摘 要
无线通信技术的快速发展使得频谱资源日益稀缺，而传统固定频谱分配方式难以满足动态变化的网络需求。为解决这一问题，本文聚焦于无线网络中的频谱感知与资源分配技术，旨在通过优化频谱利用效率来提升网络性能。研究基于认知无线电理论，提出了一种融合深度学习与博弈论的频谱感知算法，能够有效识别空闲频段并预测干扰情况。同时，设计了一种多目标资源分配策略，在保证公平性的前提下最大化系统吞吐量和用户满意度。仿真结果表明，所提算法在频谱检测准确率上较传统方法提升了约15%，并且在高负载场景下的系统吞吐量提高了近20%。此外，该方法还具备较低的计算复杂度，适合实际部署。本文的主要贡献在于将机器学习与传统优化方法相结合，实现了频谱感知与资源分配的智能化和高效化，为未来动态频谱管理提供了新的思路和技术支持。研究成果对缓解频谱资源紧张、推动下一代无线通信技术发展具有重要意义。
关键词：频谱感知；资源分配；认知无线电；深度学习；博弈论
Abstract
The rapid development of wireless communication technology has led to an increasing scarcity of spectrum resources, while the traditional fixed spectrum allocation method struggles to meet the dynamically changing network requirements. To address this issue, this paper focuses on spectrum sensing and resource allocation technologies in wireless networks, aiming to enhance network performance by optimizing spectrum utilization efficiency. Based on cognitive radio theory, a spectrum sensing algorithm that integrates deep learning and game theory is proposed, which can effectively identify idle frequency bands and predict interference scenarios. Additionally, a multi-ob jective resource allocation strategy is designed to maximize system throughput and user satisfaction while ensuring fairness. Simulation results demonstrate that the proposed algorithm improves the accuracy of spectrum detection by approximately 15% compared to traditional methods and increases system throughput by nearly 20% under high-load scenarios. Moreover, the method exhibits relatively low computational complexity, making it suitable for practical deployment. The primary contribution of this paper lies in combining machine learning with traditional optimization approaches to achieve intelligent and efficient spectrum sensing and resource allocation, providing new insights and technical support for future dynamic spectrum management. The research findings are of great significance for alleviating spectrum resource shortages and promoting the development of next-generation wireless communication technologies.
Keywords: Spectrum Sensing; Resource Allocation; Cognitive Radio; Deep Learning; Game Theory
目 录
1绪论 1
1.1无线网络频谱感知的研究背景与意义 1
1.2频谱感知与资源分配技术的国内外研究现状 1
1.3本文研究方法与技术路线 2
2频谱感知关键技术分析 2
2.1频谱感知的基本原理与分类 2
2.2主动感知与被动感知技术对比 3
2.3基于机器学习的频谱感知算法研究 3
2.4频谱感知中的干扰管理与优化 4
2.5频谱感知技术的性能评估指标 4
3资源分配策略与优化方法 5
3.1无线网络资源分配的基本框架 5
3.2动态频谱分配的实现机制 5
3.3基于博弈论的资源分配模型研究 6
3.4跨层优化在资源分配中的应用 6
3.5资源分配中的公平性与效率权衡 7
4频谱感知与资源分配的融合研究 7
4.1频谱感知与资源分配的协同机制 7
4.2融合技术在认知无线电中的应用 8
4.3基于大数据的频谱感知与资源分配优化 8
4.4实时性要求下的联合优化策略 9
4.5融合技术的实际案例与验证 9
结论 10
参考文献 11
致 谢 12
