NAS-VMIMO: A Task-Oriented Multimodal Wireless Semantic Communication System

The NAS-VMIMO project combines Neural Architecture Search (NAS) and virtual MIMO technology to build an end-to-end multimodal wireless semantic communication system. Through lightweight multimodal models and intelligent resource allocation, it addresses challenges such as bandwidth bottlenecks, energy consumption issues, and low task adaptation efficiency in traditional communication, providing a feasible solution for task-oriented communication in the 6G era.

As 5G evolves toward 6G, traditional communication faces bandwidth bottlenecks and energy consumption challenges. Multimodal data transmission has three core issues: 1. Bandwidth limitations (raw data transmission struggles to ensure real-time performance under poor channel conditions); 2. Energy consumption issues (edge devices have limited computing and transmission capabilities); 3. Task adaptation (general compression strategies are inefficient). NAS-VMIMO proposes a task-oriented semantic communication architecture to address these challenges.

The system core consists of two parts: 1. Lightweight multimodal model: Optimized via NAS, it uses knowledge distillation to extract capabilities from large models, supports unified semantic representation of multimodal data, and is suitable for edge deployment; 2. Virtual MIMO controller: Through software-defined virtual antenna arrays, it schedules single-antenna devices to work collaboratively, dynamically adapts to channel changes, and has a built-in Rayleigh MIMO channel model for simulation verification.

1. End-to-end training process: Covers synthetic data generation, channel simulation, resource model (joint optimization of bandwidth/power/computation), and task loss (incorporating downstream task performance into training objectives); 2. Semantic-channel joint coding: Breaks the traditional separate design. The semantic encoder maps inputs to the semantic space, the channel encoder adjusts strategies based on channel status, and end-to-end collaborative optimization outperforms traditional schemes under medium and low signal-to-noise ratios.

Applicable to multiple scenarios: Smart IoT (edge cameras transmit semantic features to reduce bandwidth requirements), autonomous driving (vehicles exchange key semantic information to improve communication efficiency), industrial monitoring (dynamically adjust transmission content to reduce network load), effectively addressing communication pain points in real-world scenarios.

NAS-VMIMO demonstrates the potential of combining semantic communication with virtual MIMO. The lightweight model + intelligent resource allocation provides a technical path for next-generation wireless communication; the open-source implementation of the project provides a complete experimental platform (synthetic data, channel simulation, training pipeline). With the advancement of 6G standardization, it is expected to be deployed in actual networks.