Temporally Encoded Double DQN for Proactive PRB Allocation in O-RAN Enabled Industrial Networks
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| Format: | Preprint |
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2026
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| _version_ | 1866911730749145088 |
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| author | Delavari, Elahe Wu, Xingqi Farooq, Junaid |
| author_facet | Delavari, Elahe Wu, Xingqi Farooq, Junaid |
| contents | Fifth-generation (5G) wireless systems are increasingly adopted in smart manufacturing to support heterogeneous industrial workloads through services such as enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC). However, industrial traffic is inherently process-driven and temporally correlated. So, static or reactive schedulers in the Open Radio Access Network (O-RAN) are inadequate for such non-stationary conditions, leading to sub-optimal utilization and violation of latency-reliability guarantees. This paper proposes a temporal-aware deep reinforcement learning (DRL) xApp for proactive Physical Resource Block (PRB) allocation in O-RAN-enabled industrial networks. The proposed framework integrates a long short-term memory (LSTM) encoder within a Double Deep Q-Network (DQN) to model sequential dependencies among slice-level Key Performance Indicators (KPIs), enabling predictive and stable decision-making. A continuous-time Markov chain (CTMC) traffic model is incorporated to emulate machine concurrency and process burstiness. Experimental results show that the LSTM-Double DQN improves slice satisfaction, and buffer stability under moderate and heavy load, with the longest sequence window providing the strongest gains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30630 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Temporally Encoded Double DQN for Proactive PRB Allocation in O-RAN Enabled Industrial Networks Delavari, Elahe Wu, Xingqi Farooq, Junaid Networking and Internet Architecture Fifth-generation (5G) wireless systems are increasingly adopted in smart manufacturing to support heterogeneous industrial workloads through services such as enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC). However, industrial traffic is inherently process-driven and temporally correlated. So, static or reactive schedulers in the Open Radio Access Network (O-RAN) are inadequate for such non-stationary conditions, leading to sub-optimal utilization and violation of latency-reliability guarantees. This paper proposes a temporal-aware deep reinforcement learning (DRL) xApp for proactive Physical Resource Block (PRB) allocation in O-RAN-enabled industrial networks. The proposed framework integrates a long short-term memory (LSTM) encoder within a Double Deep Q-Network (DQN) to model sequential dependencies among slice-level Key Performance Indicators (KPIs), enabling predictive and stable decision-making. A continuous-time Markov chain (CTMC) traffic model is incorporated to emulate machine concurrency and process burstiness. Experimental results show that the LSTM-Double DQN improves slice satisfaction, and buffer stability under moderate and heavy load, with the longest sequence window providing the strongest gains. |
| title | Temporally Encoded Double DQN for Proactive PRB Allocation in O-RAN Enabled Industrial Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2605.30630 |