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Volume 13 | Issue 6 | Year 2026 | Article Id. IJECE-V13I6P106 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I6P106

Physics-Constrained Inverse Antenna Design with Hardware-Deployable Intelligence for Real-Time IoT Systems

C.Rajan, M. Kavitha, Shaik Sadulla

Received Revised Accepted Published
08 Mar 2026 07 Apr 2026 06 May 2026 27 Jun 2026

Citation :

C.Rajan, M. Kavitha, Shaik Sadulla, "Physics-Constrained Inverse Antenna Design with Hardware-Deployable Intelligence for Real-Time IoT Systems," International Journal of Electronics and Communication Engineering, vol. 13, no. 6, pp. 13-6, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I6P106

Abstract

Internet of Things (IoT) platforms require compact and adaptive antennas to work within a tight size, power and cost sensitive set of constraints. The traditional design of an antenna is based on the forward Electromagnetic (EM) optimization, which is computationally expensive an d c an not be used in real time adjustment. Intelligent methods based only on data, though efficient, do not always take into consideration physical aspects and the deployability of hardware. The paper suggests a physics constrained system of inverse antenna design where intelligent inference is applied as a solution enabling methodology, instead of the new paradigm. The antenna desired specifications are invertedly synthesized t o physically valid antenna design parameters by an inverse synthesis method which explicitly applies EM, fabrication and deployment constraints. The proposed architecture combines a lightweight, physically deployable inference engine and a constraint cer tification loop based on physics, which is safe and reliable to generate the antenna. The inverse synthesis accuracy is verified using electromagnetic simulation on a s et of patch antenna s on a microstrip basis, where the mean relative error of resonant fr equency and resonant |human| at resonance were 0.25 GHz and 2.5 dB, respectively. The certification loop of physics also discards non feasible geometries, but the rate of constraint violation is lessened to 21 per cent (unconstrained inverse) and 0 per cen t (proposed). Hardware deployability is also exhibited by the 8 bit fixed point quantization of the inference engine and 12,000 parameters, which needs 48 kB of memory and 0.15 ms inference latency, and supports real time on device synthesis on an IoT plat form.

Keywords

Inverse Antenna Design Physics Constrained Intelligence Hardware Deployable Inference Iot Antennas Real Time Antenna Tuning Vlsi Aware Intelligent Systems

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