Cross-Layer Collaborative Resource Allocation in Maritime Wireless Communications: QoS-Aware Power Control and Knowledge-Enhanced Service Scheduling

ZHANG Zhilin; MAO Zhongyang; LU Faping; PAN Yaozong; LIU Xiguo; KANG Jiafang; YOU Yang; JIN Yin

doi:10.11999/JEIT250252

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ZHANG Zhilin, MAO Zhongyang, LU Faping, PAN Yaozong, LIU Xiguo, KANG Jiafang, YOU Yang, JIN Yin. Cross-Layer Collaborative Resource Allocation in Maritime Wireless Communications: QoS-Aware Power Control and Knowledge-Enhanced Service Scheduling[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250252

Citation:

ZHANG Zhilin, MAO Zhongyang, LU Faping, PAN Yaozong, LIU Xiguo, KANG Jiafang, YOU Yang, JIN Yin. Cross-Layer Collaborative Resource Allocation in Maritime Wireless Communications: QoS-Aware Power Control and Knowledge-Enhanced Service Scheduling[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250252

Citation:

ZHANG Zhilin, MAO Zhongyang, LU Faping, PAN Yaozong, LIU Xiguo, KANG Jiafang, YOU Yang, JIN Yin. Cross-Layer Collaborative Resource Allocation in Maritime Wireless Communications: QoS-Aware Power Control and Knowledge-Enhanced Service Scheduling[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250252

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Cross-Layer Collaborative Resource Allocation in Maritime Wireless Communications: QoS-Aware Power Control and Knowledge-Enhanced Service Scheduling

doi: 10.11999/JEIT250252 cstr: 32379.14.JEIT250252

ZHANG Zhilin^{1, 2},
MAO Zhongyang^{1, 2},
LU Faping^{1, 2
,
,},
PAN Yaozong^{1, 2},
LIU Xiguo^{1, 2},
KANG Jiafang^{1, 2},
YOU Yang³,
JIN Yin⁴

1.
Naval Aeronautic University, Yantai 264001, China
2.
Shandong Key Laboratory of Sea and Air Information Perception and Processing Technology, Yantai 264001, China
3.
Unit 91001, PLA, Beijing 100000, China
4.
Unit 92524, PLA, Ningbo 315000, China

Funds: The Natural Science Foundation of Shandong Province (ZR2023MD045)

Received Date: 2025-04-09
Rev Recd Date: 2025-07-20

Available Online: 2025-08-06

Abstract

Abstract

Objective Maritime wireless communication networks face significant challenges, including dynamic topology drift, large-scale channel fading, and cross-layer resource competition. These factors hinder the effectiveness of traditional single-layer resource allocation methods, which struggle to maintain the balance between high-quality communications and heterogeneous service demands under limited network resources. This results in degraded Quality of Service (QoS) and uneven service guarantees. To address these challenges, this study proposes a cross-layer collaborative resource allocation framework that achieves balanced enhancement of system throughput and QoS assurance through closed-loop optimization, integrating physical-layer power control with network-layer service scheduling. First, a cross-layer wireless network transmission model is established based on the coupling mechanism between physical-layer channel capacity and transport-layer TCP throughput. Second, a dual-threshold water-level adjustment mechanism, incorporating both Signal-to-Noise Ratio (SNR) and QoS metrics, is introduced into the classical water-filling framework, yielding a QoS-aware dual-threshold water-filling algorithm. This approach strategically trades controlled throughput loss for improved QoS of high-priority services. Furthermore, a conflict resolution strategy optimization filter with dual-channel feature decoupling is designed within a twin deep reinforcement learning framework to enable real-time, adaptive node-service dynamic matching. Simulation results demonstrate that the proposed framework improves average QoS scores by 9.51% and increases critical service completion by 1.3%, while maintaining system throughput degradation within 10%. Methods This study advances through three main components: theoretical modeling, algorithm design, and system implementation, forming a comprehensive technical system. First, leveraging the coupling relationship between physical-layer channel capacity and transport-layer Transmission Control Protocol (TCP) throughput, a cross-layer joint optimization model integrating power allocation and service scheduling is established. Through mathematical derivation, the model reveals the nonlinear mapping between wireless resources and service demands, unifying traditionally independent power control and service scheduling within a non-convex optimization structure, thus providing a theoretical foundation for algorithm development. Second, the proposed dynamic dual-threshold water-filling algorithm incorporates a dual-regulation mechanism based on SNR and QoS levels. A joint mapping function is designed to enable flexible, demand-driven power allocation, enhancing system adaptability. Finally, a twin deep reinforcement learning framework is constructed, which achieves independent modeling of node mobility patterns and service demand characteristics through a dual-channel feature decoupling mechanism. A dynamic adjustment mechanism is embedded within the strategy optimization filter, improving critical service allocation success rates while controlling system throughput loss. This approach strengthens system resilience to the dynamic, complex maritime environment. Results and Discussions Comparative ablation experiments demonstrate that the dynamic dual-threshold water-filling algorithm within the proposed framework achieves a 9.51% improvement in QoS score relative to conventional water-filling methods. Furthermore, the Domain Knowledge-Enhanced Siamese DRL (DKES-DRL) method exceeds the Siamese DRL approach by 3.25% (Fig. 6), albeit at the expense of a 9.3% reduction in the system’s maximum throughput (Fig. 7). The average number of completed transactions exceeds that achieved by the traditional water-filling algorithm by 1.3% (Fig. 8, Fig. 9). In addition, analysis of the effect of node density on system performance reveals that lower node density corresponds to a higher average QoS score (Fig. 10), indicating that the proposed framework maintains service quality more effectively under sparse network conditions. Conclusions To address the complex challenges of dynamic topology drift, multi-scale channel fading, and cross-layer resource contention in maritime wireless communication networks, this paper proposes a cross-layer collaborative joint resource allocation framework. By incorporating a closed-loop cross-layer optimization mechanism spanning the physical and network layers, the framework mitigates the imbalance between system throughput and QoS assurance that constrains traditional single-layer optimization approaches. The primary innovations of this work are reflected in three aspects: (1) Cross-layer modeling is applied to overcome the limitations of conventional hierarchical optimization, establishing a theoretical foundation for integrated power control and service scheduling. (2) A dual-dimensional water-level adjustment mechanism is proposed, extending the classical water-filling algorithm to accommodate QoS-driven resource allocation. (3) A knowledge-enhanced intelligent decision-making system is developed by integrating model-driven and data-driven methodologies within a deep reinforcement learning framework. Simulation results confirm that the proposed framework delivers robust performance in dynamic maritime channel conditions and heterogeneous traffic scenarios, demonstrating particular suitability for maritime emergency communication environments with stringent QoS requirements. Future research will focus on resolving engineering challenges associated with the practical deployment of the proposed framework.
- Maritime wireless communication network,
- Power allocation,
- Service scheduling,
- Deep Reinforcement Learning (DRL),
- Siamese network

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References(42)

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