MEAN: Mixture-of-Experts Based Neural Receiver

Dynamic Neural Networks (DyNN) adapt its network architecture at run-time compared to static neural networks. DyNNs benefit in wireless receivers based on neural networks (or neural receivers), that need to adapt their performance under varying channel conditions. Mixture-of-Experts (MoE) is one efficient way to realize a DyNN in neural receivers in which several smaller expert networks are dynamically combined in the run-time to create a dedicated network according to the channel requirements. This paper presents a novel hard-gated (also known as sparsely-gated) MoE-based neural receiver architecture called MEAN for Single Input Multiple Output (SIMO)-based wireless communication systems. Our proposed MEAN architecture for a SIMO wireless system shows a reduction of up to 50% in the number of active layers during runtime.