arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.16319cs.ITmath.IT

一种针对下行控制信息消息的无损压缩技术

A Lossless Compression Technique for the Downlink Control Information Message

Bryan Liu, Alvaro Valcarce, K. Pavan Srinath

AI总结:

本文提出基于transformer模型与Arithmetic coding的无损压缩方法,利用DCI消息的时空相关性,在单小区调度场景下比Huffman coding压缩比提升21.7%,有效提升5G控制平面容量与可靠性。

摘要:

提升无线系统的可靠性与频谱效率是无线系统的关键目标。然而,大多数努力都致力于提升数据信道容量,而控制平面容量瓶颈常被忽视。在本文中,我们提出了一种通过缩小关键信令消息——5G下行控制信息(DCI)的比特大小来提升控制平面容量与可靠性的方法。具体而言,研究将transformer模型作为Arithmetic coding的概率分布估计器,以实现无损压缩。本文全面讨论了特征工程、神经模型设计和训练技术。transformer模型探索了DCI消息之间的时间与空间相关性,以实现合理的无损压缩性能。数值结果表明,在单小区调度场景的DCI压缩中,所提方法比Huffman coding实现了高21.7%的压缩比。

英文摘要:

Improving the reliability and spectral efficiency of wireless systems is a key goal in wireless systems. However, most efforts have been devoted to improving data channel capacity, whereas control-plane capacity bottlenecks are often neglected. In this paper, we propose a means of improving the control-plane capacity and reliability by shrinking the bit size of a key signaling message - the 5G Downlink Control Information (DCI). In particular, a transformer model is studied as a probability distribution estimator for Arithmetic coding to achieve lossless compression. Feature engineering, neural model design, and training technique are comprehensively discussed in this paper. Both temporal and spatial correlations among DCI messages are explored by the transformer model to achieve reasonable lossless compression performance. Numerical results show that the proposed method achieves 21.7% higher compression ratio than Huffman coding in DCI compression for a single-cell scheduling scenario.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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