MEAN: Mixture-of-Experts Neural Receiver - Architecture and Performance Analysis
Bram Van Bolderik, Vlado Menkovski, Sonia Heemstra de Groot, Manil Dev Gomony · ACM Transactions on Embedded Computing Systems · 2025
Neural network-based wireless receivers, also known as neural receivers , have demonstrated superior performance over traditional receivers, but come with greater computational complexity. The need to use these networks on energy-conscious edge devices is increasing, necessitating energy-efficient and adaptive neural receivers to function well under varying channel conditions. Transitioning static neural receivers to dynamic models using the concepts of Dynamic Neural Networks (DyNN) could reduce runtime computational complexity and energy consumption. This could be achieved by adapting the network architecture during run-time and exploit the varying channel conditions in a mobile communication system to reduce the computational complexity. This work introduces MEAN, a novel hard-gated Mixture-of-Experts (MOE) based neural receiver architecture. The main idea behind MEAN is to use several smaller Signal-to-Noise-Ratio (SNR) expert networks are used during run-time to create a network that selects the correct expert for the current data input to reduce complexity. The paper consists of the following key contributions. (1) MEAN architecture based on [ 25 ]: A hard-gated MoE model that dynamically selects the most suitable expert for each input dynamically based on current channel conditions. (2) Comprehensive system-level performance analysis of MEAN across different code rates and modulation schemes, including validation of expert selection during inference. (3) Loss function optimization for the gating network to promote the activation of specific experts in designated noise regions. This enhancement reduces gating network complexity while improving expert selection accuracy over previous MEAN implementations. (4) Hardware analysis of a gate-level implementation of MEAN synthesized in 22nm FD-SOI technology. The design is generated using a custom High-Level Synthesis (HLS) framework, providing detailed power and area evaluations. The proposed MEAN architecture for a Single Input Multiple Output (SIMO) wireless system achieves a 37.11% reduction in total power consumption with only an 11.04% increase in area, while maintaining accuracy comparable to that of a static neural network.