Modeling frameworks for spectrum sharing and channel reconstruction in 5G systems

我们提出了模型的定义，从有限的用户设备（UE）测量重建信道。我们将使用从爱立信的UE收集的现有反馈，这些UE通过真实的网络进行通信。我们将与爱立信的团队合作，寻找不同的方法（例如，信道互易性），以获取有限UE反馈的标记数据集和要在开发重构模型中使用的对应信道系数。我们将开发一种仿真工具，以基于从通过真实的网络进行通信的UE收集的有限CSI反馈来重建时空信道。 该模型将研究频谱共享和分配，以提高频谱重用的效率。这包括分析资源和功率分配，以提高频谱效率并实现其他目标，例如满足不同类型用户的服务质量（QoS）需求。频谱感知方法的研究是一个重要的研究项目，包括频谱感知技术，以适应动态无线电环境。我们将提出新的算法来检测和分类信号在低信号干扰噪声比（SINR）的情况下。我们将使用真实的和模拟信号的混合来提取空间和时间维度上不同类型信号的特征。 我们将提出分布式算法，将在性能方面进行评估，并与现有的资源分配算法的结果进行比较。我们还将评估和比较集中式和分布式自治方法的资源和功率分配的性能。 将评估这些方法的性能和复杂性，以验证其效率和可用性。

We propose the definition of models to reconstruct channels from limited User Equipment (UE) measurements. We will use existing feedback that has been collected from UEs at Ericsson communicating over real networks. In collaboration with the team at Ericsson we will look for different methods (e.g., channel reciprocity) to acquire labeled data sets of limited UE feedback and corresponding channel coefficients to be used in developing the reconstruction models. We will develop a simulation tool to reconstruct temporal-spatial channels based on limited CSI feedback that has been collected from UEs communicating over real networks. The models will study spectrum sharing and assignment to increase the efficiency of spectrum reuse. This includes analysis of resource and power allocation to increase spectrum efficiency and achieve other objectives, such as meeting Quality of Service (QoS) demands for different types of users. An important research item is the study of spectrum sensing methods, including spectrum awareness techniques to adapt to dynamic radio environments. We will propose new algorithms to detect and classify signals in low Signal-to-Interference-plus-Noise Ratio (SINR) scenarios. We will use a mix of real and simulated signals to extract features of different types of signals over the spatial and time dimensions. We will propose distributed algorithms that will be evaluated in terms of performance and compare the results with existing algorithms for resources allocation. We will also evaluate and compare the performance of centralized and distributed autonomous approaches for resource and power allocation. The performance as well as the complexity of such methods will be evaluated to validate their efficiency and usability.