Toward Wireless System and Circuit Co-Design for the Internet of Self-Adaptive Things

TL;DR

The paper addresses the need for continuous, real-time adaptation of wireless parameters in IoT by proposing a co-design framework that couples end-to-end wireless system models with transistor-level RFIC non-idealities. It advances the field with an architecture that integrates a reconfigurable RF front-end (LNA, mixer, LPFs) with system-level channel models in a unified simulation platform, enabling optimization based on metrics such as $BER$, $SER$, $EVM$, and $SNR$. A case study demonstrates significant energy–per–performance gains, achieving up to $16\times$ power reduction by adjusting RF front-end parameters while maintaining reliable demodulation for modulated signals. Overall, the framework provides a practical path to spectrum-agile, energy-efficient RX designs and sets the stage for ML-driven, real-time optimization deployed on programmable hardware.

Abstract

The deployment of a growing number of devices in Internet of Things (IoT) networks implies that uninterrupted and seamless adaptation of wireless communication parameters (e.g., carrier frequency, bandwidth and modulation) will become essential. To utilize wireless devices capable of switching several communication parameters requires real-time self-optimizations at the radio frequency integrated circuit (RFIC) level based on system level performance metrics during the processing of complex modulated signals. This article introduces a novel design verification approach for reconfigurable RFICs based on end-to-end wireless system-level performance metrics while operating in a dynamically changing communication environment. In contrast to prior work, this framework includes two modules that simulate a wireless channel and decode waveforms. These are connected to circuit-level modules that capture device- and circuit-level non-idealities of RFICs for design validation and optimization, such as transistor noises, intermodulation/harmonic distortions, and memory effects from parasitic capacitances. We demonstrate this framework with a receiver (RX) consisting of a reconfigurable complementary metal-oxide semiconductor (CMOS) low-noise amplifier (LNA) designed at the transistor level, a behavioral model of a mixer, and an ideal filter model. The seamless integration between system-level wireless models with circuit-level and behavioral models (such as VerilogA-based models) for RFIC blocks enables to preemptively evaluate circuit and system designs, and to optimize for different communication scenarios with adaptive circuits having extensive tuning ranges. An exemplary case study is presented, in which simulation results reveal that the LNA power consumption can be reduced up to 16x depending on system-level requirements.

Figures (5)

• Figure 1: Overview of the framework for co-design and optimization of reconfigurable RF circuits and wireless communication systems.
• Figure 2: Co-design simulation platform for wireless systems with dynamically reconfigurable RF front-end circuits.
• Figure 3: Simulated LNA performance parameters vs. bias current.
• Figure 4: Results from the simulation with generated QPSK packets: transient RF front-end characteristics and reconstructed constellations.
• Figure 5: Envisioned ML-based real-time RF front-end optimization.