arXiv subjects

Rıfat Volkan Şenyuva

Publications and source records attributed to Rıfat Volkan Şenyuva.

7 recordsLinked to original sources

arXiv2026

Wideband Compressed-Domain Cramér-Rao Bounds for Near-Field XL-MIMO: Data and Geometric Diversity Decomposition

Wideband orthogonal frequency-division multiplexing (OFDM) over near-field extremely large-scale MIMO (XL-MIMO) arrays couples beam squint and wavefront curvature, and existing single-frequency compressed covariance models are severely biased as a result. To the best of our knowledge, no compressed-domain Cramér-Rao bound (CRB) has been reported for this regime under hybrid analog-digital architectures; existing wideband near-field bounds assume full-array observation. We derive the wideband compressed-domain CRB and decompose its Fisher information gain into two terms: a dominant data-diversity term, exactly 10 log10(K_s) dB, where K_s denotes the number of independent subcarrier observations, and we prove that for a centred array the full-array range bound is invariant to beam squint, so the full-array wideband gain equals the data-diversity term to within 3x10^-4 dB. Under hybrid compression, however, this invariance breaks: the residual is a combiner-dependent fluctuation of either sign rather than a propagation-geometry effect, with mean +0.157 dB and standard deviation 0.767 dB over 385 realizations at N_RF = 16, B = 400 MHz, and its spread shrinks toward zero as the number of RF chains grows. At 28 GHz with B = 400 MHz, data diversity contributes +27.1 dB and hybrid compression contributes an additional 12.6 dB gap relative to the full-array bound at N_RF = 16 RF chains. Frequency-aware covariance modeling is therefore the dominant requirement, and the residual is a property of the analog front end rather than of the propagation geometry.

eess.SP↗

arXiv2026

Wideband Near-Field Channel Estimation Under Hybrid Compression: Cross-Subcarrier KL Covariance Fitting With OFDM Fresnel Model

We consider wideband channel estimation for extremely large-scale multiple-input multiple-output (XL-MIMO) arrays under hybrid analog-digital compression, in which a uniform linear array (ULA) is observed through far fewer radio-frequency (RF) chains than antennas. At a carrier frequency of 28 GHz with bandwidths reaching several hundred MHz, the standard narrowband polar-domain channel model fails: the near-field Fresnel curvature becomes subcarrier-dependent, and the compressed observation destroys the per-subcarrier spatial covariance structure that narrowband methods exploit. We propose the Wideband Cross-subcarrier Kullback--Leibler (WB-CL-KL) estimator, which jointly estimates angle and range directly from the compressed sample covariance, without full-array reconstruction, by fitting a structured Fresnel covariance model across orthogonal frequency-division multiplexing (OFDM) subcarriers via a cross-subcarrier Kullback--Leibler (KL) divergence criterion. We also derive the wideband compressed-domain Cramér--Rao bound (CRB) -- the performance lower bound for this hybrid architecture -- from the Slepian--Bangs formula, and decompose its gain over the narrowband bound into a data-diversity component of +27.093 dB and a geometric-diversity component of +0.701 dB, totalling +27.793 dB at B = 400 MHz (Propositions 1 and 2). In the single-path line-of-sight regime, WB-CL-KL attains a range root-mean-square error of 19.8 mm against a 19.9 mm bound at signal-to-noise ratio (SNR) = 10 dB, a ratio of 0.996. Under the 3GPP Urban Micro (UMi) path-loss and shadow-fading SNR distribution, it achieves a bound ratio of 0.959 at the median deployment SNR of 9.6 dB, indicating near-CRB operation at the representative deployment point, where the compressed-domain bound is evaluated at the scene-median geometry.

cs.IT↗

arXiv2026

Covariance-Guided DFT Beam Selection for Beamspace ESPRIT in Hybrid mmWave Sensor Arrays

Accurate direction-of-arrival estimation with hybrid analog--digital millimeter-wave sensor arrays is important for localization, environment sensing, and measurement beam control for sensing applications. However, the limited number of radio-frequency chains and training beams in practical hardware makes it difficult to approach the angular resolution of fully digital arrays. This paper develops a covariance-guided discrete Fourier transform (DFT) beam selection framework tailored to beamspace ESPRIT for hybrid millimeter-wave receivers. A short hybrid training phase realizes a virtual centro-symmetric subarray and yields a sample covariance that is processed by forward--backward averaging, nonnegative least-squares power and noise fitting, and a Toeplitz positive-semidefinite projection to reconstruct a denoised full-aperture covariance matrix. This covariance is then used to score and select, within each coarse sector, small contiguous blocks of DFT beams that concentrate signal energy and preserve effective aperture under a strict beam budget. The selected beams feed a sparse beamspace ESPRIT stage that operates only on actually available adjacent beam pairs, so that the overall complexity is dominated by a single low-dimensional ESPRIT call. Monte Carlo simulations for a thirty-two-element uniform linear array with three paths indicate that, in the considered scenarios, the proposed method can reduce the gap to the Cramér--Rao bound, lower the failure rate, and provide favorable accuracy--runtime trade-offs compared with a sectorization-based baseline built from the same codebook and estimator. For the unitary DFT codebook studied here, the fine-stage beam selector reduces to a covariance-guided contiguous energy-window rule; the broader score formulation also accommodates non-unitary effective beam dictionaries arising from hardware non-idealities.

eess.SP↗

arXiv2026

Covariance-Domain Near-Field Channel Estimation under Hybrid Compression: USW/Fresnel Model, Curvature Learning, and KL Covariance Fitting

Near-field propagation in extremely large aperture arrays requires joint angle-range estimation. In hybrid architectures, only $N_\mathrm{RF}\ll M$ compressed snapshots are available per slot, making the $N_\mathrm{RF}\times N_\mathrm{RF}$ compressed sample covariance the natural sufficient statistic. We propose the Curvature-Learning KL (CL-KL) estimator, which grids only the angle dimension and \emph{learns the per-angle inverse range} directly from the compressed covariance via KL divergence minimisation. CL-KL uses a $Q_θ$-element dictionary instead of the $Q_θQ_r$ atoms of 2-D polar gridding, eliminating the range-dimension dictionary coherence that plagues polar codebooks in the strong near-field regime, and operates entirely on the compressed covariance for full compatibility with hybrid front-ends. At $N_\mathrm{MC}=400$ ($f_c=28$~GHz, $M=64$, $N_\mathrm{RF}=8$, $N=64$, $d=3$, $r\in[0.05,1.0]\,r_\mathrm{RD}$), CL-KL achieves the lowest channel NMSE among all six evaluated methods -- including four full-array baselines using $64\times$ more data -- at $\mathrm{SNR}\in\{-5,0,+5,+10\}$~dB. Running in approximately 70~ms per trial (vs.\ 5~ms for the compressed-domain peer P-SOMP), CL-KL's dominant cost is the $N_\mathrm{RF}{\times}N_\mathrm{RF}$ inversion rather than $M$: measured runtime stays near 70~ms across $M\in\{32,64,128,256\}$, making it aperture-scalable for XL-MIMO deployments. CL-KL is further validated against a derived compressed-domain Cramér-Rao bound and confirmed robust to non-Gaussian (QPSK) source distributions, with a maximum NMSE gap below 0.6~dB.

eess.SP↗

arXiv2023

Channel Estimation for RIS aided MISO System

This paper considers the channel estimation of a single user in a MISO system with an intelligent reflecting surface (IRS). The performances of the minimum variance unbiased (MVU) and minimum mean square error (MMSE) estimators using the discrete Fourier transform activation pattern for the IRS, updated at every symbol interval, are compared. Numerical results show that the MMSE estimator provides over 10 dB SNR improvement compared to the MVU estimator.

cs.IT↗

arXiv2023

Union Bounds on the Symbol Error Probability of LoRa Modulation for Flat Rician Block Fading Channels

In this paper the symbol error performance of LoRa modulation is addressed for flat Rician block fading channels. First the exact symbol error probability of the LoRa modulation on Rician fading is derived. Then the upper and lower union bounds are employed on the derived symbol error probability. The proposed bounds are compared against the exact symbol error probability, the numerical evaluation of the symbol error probability and the state-of-art approximation of the LoRa symbol error probability. Numerical results show that while the proposed upper bound is very tight to the exact symbol error probability, there is approximately a 2.5 dB gap for the lower bound.

cs.IT↗

arXiv2021

Harmonic Retrieval of CFO and Frame Misalignment for OFDM-based Inter-Satellite Links

As dense low Earth orbit (LEO) constellations are being planned, the need for accurate synchronization schemes in high-speed environments remains a challenging problem to tackle. To further improve synchronization accuracy in channeling environments, which can also be applied in the LEO networks, we present a new method for estimating the carrier frequency offset (CFO) and frame misalignment in orthogonal frequency division multiplexing (OFDM) based inter-satellite links. The proposed method requires the transmission of pilot symbols to exploit 2-D estimation of signal parameters via rotational invariance techniques (ESPRIT) and estimate the CFO and the frame misalignment. The Cramer-Rao lower bounds (CRLB) of the joint estimation of the CFO and frame misalignment are also derived. Numerical results show that the difference between the proposed method and the state-of-art method is less than 5dB at its worse.

eess.SP↗