CORTEXA
← Browse
arxiveess.SPcs.NI2026-07-08

Bessel Beam Optimization for Near-Field THz Communications under UE Location Uncertainty

Aditya Jolly, Vitaly Petrov, Gábor Fodor, Emil Björnson

To achieve the desired coverage and capacity levels, future terahertz (THz) wireless systems are envisioned to utilize extremely large antenna arrays. At THz frequencies, the combination of short wavelengths and large array apertures often makes many of the conventional far-field assumptions invalid in practice. As a result, many UEs operate in the radiative near-field zone, where novel near-field beam synthesis methods become viable. This paper studies phase-only Bessel-like near-field beam configurations for downlink THz multiple-input multiple-output links under imperfect UE location knowledge. We first formulate a spectral efficiency maximization problem with respect to the "Bessel cone angle''. We then derive low-complexity closed-form approximations for the optimal Bessel beam configuration for: (i)deterministic UE location; (ii)Gaussian and (iii)uniform error in the UE location. Finally, through extensive simulations across multiple signal frequencies, UE locations, and array sizes, we show that our proposed simple closed-form approximations closely match (under 0.1% difference) the best performance achieved via exhaustive search, while simultaneously reducing the configuration complexity down to as low as O(1).

View free PDFSource page

Related papers

arxiveess.SPphysics.app-ph2026-07-24

Noise-Robust Frequency Estimation via Overlapped Sampling-Intervals Zero-Crossing Fitting

Phichai Youplao, Nithiroth Pornsuwancharoen, Yusaku Fujii

The trade-off between noise averaging and temporal resolution fundamentally limits conventional zero-crossing frequency estimators under dynamic and noisy conditions. This paper presents an overlapped sampling-intervals zero-crossing fitting method (OS-ZFM), which introduces a st…

View free PDFSource page
arxiveess.SP2026-07-24

Continuous Intra-Symbol Phase Noise Tracking for THz OFDM via Polynomial Reconstruction

Sawatsakorn Chaiyasoonthorn, Ura Klongklaew, Phichai Youplao

Terahertz (THz) communication systems for sixth-generation (6G) networks are severely impaired by Wiener phase noise (WPN), whose innovation variance at sub-THz carriers is substantially larger than in millimeter-wave 5G systems. Conventional common-phase-error (CPE) compensation…

View free PDFSource page
arxiveess.SP2026-07-24

Innovation-Domain Decision-Directed Phase Tracking for Wiener Phase Noise in Fast Rayleigh Fading

Ura Klongklaew, Nithiroth Pornsuwancharoen, Phichai Youplao

This letter proposes an innovation-domain decision-directed phase tracking (ID-DDPT) architecture for coherent detection over Rayleigh fading channels with temporally correlated phase evolution and Wiener phase noise. By reformulating phase tracking into the innovation domain, re…

View free PDFSource page
arxiveess.SP2026-07-24Cited by 83

Ultra-wideband statistical propagation channel model for implant sensors in the human chest

Ali Khaleghi, Raúl Chávez-Santiago, Ilangko Balasingham

Implant medical wireless sensors for monitoring physiological parameters, automatic drug provision, and so on represent a new promising healthcare technology. Inherent characteristics of ultra-wideband (UWB) radio make this technology highly suitable for the wireless interface of…

View free PDFSource page
arxiveess.SP2026-07-24Cited by 132

Propagation models for IEEE 802.15.6 standardization of implant communication in body area networks

Raul Chavez-Santiago, Kamran Sayrafian-Pour, Ali Khaleghi, Kenichi Takizawa, Jianqing Wang, Ilangko Balasingham, et al.

A body area network is a radio communication protocol for short-range, low-power, and highly reliable wireless communication for use on the surface, inside, or in the peripheral proximity of the human body. Combined with various biomedical sensors, BANs enable realtime collection…

View free PDFSource page
arxivcs.NIcs.MAeess.SY2026-07-24

Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks

Chuan-Chi Lai, Ang-Hsun Tsai

This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Awa…

View free PDFSource page