Search arXivSearch

arXiv · 2609.05803

Comparative Performance of Graphene-Enabled Transmitarray Antenna and Reflectors for Wide-Angle Terahertz Beam Steering

Abstract

This work compares a hemispherical graphene-based transmitarray antenna with its planar reflector counterpart for wide-angle beam steering in the THz regime. The theoretical framework of the planar reflector is formulated and numerically evaluated, yielding an elevation beam-steering range of \pm60°. In contrast, the transmitarray extends the elevation steering range to \pm78° while maintaining full 360° azimuthal coverage. The planar reflector exhibits a larger HPBW variation of 26.48°, compared with 13.1° for the transmitarray, resulting in a broader reflected-beam distribution and reduced directional power density, directivity, and gain. Meanwhile, the transmitarray maintains a more stable and controllable beamwidth response with greater directional power concentration over a wide steering range. The performance advantages of the transmitarray are demonstrated through comparisons with experimental results reported in the literature for planar reflectors and antennas. We further provide a comprehensive assessment of the performance advantages of the transmitarray over planar configurations across the remaining metrics.

Explore related subjects

Keep this discovery

BibTeXRIS

Somayeh Komeylian, Truong Nguyen, Christopher Paolini. 2026-09-05. Comparative Performance of Graphene-Enabled Transmitarray Antenna and Reflectors for Wide-Angle Terahertz Beam Steering. https://arxiv.org/abs/2609.05803

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

AEGIS: Risk-Budgeted Online Scheduling for Resilient Continuous Edge Inference

Continuous edge inference requires sustained wireless and computing support across successive service instances. Under recurring channel degradation, transient edge overload, and multi-user contention, isolated deadline misses may accumulate into persistent service degradation. Existing schedulers mainly optimize instantaneous latency or per-timeslot utility and provide limited control over such cross-time effects. To address this issue, we propose AEGIS (Adaptive Exposure-Governed Inference Scheduling), a risk-budgeted online framework for service-level operational resilience. AEGIS regulates predicted deadline-risk exposure through dynamically replenished per-user risk budgets and establishes an explicit finite-horizon bound on cumulative admitted-risk exposure. One-step state estimation supports anticipatory delay and risk assessment, while the centralized bandwidth--computing allocation is transformed into an exact-potential formulation and solved through asynchronous coordinate updates. Simulation results demonstrate that AEGIS enhances timely-service continuity, contains persistent deadline violations, and improves post-stress recovery through adaptive cross-time risk regulation. Meanwhile, it effectively controls predicted-risk exposure while preserving competitive service performance, achieving a favorable balance between service resilience and risk control.

cs.NI

Enhancing Network Resilience via Graph-Based Anomaly Detection in Sovereign Functions

Sovereign network functions, e.g., routing protocols, are becoming increasingly complex and susceptible to failures arising from protocol configuration anomalies and anomalous configurations. This paper interprets the protocol configuration anomaly detection problem as detection of structural inconsistencies of connected nodes and edges in a bipartite graph that captures both physical network entities and logical protocol states. This graph structural inconsistency detector (GSID) model is proposed to solve the problem efficiently. To handle the heterogeneous nature of protocol configuration parameters, GSID employs an adaptive configuration encoder (ACE) that dynamically selects encoding strategies per parameter to preserve fine-grained numerical discrepancies. To expose the subtle inconsistencies of connected nodes and edges in the bipartite graph, GSID uses an inconsistency dynamic attention (IDA) mechanism that scores edges by drawing asymmetric attentions from both ends, rule compliance from one end and route connectivity from the other. It is demonstrated experimentally that GSID outperforms state-of-the-art baselines by threefold in F1 score and by 23.2% in accuracy. Ablation studies validate the effectiveness of both the ACE and IDA modules. Tests on unseen network scales and real-world network topologies show the superior adaptability of our GSID, compared to the baselines.

cs.NI