数据驱动的开放动态频谱接入系统构建方法

在认知无线电(CR)背景下，动态频谱接入已成为提高无线网络频谱利用率的重要途径。基于全球移动通信系统-铁路(GSM-R)系统中采集的细粒度频谱监测数据，提出一种数据驱动的深度学习方法，建模频谱模式，并建立一套动态频谱接入访问框架。采用一种深度频谱生成模型指导频谱分配；设计一种综合递归序列表征与场景特征嵌入的深度网络，建模和预测短时频谱占用情况，并由此提出一种动态信道接入策略。进一步，利用软件无线电(SDR)平台实现一套跳频系统，并将其与动态频谱接入策略进行集成。使用真实的历史频谱数据评估该系统的数据吞吐能力，测试结果表明，所提方法及构建的跳频系统能有效提高机会通信能力，高效利用频谱资源。该频谱接入框架及SDR系统实现具有较强的通用性，易于集成到不同场景和频段的系统中。

A data-driven method for open dynamic spectrum access system

In the context of Cognitive Radio(CR), dynamic spectrum access has become a key approach to improve the spectrum utilization in wireless network. In this paper, using fine-grained spectrum measurement data collected from Global System for Mobile Communications-Railway(GSM-R), a data-driven deep-learning method is proposed to model the spectrum pattern and a framework is developed for dynamic spectrum access. A deep spectrum generative model is adopted to guide the spectrum allocation. A deep network that combines recurrent series model and background feature embeddings is developed to model and predict the short-term spectrum occupancy, then a strategy is proposed for dynamic channel access. Furthermore, a frequency-hopping system is implemented with Software Defined Radio(SDR) platform and it is integrated with the proposed strategy. The throughput capacity of this system is evaluated with real-world historical spectrum data. It is shown that the proposed method and system can enhance the ability of opportunistic communication and utilize the spectrum resource efficiently. The proposed spectrum access framework and the implementation with SDR are of great generality, so that they can be easily integrated into systems with different scenarios and frequency spans.