while embedding communication data and maintaining stream separability. This makes them attractive for Integrated Sensing and Communications (ISAC), where reliable data transmission, high-resolution sensing, and imaging must coexist under time-varying propagation. In dynamic multipath environments, delay-Doppler dispersion across multiple paths perturbs the transmit subspaces and partially breaks the relaxed orthogonality conditions. This paper analyzes this effect and develops a multipath-aware decoding framework based on structured parameter estimation, effective-subspace reconstruction, and low-complexity linear equalization. Numerical results show communication performance comparable to OFDM-based ISAC and MIMO-OTFS baselines while improving sensing and imaging through full-band per-transmit operation. The proposed approach achieves approximately 30 cm range resolution, more than 15 dB suppression of multipath imaging artifacts with coherent SAR processing, and a favorable sensing-communication trade-off. Over-the-air experiments at 60 GHz validate multi-stream communication, the designed zero-correlation region, and accurate radar ranging. A second campaign in a highly reflective indoor environment further demonstrates multipath-aware stream equalization under strong unsuppressed reflections.

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arXiv2026

Exploiting Phase Noise for Sensing Privacy in ISAC Systems

We investigate sensing privacy in orthogonal frequency-division multiplexing (OFDM) integrated sensing and communication (ISAC) systems under the impact of phase noise (PN) arising from local oscillator (LO) imperfections. Specifically, we consider an ISAC scenario comprising a legitimate monostatic ISAC transceiver (Alice), an eavesdropper performing unauthorized bistatic sensing (Eve) and a communication user (UE), each equipped with a non-ideal LO. To characterize sensing performance in the presence of PN, we carry out a misspecified Cramér-Rao bound (MCRB) analysis of monostatic and bistatic range estimation at Alice and Eve, whose differential PN processes are self-correlated (delay-dependent) and cross-correlated (delay-independent) due to the use of a shared and an independent LO, respectively. Simulation results reveal three-way trade-offs among legitimate monostatic sensing at Alice, unauthorized bistatic sensing at Eve and communication to the UE under PN, governed by the LO quality at Alice. Through the LO asymmetry between Alice and Eve, worsening LO quality at Alice can significantly enlarge sensing privacy gap in her favor, especially for nearby targets, with only a moderate reduction in data rate in noise-limited regimes.

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arXiv2026

Ambient IoT Backscatter Devices as Passive Anchors for NLOS Cellular Positioning: Fundamental Limits

Ambient Internet-of-Things backscatter devices at known locations can act as low-cost passive anchors by creating geometrically anchored reflected paths in cellular networks. Unlike reconfigurable intelligent surfaces, practical backscatter devices are independently controlled and lack a common phase reference; their modulation signatures may be known, but their reflection gains and residual phases are generally uncalibrated. We study how much localization information survives this incomplete per-device calibration in uplink non-line-of-sight (NLOS) positioning, where the direct NLOS path and the backscatter-assisted paths share an unknown scatterer. Treating the common channel gain, the relative backscatter response, and the residual device phases as nuisance parameters, we derive closed-form equivalent Fisher information matrices for calibrated, partially calibrated, and fully uncalibrated operation. The analysis shows that unknown device phases remove carrier-phase information from the backscatter-assisted paths, whereas joint uncertainty in the common gain and relative response leaves the direct NLOS path with only bandwidth-dependent delay information. The resulting position-domain bounds show that device count alone is insufficient: the passive anchors must also observe the common scatterer from sufficiently diverse directions. For joint single-snapshot identification of the user equipment and scatterer, at least two devices in two dimensions and three in three dimensions are necessary. The results identify deployment implications for Ambient Internet-of-Things positioning and show which calibration losses also apply to separable subpanel-based reconfigurable-surface architectures.

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arXiv2026

Exploiting Structural Sparsity and Delay-Doppler Decoupling for Low-Complexity OTFS-ISAC Receivers

In this work, the problems of channel estimation, radar sensing, and data detection are addressed for monostatic integrated sensing and communications (ISAC) applications within orthogonal time frequency space (OTFS) systems operating with a reduced cyclic prefix (RCP). Specifically, the delay-Doppler (DD) input-output relationship is formulated in a discrete representation that enables signal-independent disjoint parameter estimation by encapsulating fractional delay and Doppler effects through distinct, structurally sparse matrices. This exact algebraic separability is directly exploited to develop a low-complexity parameter estimation framework for the communication channel, which is seamlessly adapted for monostatic radar sensing on backscattered data frames. To enhance path detection robustly and safeguard estimation accuracy under low signal-to-noise ratio (SNR) regimes where traditional stopping criterionc(SC)-based methods fail, a deep learning (DL) architecture is integrated to perform model order selection via multi-class classification. Furthermore, a path-wise variant of the iterative Landweber method, designated as iterative matched filtering and combining (IMFC), is introduced for low-complexity data detection by leveraging the identical structural sparsity unlocked by the decoupled framework. Simulation results indicate the proposed estimation scheme achieves lower normalized mean squared error (NMSE) than conventional channel estimation algorithms and sensing performance close to the Cramer-Rao lower bound (CRLB). Finally, the IMFC equalizer is shown to deliver bit error rate (BER) performance comparable to the traditional linear minimum mean squared error (LMMSE) benchmark while dramatically reducing the computational load.

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arXiv2026

Multi-Domain Security for 6G ISAC: Challenges and Opportunities in Transportation

Integrated sensing and communication (ISAC) will be central to 6G-enabled transportation, providing both seamless connectivity and high-precision sensing. However, this tight integration exposes attack points not encountered in pure sensing and communication systems. In this article, we identify unique ISAC-induced security challenges and opportunities in three interrelated domains: cyber-physical (where manipulation of sensors and actuators can mislead perception and control), physical-layer (where over-the-air signals are vulnerable to spoofing and jamming) and protocol (where complex cryptographic protocols cannot detect lower-layer attacks). Building on these insights, we put forward a multi-domain security vision for 6G transportation and propose an integrated security framework that unifies protection across domains by leveraging existing ISAC measurements for lightweight cross-checks.

cs.CR↗

arXiv2026

Beyond Legacy OFDM: A Mobility-Adaptive Multi-Gear Framework for 6G

While Third Generation Partnership Project (3GPP) has confirmed orthogonal frequency division multiplexing (OFDM) as the baseline waveform for sixth-generation (6G), its performance is severely compromised in the high-mobility scenarios envisioned for 6G. Building upon the GEARBOX-PHY vision, we present gear-switching OFDM (GS-OFDM): a unified framework in which the base station (BS) adaptively selects among three gears, ranging from legacy OFDM to delay-Doppler domain processing based on the channel mobility conditions experienced by the user equipments (UEs). We illustrate the benefit of adaptive gear switching for communication throughput and, finally, we conclude with an outlook on research challenges and opportunities.

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arXiv2026

Near-Field Localization via Reconfigurable Antennas

Reconfigurable antennas (RAs) utilize the electromagnetic (EM) domain to provide dynamic control over antenna radiation patterns, which offers an effective way to enhance power efficiency in wireless links. Unlike conventional arrays with fixed element patterns, RAs enable on-demand beam-pattern synthesis by directly controlling each antenna's EM characteristics. While existing research on RAs has primarily focused on improving spectral efficiency, this paper explores their application for downlink localization. Moreover, the majority of existing works focus on far-field scenarios with little attention on near-field (NF). Motivated by these gaps, we consider a synthesis model in which each antenna generates desired beampatterns from a finite set of EM basis functions. We then formulate a joint optimization problem for the baseband (BB) and EM precoders with the objective of minimizing the user equipment (UE) position error bound (PEB) in NF conditions. Our analytical derivations and extensive simulation results demonstrate that the proposed hybrid precoder design for RAs significantly improves UE positioning accuracy compared to traditional non-reconfigurable arrays.

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arXiv2026

Model-based Implicit Neural Representation for sub-wavelength Radio Localization

The increasing deployment of large antenna arrays at base stations has significantly improved the spatial resolution and localization accuracy of radio-localization methods. However, traditional signal processing techniques struggle in complex radio environments, particularly in scenarios dominated by non line of sight (NLoS) propagation paths, resulting in degraded localization accuracy. Recent developments in machine learning have facilitated the development of machine learning-assisted localization techniques, enhancing localization accuracy in complex radio environments. However, these methods often involve substantial computational complexity during both the training and inference phases. This work extends the well-established fingerprinting-based localization framework by simultaneously reducing its memory requirements and improving its accuracy. Specifically, a model-based neural network is used to learn the location-to-channel mapping, and then serves as a generative neural channel model. This generative model augments the fingerprinting comparison dictionary while reducing the memory requirements. The proposed method outperforms fingerprinting baselines by achieving sub-wavelength localization accuracy, even in complex static NLoS environments. Remarkably, it offers an improvement by several orders of magnitude in localization accuracy, while simultaneously reducing memory requirements by an order of magnitude compared to classical fingerprinting methods.

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arXiv2026

A Retrieval-Assisted Framework for Wireless Localization

Accurate and robust wireless localization is a key enabler for a wide range of mobile computing applications. Fingerprint-based localization using channel state information (CSI) has attracted significant attention due to its high accuracy and compatibility with existing communication infrastructures. However, traditional similarity-based fingerprinting methods suffer from high computational complexity and limited scalability in high-dimensional CSI spaces, while purely learning-based approaches fail to explicitly exploit correlations among reference fingerprints during inference. To address these challenges, this paper proposes a unified retrieval-assisted fingerprinting localization framework that tightly integrates similarity-based and learning-based paradigms. Specifically, channel charting is employed to project high-dimensional CSI into a low-dimensional latent space, enabling efficient and scalable retrieval of locally correlated reference points (RPs). Building upon the retrieved RPs, a graph attention network (GAT) is designed to explicitly model inter-sample correlations between the query CSI and its associated references, allowing adaptive and geometry-aware feature aggregation for accurate position estimation. Extensive experiments conducted on both real-world indoor and ray-tracing simulated outdoor scenarios demonstrate that the proposed method consistently outperforms state-of-the-art similarity-based and learning-based localization approaches.

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arXiv2026

Exploiting Double-Bounce Paths in Snapshot Radio SLAM: Bounds, Algorithms and Experiments

Radio-based simultaneous localization and mapping (SLAM) has the potential to provide precise user equipment (UE) localization and environmental sensing capabilities by exploiting radio signals. Most existing approaches leverage line-of-sight (LoS) and single-bounce non-line-of-sight (NLoS) paths solely, while higher-order NLoS paths are treated as disturbance. In this paper, we investigate the benefits of leveraging double-bounce NLoS paths for solving the bistatic snapshot radio SLAM problem. We derive the Cramer-Rao bound (CRB) for joint estimation of the UE state and landmark positions when double-bounce NLoS paths are present. In addition, we propose an algorithm to identify double-bounce NLoS paths and leverage them into joint UE and landmarks estimation. The derived bounds are validated through simulated data, and the proposed algorithms are evaluated using experimental millimeter wave (mmWave) measurements harnessing beamformed 5G cellular reference signals. The numerical and experimental results demonstrate that the double-bounce NLoS paths which share at least one incidence point (IP) with the single-bounce NLoS paths improve the estimation accuracy of the UE state and existing IPs of single-bounce NLoS paths. Importantly, exploiting double-bounce NLoS paths enhances environmental mapping capabilities by revealing landmarks that are unobservable with single-bounce NLoS paths alone.

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arXiv2026

RIS Nearfield Position and Velocity Estimation Using a Validated Propagation Model

We investigate reconfigurable intelligent surfaces (RISs) for the task of position and velocity estimation in non-LOS (NLOS) indoor scenarios, using a snapshot based multi-step estimation algorithm. We evaluate a compound RIS structure prototype composed of four RIS tiles with 1-bit phase control per RIS unit cell. Numerical simulation results taking the antenna patterns into account are presented for an 3 m x 3 m area of interest. We demonstrate that the initial grid search step using the far field assumption is not robust enough for small distances to the RIS center and propose a more robust algorithm. Furthermore, we show that the effect of the antenna pattern causes an increased position and velocity error. Our modified three-step algorithm achieves a position error of 7 mm and a velocity error of 0.12 m/s at a distance of 2 m to the RIS center under a realistic numerical propagation model.

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arXiv2026

Phase-Only Positioning in Distributed MIMO Under Phase Impairments: AP Selection Using Deep Learning

Carrier phase positioning (CPP) can enable cm-level accuracy in next-generation wireless systems, while recent literature shows that accuracy remains high using phase-only measurements in distributed MIMO (D-MIMO). However, the impact of phase synchronization errors on such systems remains insufficiently explored. To address this gap, we first show that the proposed hyperbola intersection method achieves highly accurate positioning even in the presence of phase synchronization errors, when trained on appropriate data reflecting such impairments. We then introduce a deep learning (DL)-based D-MIMO antenna point (AP) selection framework that ensures high-precision localization under phase synchronization errors. Simulation results show that the proposed framework improves positioning accuracy compared to prior-art methods, while reducing inference complexity by approximately 19.7%.

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arXiv2026

Optimal Placement of Movable Antennas for Angle-of-Departure Estimation Under User Location Uncertainty

Movable antennas (MA) have gained significant attention in recent years to overcome the limitations of extremely large antenna arrays in terms of cost and power consumption. In this paper, we investigate the use of MA arrays at the base station (BS) for angle-of-departure (AoD) estimation under uncertainty in the user equipment (UE) location. Specifically, we (i) derive the theoretical performance limits through the Cramér-Rao bound (CRB) and (ii) optimize the antenna positions to ensure robust performance within the UE's uncertainty region. Numerical results show that dynamically optimizing antenna placement by explicitly considering the uncertainty region yields superior performance compared to fixed arrays, demonstrating the ability of MA systems to adapt and outperform conventional arrays.

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arXiv2026

Vehicular Wireless Positioning -- A Survey

The rapid advancement of connected and autonomous vehicles has driven a growing demand for precise and reliable positioning systems capable of operating in complex environments. Meeting these demands requires an integrated approach that combines multiple positioning technologies, including wireless-based systems, perception-based technologies, and motion-based sensors. This paper presents a comprehensive survey of wireless-based positioning for vehicular applications, with a focus on satellite-based positioning (such as global navigation satellite systems (GNSS) and low-Earth-orbit (LEO) satellites), cellular-based positioning (5G and beyond), and IEEE-based technologies (including Wi-Fi, ultrawideband (UWB), Bluetooth, and vehicle-to-vehicle (V2V) communications). First, the survey reviews a wide range of vehicular positioning use cases, outlining their specific performance requirements. Next, it explores the historical development, standardization, and evolution of each wireless positioning technology, providing an in-depth categorization of existing positioning solutions and algorithms, and identifying open challenges and contemporary trends. Finally, the paper examines sensor fusion techniques that integrate these wireless systems with onboard perception and motion sensors to enhance positioning accuracy and resilience in real-world conditions. This survey thus offers a holistic perspective on the historical foundations, current advancements, and future directions of wireless-based positioning for vehicular applications, addressing a critical gap in the literature.

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