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A Lossless Compression Technique for the Downlink Control Information Message

Bryan Liu, Alvaro Valcarce, K. Pavan Srinath

TL;DR

The paper tackles control-plane bottlenecks by compressing Downlink Control Information (DCI) losslessly using a transformer-based probability estimator paired with arithmetic coding, leveraging both temporal and spatial correlations. It introduces field-aware embedding, memory-based encoder/decoder features, and entropy-based field ordering to efficiently represent DCI fields. The method demonstrates ~21.7% improvements in compression ratio over Huffman coding in a single-cell scenario and yields modest yet meaningful channel-decoding gains (up to $0.8\mathrm{~dB}$) when combined with standard coding. This approach offers a practical, 5G NR–compliant path to enhancing downlink control-channel capacity and reliability for dense, future networks.

Abstract

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.

A Lossless Compression Technique for the Downlink Control Information Message

TL;DR

The paper tackles control-plane bottlenecks by compressing Downlink Control Information (DCI) losslessly using a transformer-based probability estimator paired with arithmetic coding, leveraging both temporal and spatial correlations. It introduces field-aware embedding, memory-based encoder/decoder features, and entropy-based field ordering to efficiently represent DCI fields. The method demonstrates ~21.7% improvements in compression ratio over Huffman coding in a single-cell scenario and yields modest yet meaningful channel-decoding gains (up to ) when combined with standard coding. This approach offers a practical, 5G NR–compliant path to enhancing downlink control-channel capacity and reliability for dense, future networks.

Abstract

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.
Paper Structure (12 sections, 6 equations, 5 figures, 3 tables, 1 algorithm)

This paper contains 12 sections, 6 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Downlink control information
  4. Deep-learning based lossless compression
  5. Transformer-based lossless compression for DCI message
  6. Features construction
  7. DCI compression
  8. field sorting
  9. Numerical Results
  10. Compression ratios
  11. Channel decoding performance
  12. Conclusion

Figures (5)

  • Figure 1: DCI compression with a transformer model
  • Figure 2: Reordering DCI fields to reach a better convergence result
  • Figure 3: Comparison on compression ratios, where the light blue dot indices a control bit with bit value of 1 and the white dot refers to 0. The dark blue dot refers to the null space.
  • Figure 4: Training curves comparison by ordering the fields' entropies by a descending order and an ascending order
  • Figure 5: Frame error rate comparison with lossless compression over an channel