# Modular Hypernetworks for Scalable and Adaptive Deep MIMO Receivers

arXiv: https://arxiv.org/abs/2408.11920 · HTML: https://mlsanity.com/paper/2408.11920/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2024.

## Built on by (most cited first, 5 of 5)

- [Deep Unfolding: Recent Developments, Theory, and Design Guidelines](https://mlsanity.com/paper/2512.03768/) (2025)
- [Turbo-ICL: In-Context Learning-Based Turbo Equalization](https://mlsanity.com/paper/2505.06175/) (2025)
- [In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory](https://mlsanity.com/paper/2506.15176/) (2025)
- [Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis](https://mlsanity.com/paper/2602.20178/) (2026)
- [MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETs](https://mlsanity.com/paper/2609.40170/) (2026)

## Abstract (arXiv)

Deep neural networks (DNNs) were shown to facilitate the operation of uplink multiple-input multiple-output (MIMO) receivers, with emerging architectures augmenting modules of classic receiver processing. Current designs consider static DNNs, whose architecture is fixed and weights are pre-trained. This induces a notable challenge, as the resulting MIMO receiver is suitable for a given configuration, i.e., channel distribution and number of users, while in practice these parameters change frequently with network variations and users leaving and joining the network. In this work, we tackle this core challenge of DNN-aided MIMO receivers. We build upon the concept of hypernetworks, augmenting the receiver with a pre-trained deep model whose purpose is to update the weights of the DNN-aided receiver upon instantaneous channel variations. We design our hypernetwork to augment modular deep receivers, leveraging their modularity to have the hypernetwork adapt not only the weights, but also the architecture. Our modular hypernetwork leads to a DNN-aided receiver whose architecture and resulting complexity adapts to the number of users, in addition to channel variations, without retraining. Our numerical studies demonstrate superior error-rate performance of modular hypernetworks in time-varying channels compared to static pre-trained receivers, while providing rapid adaptivity and scalability to network variations.