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Hüseyin Yiğitler

Publications and source records attributed to Hüseyin Yiğitler.

14 recordsLinked to original sources

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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Backscatter Assisted Indoor NLOS Positioning

Passive backscatter devices (BDs) can enable indoor non-line-of-sight (NLOS) positioning by serving as virtual anchors whose Doppler-separated signatures are observable in standard channel estimates. This paper studies continuous user-equipment (UE) tracking in corridor environments using a noncoherent power-domain formulation that avoids BD phase synchronization and remains robust to residual carrier offsets and strong multipath. The BD-dependent measurements are modeled by a log-distance law with unknown BD-specific offsets, which allows passive asynchronous devices to be used as anchors without transmit-power calibration. Based on this model, we develop a corridor-constrained maximum a posteriori (MAP) tracker with motion regularization and Huber-robust estimation. In ray-tracing-inspired simulations, the method achieves median positioning errors of 0.23--0.27 m with 90th-percentile errors below 0.45 m. In office-corridor measurements with four passive BDs at 866 MHz, it attains an aggregated median error of 0.505 m and outperforms a simple weighted-average baseline. The results show that passive asynchronous BDs can provide practical sub-meter indoor NLOS tracking while remaining compatible with existing channel-estimation pipelines and energy-autonomous BD deployments.

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Self-Calibrated Indoor Tracking from Backscatter Fiducials under NLOS Transmitter Illumination

This paper studies indoor tracking from wall-mounted backscatter fiducials in corridor segments outside direct transmitter illumination. In the measured setup, the transmitter-to-fiducial links are NLOS, whereas the fiducial-to-receiver links along the corridor are largely LOS. The main challenge is that the effective fiducial response is deployment-dependent, so a fixed calibrated link budget is not reliable. We therefore use a grid-based penalized-likelihood tracker that profiles the receiver path, a fitted log-distance slope parameter, and fiducial-specific offsets directly from received powers. The resulting paths can then be reused as surrogate calibration coordinates for residual-map correction, while the same correction with measured calibration coordinates is reported only as a reference. On a short four-fiducial corridor segment, the profiled dual-band tracker gives a 0.52 m median error without measured calibration coordinates, and surrogate residual correction improves this to 0.46 m. With measured calibration coordinates, the same correction and a RADAR-style fingerprint reference both reach 0.31 m. The main remaining limitation is therefore the quality of the surrogate calibration paths rather than the structured observation model itself.

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Adaptive Internal Calibration for Temperature-Robust mmWave FMCW Radars

We present a novel internal calibration framework for Millimeter- Wave (mmWave) Frequency-Modulated Continuous-Wave (FMCW) radars to ensure robust performance under internal temperature variations, tailored for deployment in dense wireless networks. Our approach mitigates the impact of temperature-induced drifts in radar hardware, enhancing reliability. We propose a temperature compensation model that leverages internal sensor data and signal processing techniques to maintain measurement accuracy. Experimental results demonstrate improved robustness across a range of internal temperature conditions, with minimal computational overhead, ensuring scalability in dense network environments. The framework also incorporates ethical design principles, avoiding reliance on sensitive external data. The proposed scheme reduces the Pearson correlation between the amplitude of the Intermediate Frequency (IF) signal and internal temperature drift up to 84%, significantly mitigating the temperature drift.

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Non-Invasive Arterial Pulse Detection with Millimeter-wave Radar and Comparison With Photoplethysmography

Cardiovascular diseases remain a leading cause of mortality and disability. The convenient measurement of cardiovascular health using smart systems is therefore a key enabler to foster accurate and early detection and diagnosis of cardiovascular diseases and it require accessing a correct pulse morphology similar to arterial pressure wave. This paper investigates the comparison between different sensor modalities, such as mmWave and photoplethysmography from the same physiological site and reference continuous non-invasive blood pressure devide. We have developed a hardware prototype and established an experiment consist of 23 test participants. Both mmWave and PPG are capable of detecting inter-beat intervals. mmWave is providing more accurate arterial pulse waveform than green photoplethysmography.

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Coherent Multi-antenna Receiver for BPSK-modulated Ambient Backscatter Tags

Ambient Backscatter Communication (AmBC) is an emerging communication technology that can enable green Internet-of-Things deployments. The widespread acceptance of this paradigm is limited by low Signal-to-Interference-Plus-Noise Ratio (SINR) of the signal impinging on the receiver antenna due to the strong direct path interference and unknown ambient signal. The adverse impact of these two factors can be mitigated by using non-coherent multi-antenna receivers, which is known to require higher SINR to reach Bit-Error-Rate (BER) performance of coherent receivers. However, in literature, coherent receivers for AmBC systems are little-studied because of unknown ambient signal, unknown location of AmBC tags, and varying channel conditions. In this paper, a coherent multi-antenna receiver, which does not require a prior information of the ambient signal, for decoding Binary-Phase-shift-Keying (BPSK) modulated signal is presented. The performance of the proposed receiver is compared with the ideal coherent receiver that has a perfect phase information, and also with the performance of non-coherent receiver, which assumes distributions for ambient signal and phase offset caused by excess length of the backscatter path. Comparative simulation results show the designed receiver can achieve the same BER-performance of the ideal coherent receiver with 1-dB more SINR, which corresponds to 5-dB or more gain with respect to non-coherent reception of On-Off-Keying modulated signals. Variation of the detection performance with the tag location shows that the coverage area is in the close vicinity of the transmitter and a larger region around the receiver, which is consistent with the theoretical results.

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Optimum Multi-Antenna Ambient Backscatter Receiver for General Binary-Modulated Signal

Ambient backscatter communication (AmBC) is becoming increasingly popular for enabling green communication amidst the continual development of the Internet-of-things paradigm. Efforts have been put into backscatter signal detection as the detection performance is limited by the low signal-to-interference-plus-noise ratio (SINR) of the signal at the receiver. The low SINR can be improved by adopting a multi-antenna receiver. In this paper, the optimum multi-antenna receiver that does not impose any constraints on the types of binary modulation performed by the backscatter device and the waveform used by the ambient source system is studied. The proposed receiver owns a simple structure formed by two beamformers. Bit error rate (BER) performances of the optimum receiver are derived under constant-amplitude ambient signal and Gaussian-distributed ambient signal. Moreover, to facilitate the implementation of the optimum receiver, a simplified receiver is proposed and practical approximations to required beamformers are provided. The derived optimum receiver avoids the complex direct path interference cancellation and coherent reception, but exploits the fact that backscatter signal changes the composite channel impinging at the receiver and the directivity of receiver antenna array. Comparative simulation results show that the performance of the optimum receiver achieves the same performance as the coherent receiver even though it realizes non-coherent reception. The studied receivers provide high flexibility for implementing simple and low-cost receivers in different AmBC systems.

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Hybrid Beamformer Design for High Dynamic Range Ambient Backscatter Receivers

In bi-static Ambient Backscatter Communications (AmBC) systems, the direct path from the ambient source to the receiver can be several orders of magnitude stronger than the scattered path modulated by the AmBC device. Because of the large power difference between these two signals, the receiver needs to operate at a large dynamic range. In this paper, we propose a novel analog-digital hybrid null-steering beamformer which allows the backscatter receiver to detect and decode the weak AmBC-modulated signal buried in the strong direct path signals and the noise without requiring the instantaneous channel state information. The analog cancellation of the strong signal components allow the receiver automatic gain control to adjust to the level of the weak AmBC signals. This hence allows common analog-to-digital converters to be used for sampling the signal. After cancelling the strong components, the ambient source signal appears as zero mean fast fading from the AmBC system point of view. We use the direct path signal component to track the phase of the unknown ambient signal. In order to avoid channel estimation, we propose AmBC to use orthogonal channelization codes.

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RSS Models for Respiration Rate Monitoring

Received signal strength based respiration rate monitoring is emerging as an alternative non-contact technology. These systems make use of the radio measurements of short-range commodity wireless devices, which vary due to the inhalation and exhalation motion of a person. The success of respiration rate estimation using such measurements depends on the signal-to-noise ratio, which alters with properties of the person and with the measurement system. To date, no model has been presented that allows evaluation of different deployments or system configurations for successful breathing rate estimation. In this paper, a received signal strength model for respiration rate monitoring is introduced. It is shown that measurements in linear and logarithmic scale have the same functional form, and the same estimation techniques can be used in both cases. The implications of the model are validated under varying signal-to-noise ratio conditions using the performances of three estimators: batch frequency estimator, recursive Bayesian estimator, and model-based estimator. The results are in coherence with the findings, and they imply that different estimators are advantageous in different signal-to-noise ratio regimes.

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On the Achievable Rate of Bi-Static Modulated Re-Scatter Systems

In ambient re-scatter communications, devices convey information by modulating and re-scattering the radio frequency signals impinging on their antennas. In this correspondence, we consider a system consisting of a legacy modulated continuous carrier multiple-input-multiple-output (MIMO) link and a multi-antenna modulated re-scatter (MRS) node, where the MRS node modulates and re-scatters the signal generated by the legacy transmitter. The receiver seeks to decode both the original message and the information added by the MRS. We show that the achievable sum rate of this system exceeds that which the legacy system could achieve alone. We further consider the impact of channel estimation errors under the least squares channel estimation and study the achievable rate of the legacy and MRS systems, where a linear minimum mean square error receiver with successive interference cancellation is utilized for joint decoding.

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Detector Based Radio Tomographic Imaging

Received signal strength based radio tomographic imaging is a popular device-free indoor localization method which reconstructs the spatial loss field of the environment using measurements from a dense wireless network. Existing methods solve an associated inverse problem using algebraic or compressed sensing reconstruction algorithms. We propose an alternative imaging method that reconstructs spatial field of occupancy using a back-projection based reconstruction algorithm. The introduced system has the following advantages over the other imaging based methods: i.) significantly lower computational complexity such that no floating point multiplication is required; ii.) each link's measured data is compressed to a single bit, providing improved scalability; iii.) physically significant and repeatable parameters. The proposed method is validated using measurement data. Results show that the proposed method achieves the above advantages without loss of accuracy compared to the other available methods.

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Enhancing the Accuracy of Device-free Localization Using Spectral Properties of the RSS

Received signal strength based device-free localization has attracted considerable attention in the research society over the past years to locate and track people who are not carrying any electronic device. Typically, the person is localized using a spatial model that relates the time domain signal strength measurements to the person's position. Alternatively, one could exploit spectral properties of the received signal strength which reflects the rate at which the wireless propagation medium is being altered, an opportunity that has not been exploited in the related literature. In this paper, the power spectral density of the signal strength measurements are related to the person's position and velocity to augment the particle filter based tracking algorithm with an additional measurement. The system performance is evaluated using simulations and validated using experimental data. Compared to a system relying solely on time domain measurements, the results suggest that the robustness to parameter changes is increased while the tracking accuracy is enhanced by 50% or more when 512 particles are used.

cs.NI↗

Detecting Human-induced Reflections using RSS of Narrowband Wireless Transceivers

Radio frequency sensor networks are becoming increasingly popular as an indoor localization and monitoring technology for gaining unobtrusive situational awareness of the surrounding environment. The localization effort in these networks is built upon the well-established fact that the received signal strength measurements vary due to a person's presence on the line-of-sight of a transmitter-receiver pair. To date, modeling this decrease in received signal strength and utilizing it for localization purposes have received a considerable amount of attention in the research field. However, when the person is in the close vicinity of the line-of-sight but not obstructing it, the signal reflected from the human body is also affecting the received signal strength and can be used for occupancy assessment purposes. In this paper, we first model the effect of human-induced reflections as a function of communication frequency, and then use the model as a basis for energy based occupancy detection. The derived methods are evaluated numerically and the detection probability of the proposed detector is validated with experimental data. The results suggest that when more than eight frequency channels are utilized, presence of a person can be detected using RSS measurements of a single transmit-receive pair with detection probability higher than 0.95 and false alarm probability less than 0.01 in an area of 2 m x 2.5 m. Moreover, the important implications of the studied methods on the available narrowband radio frequency sensor network applications are discussed in detail.

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A Three-State Received Signal Strength Model for Device-free Localization

The indoor radio propagation channel is typically modeled as a two-state time-variant process where one of the states represents the channel when the environment is static, whereas the other state characterizes the medium when it is altered by people. In this paper, the aforementioned process is augmented with an additional state. It is shown that the changes in received signal strength are dictated by: i) electronic noise, when a person is not present in the monitored area; ii) reflection, when a person is moving in the close vicinity of the line-of-sight; iii) shadowing, when a person is obstructing the line-of-sight component of the transmitter-receiver pair. Statistical and spatial models for the three states are derived and the models are empirically validated. Based on the models, a simplistic device-free localization application is designed which aims to: first, estimate the temporal state of the channel using a hidden Markov model; second, track a person using a particle filter. The results suggest that the tracking accuracy is enhanced by at least 65% while the link's sensitivity region is increased by 100% or more with respect to empirical models presented in earlier works.

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