Intelligent Resource Allocation Algorithm Based on Outdated CSI for Multi-Node URLLC

ZHAO Yizhen; GAO Wei; HU Yulin; ZHU Yao

doi:10.11999/JEIT260216

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ZHAO Yizhen, GAO Wei, HU Yulin, ZHU Yao. Intelligent Resource Allocation Algorithm Based on Outdated CSI for Multi-Node URLLC[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260216

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ZHAO Yizhen, GAO Wei, HU Yulin, ZHU Yao. Intelligent Resource Allocation Algorithm Based on Outdated CSI for Multi-Node URLLC[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260216

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Intelligent Resource Allocation Algorithm Based on Outdated CSI for Multi-Node URLLC

doi: 10.11999/JEIT260216 cstr: 32379.14.JEIT260216

ZHAO Yizhen^{1, 2},
GAO Wei^{1, 2},
HU Yulin^{1, 2
,
,},
ZHU Yao^{1, 2}

1.
School of Electronic Information, Wuhan University, Wuhan, 430072, China

Funds: The National Natural Science Foundation of China (62471341, 12411530121), Hubei Provincial Science and Technology Cooperation Project (2025EHA040)

Accepted Date: 2026-04-23
Rev Recd Date: 2026-04-23

Available Online: 2026-05-13

Abstract

Abstract

Objective Ultra-Reliable and Low-Latency Communications (URLLC) have found widespread applications in Industrial Internet-of-Things (IIoT) systems. However, in mobile operation scenarios such as transportation and inspection, the acquisition of instantaneous Channel State Information (CSI) is often impractical due to feedback overhead, forcing resource allocation decisions to be made based on outdated CSI. This mismatch significantly limits the achievable energy efficiency of the system. Traditional convex optimization methods have difficulty addressing such challenges, while classical Deep Reinforcement Learning (DRL) algorithms also exhibit inherent limitations in terms of convergence stability and policy performance when confronted with the stringent Quality-of-Service (QoS) constraints in URLLC. Motivated by these challenges, considering a multi-user URLLC system operating under outdated CSI in dynamic scenarios, this paper formulates an energy efficiency maximization problem while guaranteeing the communication latency and reliability requirements, and aims to design an efficient and stable algorithm for joint power and blocklength allocation. Methods To achieve the above objective, this paper proposes a Successive Convex Approximation (SCA)–assisted DRL framework for energy efficiency maximization under outdated CSI. Specifically, a SCA-based algorithm is first developed to derive a pre-allocation of transmit power and blocklength, yielding a feasible and physically interpretable yet relatively conservative baseline solution. Building upon this baseline, a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is employed to perform incremental refinement through interaction with the dynamic environment, thereby alleviating the conservative nature of SCA. Meanwhile, the SCA solution is incorporated as prior knowledge together with user location information into the state representation, which effectively narrows the policy search space and enables the DRL agent to better capture large-scale channel characteristics and system dynamics under outdated CSI, thereby enhancing the learning efficiency and stability. Results and Discussions The effectiveness of the proposed method is validated through the following simulation results. In the simulation, the proposed algorithm is evaluated against SCA, TD3 without SCA guidance, and TD3 without user location information. Simulation results demonstrate that the proposed method significantly outperforms all benchmark schemes in terms of convergence stability and system energy efficiency. During the training phase (Fig. 3), the average reward of the proposed algorithm increases steadily and converges stably, whereas removing location information leads to low and highly fluctuating rewards, and removing SCA guidance results in convergence to a much lower reward level, highlighting the importance of both prior guidance and location-aware state representation. Besides, during the actual operation stage of the system, the proposed algorithm achieves high and stable energy efficiency (Fig. 4), significantly outperforming comparative algorithms. Under outdated CSI, DRL-based methods outperform conservative optimization when transmission is successful, while the absence of location information or SCA guidance significantly degrades energy efficiency or increases transmission failures, verifying the two factors' effectiveness in improving energy efficiency and ensuring strategy validity. The simulation also examined the impact of key system parameters on energy efficiency. For basic resource parameters such as blocklength (Fig. 5) or power (Fig. 6), appropriately increasing their budget can help improve system energy efficiency. For parameters about reliability (Fig. 7), in order to avoid waste of resources, they should be reasonably set according to business requirements. Finally, the simulation of the average energy efficiency varying with the number of nodes and the number of network neurons provides certain reference basis for the configuration of the algorithm structure and the design of the network scale (Fig. 8). Conclusions In conclusion, this paper addresses the challenge of energy-efficient resource allocation for multi-user URLLC systems operating under outdated CSI by integrating SCA with DRL. That is, a TD3-based DRL approach is enhanced by introducing a SCA reference solution as prior guidance and incorporating user location information into the state representation. Such an optimization–learning dual-driven solution framework combines the interpretability and feasibility of model-based optimization with the adaptivity and expressive power of data-driven learning. The effectiveness of the proposed method is evaluated through simulations: (1) The proposed method achieves higher energy efficiency than pure optimization and conventional TD3 while satisfying URLLC latency and reliability constraints; (2) The SCA reference improves the stability and effectiveness of the strategy under outdated CSI; (3) Incorporating user location information enables more efficient decision-making. However, this work focuses on a single-cell multi-user scenario, and practical issues such as multi-cell interference, cooperative multi-base-station scheduling, and more complex mobility patterns are not considered. Future work will extend the proposed framework to more realistic multi-cell and multi-agent scenarios and investigate its applicability under more severe CSI imperfections.

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References(19)

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