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.

翻译：深度神经网络（DNN）已被证明能够优化上行链路多输入多输出（MIMO）接收机的性能，新兴架构通过增强经典接收机处理模块实现这一目标。现有设计多采用静态DNN，其网络结构固定且权重需预先训练。这导致一个显著挑战：所得MIMO接收机仅适用于特定配置（即特定信道分布与用户数量），而实际网络中这些参数常因网络状态变化及用户动态接入/离开而频繁改变。本研究针对DNN辅助MIMO接收机的这一核心挑战展开工作。基于超网络概念，我们为接收机引入预训练的深度模型，其功能是根据瞬时信道变化动态更新DNN辅助接收机的权重。所设计的超网络特别针对模块化深度接收机，利用其模块化特性使超网络不仅能调整权重，还能动态调整网络架构。这种模块化超网络实现的DNN辅助接收机，其架构与计算复杂度可随信道变化及用户数量自适应调整，且无需重新训练。数值研究表明，在时变信道环境下，模块化超网络相比静态预训练接收机具有更优的误码率性能，同时能快速适应网络变化并实现高效扩展。