Energy and robustness trade-offs in adaptive neural mmWave channel estimation on edge devices

Abstract The evolution toward 6G will continue to leverage massive multiple-input multiple-output and millimeter-wave systems, which demand accurate angle-of-arrival (AoA) and angle-of-departure (AoD) estimation. While several deep learning models have demonstrated strong performance for this task, their accuracy, like that of most estimation methods, is often degraded by hardware non-idealities, which can be further exacerbated by time-varying operational factors such as component aging and adverse weather, among others. Building on a pre-trained U-Net architecture with demonstrated competitive performance for AoA/AoD estimation, we first propose an adaptation mechanism based on fine-tuning with impairment-augmented data. Specifically, we simulate hardware imperfections by introducing random phase errors in the antenna elements, ranging from mild fluctuations to severe signal distortions. The U-Net model with adaptation capabilities is then implemented on an NVIDIA Jetson Orin Nano device, a compact edge platform with heterogeneous computing resources. To this end, we design a co-execution strategy that performs AoA/AoD estimation (inference) on the CPU while simultaneously fine-tuning the model on the GPU, thus enabling continuous model adaptation to changing environmental or hardware conditions while preserving real-time inference performance. Experimental results show that impairment-aware fine-tuning effectively counters hardware degradation, particularly under significant phase impairments. In such scenarios, the fine-tuned model consistently preserves or even improves estimation accuracy, reducing the Root Mean Square Error (RMSE) by approximately 3.6% and increasing the Probability of Detection ( $$P_D$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>P</mml:mi> <mml:mi>D</mml:mi> </mml:msub> </mml:math> ) by up to 1 percentage point compared to the base model. Furthermore, a detailed energy-performance analysis demonstrates that while maximum frequency settings reduce training time by over 11 $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> , they also increase power consumption by more than 5 $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> , with optimal energy efficiency achieved at mid-range CPU and high GPU frequencies. This work establishes the feasibility of concurrent training and inference on resource-constrained heterogeneous hardware, paving the way for resilient and autonomous edge intelligence in future 6G systems.