arXiv subjects

Erfan Khordad

Publications and source records attributed to Erfan Khordad.

8 recordsLinked to original sources

arXiv2026

Deep Learning-Based Computer Vision for Beam Selection and Proactive Blockage Prediction

Millimeter-wave communication faces two critical challenges: propagation losses requiring costly narrow-beam alignment, and penetration losses causing link failures from blocked line-of-sight paths. We address propagation loss through a novel vision-aided beam selection framework that integrates RGB imagery with received power profiles for efficient transmitter identification and beam prediction. This framework achieves 98.96% top-5 beam prediction accuracy, surpassing current state-of-the-art methods by at least 6% across all metrics. We address penetration loss through a proactive blockage prediction framework using a modified object tracker with weighted centroid-based depth estimation. This represents the first analysis of simultaneous non-uniform mobility of both transmitters and obstacles. Evaluated on completely unseen data, this framework achieves over 98% accuracy in predicting blockages up to three frames ahead, establishing strong performance benchmarks.

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arXiv2026

Beam Squinting Effects in Super Wideband Communication Systems

Beam squint, the frequency-dependent shift of the main beam, poses a major challenge for wideband antenna arrays. This paper focuses on the beam squint effects in super wideband (SW) systems, where high mutual coupling (MC) effects are present. These high MC effects complicate beamforming (BF) by creating frequency-dependent phase relationships that invalidate conventional approaches. To accurately model MC effects, this paper uses a circuit-theoretic framework for tightly coupled SW uniform linear arrays (ULAs). We derive closed-form expressions for the average received signal-to-noise ratio (SNR) with BF in conventional half-wavelength spaced, weakly coupled arrays and validate them. Extending our analysis to tightly coupled SW arrays, we demonstrate that, in contrast to conventional weakly coupled arrays, the effective true time delays exhibit a nonlinear dependence on frequency due to coupling-induced phase shifts. A comparative analysis reveals that strong MC in SW arrays significantly reduces squint in phase-controlled BF, extending the usable bandwidth considerably.

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arXiv2026

Joint Max-Min Power Control and Clustering in Cell-Free Wireless Networks: Design and Analysis

Cell-free wireless networks have attracted significant interest for their ability to eliminate cell-edge effects and deliver uniformly high service quality through macro-diversity. In this paper, we develop an algorithm to jointly optimize uplink transmit powers and dynamic user-centric access point (AP) clusters in a centralized cell-free network. This approach aims to efficiently mitigate inter-user interference and achieve higher max-min signal-to-interference-plus-noise ratio (SINR) targets for users. To this end, we re-purpose an iterative power control algorithm based on non-linear Perron-Frobenius theory and prove its convergence for the maximum ratio combiner (MRC) receiver under various AP subset selection schemes. We further provide analytical results by framing the joint optimization as a conditional eigenvalue problem with power and AP association constraints, and leveraging Perron-Frobenius theory on a centrally constructed matrix. The numerical results highlight that optimizing each user's serving AP cluster is essential to achieving higher max-min SINR targets with the simple MRC receiver.

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arXiv2025

Target Detection with Tightly-coupled Antennas: Analysis for Unknown Wideband Signals

This paper presents analysis for target detection using tightly-coupled antenna (TCA) arrays with high mutual coupling (MC). We show that the wide operational bandwidth of TCAs is advantageous for target detection. We assume a sensing receiver equipped with a TCA array that collects joint time and frequency samples of the target's echo signals. Echoes are assumed to be unknown wideband signals, and noise at the TCA array follows a frequency-varying correlation model due to MC. We also assume that the echo signals are time varying, with no assumption on the temporal variation. We consider three regimes in frequency as constant, slowly or rapidly varying, to capture all possible spectral dynamics of the echoes. We propose a novel detector for the slowly-varying regime, and derive detectors based on maximum likelihood estimation (MLE) for the other regimes. For the rapidly-varying regime, we derive an extended energy detector for correlated noise with frequency and time samples. We analyze the performance of all the detectors. We also derive and analyze an ideal detector giving an upper bound on performance. We validate our analysis with simulations and demonstrate that our proposed detector outperforms the MLE-based detectors in terms of robustness to frequency variation. Also, we highlight that TCA arrays offer clear advantages over weakly-coupled antenna arrays in target detection.

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arXiv2025

Rician Channel Modelling for Super Wideband MIMO Communications

Recent developments in Multiple-Input-Multiple-Output (MIMO) technology include packing a large number of antenna elements in a compact array to access the bandwidth benefits provided by higher mutual coupling (MC). The resulting super-wideband (SW) systems require a circuit-theoretic framework to handle the MC and channel models which span extremely large bands. Hence, in this paper, we make two key contributions. First, we develop a physically-consistent Rician channel model for use with SW systems. Secondly, we express the circuit-theoretic models in terms of a standard MIMO model, so that insights into the effects of antenna layouts, MC, and bandwidth can be made using standard communication theory. For example, we show the bandwidth widening resulting from the new channel model. In addition, we show that MC distorts line-of-sight paths which has beamforming implications. We also highlight the interaction between spatial correlation and MC and show that tight coupling reduces spatial correlations at low frequencies.

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arXiv2024

Deep Learning based Computer-vision for Enhanced Beamforming

Meeting the high data rate demands of modern applications necessitates the utilization of high-frequency spectrum bands, including millimeter-wave and sub-terahertz bands. However, these frequencies require precise alignment of narrow communication beams between transmitters and receivers, typically resulting in significant beam training overhead. This paper introduces a novel end-to-end vision-aided beamforming framework that utilizes images to predict optimal beams while considering geometric adjustments to reduce overhead. Our model demonstrates robust adaptability to dynamic environments without relying on additional training data where the experimental results indicate a top-5 beam prediction accuracy of 98.96%, significantly surpassing current state-of-the-art solutions in vision-aided beamforming.

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arXiv2019

A Kronecker-Based Sparse Compressive Sensing Matrix for Millimeter Wave Beam Alignment

Millimeter wave beam alignment (BA) is a challenging problem especially for large number of antennas. Compressed sensing (CS) tools have been exploited due to the sparse nature of such channels. This paper presents a novel deterministic CS approach for BA. Our proposed sensing matrix which has a Kronecker-based structure is sparse, which means it is computationally efficient. We show that our proposed sensing matrix satisfies the restricted isometry property (RIP) condition, which guarantees the reconstruction of the sparse vector. Our approach outperforms existing random beamforming techniques in practical low signal to noise ratio (SNR) scenarios.

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arXiv2019

Rate Balancing in Full-Duplex MIMO Two-Way Relay Networks

Maximizing the minimum rate for a full-duplex multiple-input multiple-output (MIMO) wireless network encompassing two sources and a two-way (TW) relay operating in a two hop manner is investigated. To improve the overall performance, using a zero-forcing approach at the relay to suppress the residual self-interference arising from full-duplex (FD) operation, the underlying max-min problem is cast as an optimization problem which is non-convex. To circumvent this issue, semidefinite relaxation technique is employed, leading to upper and lower bound solutions for the optimization problem. Numerical results verify that the upper and lower bound solutions closely follow each other, showing that the proposed approach results in a close-to-optimal solution. In addition, the impact of residual self-interference upon the overall performance of the network in terms of the minimum rate is illustrated by numerical results, and for low residual self-interference scenarios the superiority of the proposed method compared to an analogous half-duplex (HD) counterpart is shown.

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