Drlt-Net: deep RNN-LSTM telemetry network for adaptive multiband UWB implantable pacemaker communication

The paper proposes a multiband ultra-wideband adaptive antenna with a deep recurrent neural network long short-term memory (RNN-LSTM) algorithm to enable reliable telemetry for implantable pacemakers. It is proposed that electromagnetic optimization can be combined with data-driven adaptation in the proposed design in order to ensure stable resonance and low signal distortion under different tissue conditions. A 2.4 and 5.8–9 GHz compact fractal patch antenna in biocompatible polycarbonate material is fabricated to provide high efficiency and low specific absorption rate of its radiation. The RNN-LSTM network is able to predict time-varying impedance drift and apply the adaptive retuning of antenna parameters in a closed-loop control loop to allow communication over long durations with low latency. Electromagnetic modeling does verify a return loss of −41.2, a 6.5 dBi gain, radiation efficiency of 88, and an Alexander report of 0.72 W/kg to be within the safety thresholds of IEEE C95.1. The smart feedback system offers 99% telemetry error and latency of under 8 ms improvements, compared with the conventional methods of tuning that are static. The findings show that the proposed hybrid solution would greatly boost signal integrity, safety, and energy usage, which is a viable breakthrough to the next generation of adaptable biomedical communication systems.