Learning-Based Data Transmissions for Future 6G Enabled Industrial IoT: A Data Compression Perspective

摘要

The sixth-generation (6G) wireless system has been perceived to be the technology to connect everything. This will generate a huge volume of data traffic, resulting in severe spectrum shortage and system latency. To address this issue, data compression is considered to be indispensable for 6G to achieve efficient data transmissions, increase spectrum efficiency, and reduce system latency. Consequently, data compression technologies based on machine learning and deep learning, commonly known as learning-based data compressions, have received intensive attention lately. Taking the industrial IoT (IIoT) as a use case, this article attempts to explore the latest research progress on learning-based data compressions toward efficient data transmissions. Specifically, we first propose a novel learning-based data compression framework for edge-cloud collaborative IIoT. Then, we summarize the learning-based data transmission methods which are involved with various layers of the proposed edge-cloud collaborative framework. Moreover, we conduct a case study to show that our learning-based data transmission methods can effectively reduce the volume of the transmitted data. Finally, we highlight several promising future research directions on the learning-based data compression, such as robustness and instability of deep models, deep model optimization, and future deployment strategies for 6G enabled IIoT.