mlsanityData to 7 Oct 2026

Channel-Token Attention for Reliable Dynamic Spectrum Access under Bursty Primary-User Traffic

arXiv 2608.22992 ↗First posted 24 Aug 2026cs.NI cs.LG eess.SPcited by 0

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Abstract (arXiv)

Dynamic spectrum access must coordinate secondary users under bursty primary-user activity while preserving packet reliability and delay. We present TACAN, a centralized policy that represents each channel as a token containing occupancy history and automatic-modulation-classification entropy; a context token supplies queue class, delay and user identity. A Transformer encoder is warm-started from an occupancy-greedy policy and refined with proximal policy optimization. The frozen policies were trained to maintain a channel assignment in every slot, including when queues were empty. We therefore replay them on held-out trajectories and distinguish standby assignment success from packet-present access and packet delivery. In a 20-channel network with 60 primary devices and 4 secondary users, TACAN achieves 92.53% +/- 0.47 packet-present access success, compared with 89.94% for Greedy and 83.53% for PPO+MLP. Its paired gain over Greedy is 2.59 points (parametric 95% CI 1.89-3.29), with wins in all five seeds; the exact two-sided sign-test value is 0.0625. The gain rises from 0.57 points at normal primary-user load to 7.67 points at extreme load. TACAN also reduces mean delivery delay from 1.208 to 1.123 slots and the conditional user-reliability gap from 9.69 to 3.15 points. Delivered packets per SU-slot remain arrival-limited (30.12% versus 30.11%), so no packet-throughput gain is claimed.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 24 Aug 2026.

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References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 163
  3. 2017Over the Air Deep Learning Based Radio Signal Classificationcited by 121
  4. 2017Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networkscited by 37
  5. 2018Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networkscited by 34
  6. 2021Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networkscited by 7
  7. 2020Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networkscited by 6
  8. 2025Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learningcited by 2

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