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

我们提出了从有限的用户设备（UE）测量中重建信道的模型定义。我们将使用从爱立信在真实网络上通信的用户收集到的现有反馈。在与爱立信团队的合作中，我们将寻找不同的方法（例如，信道互易性）来获取有限UE反馈的标记数据集和相应的信道系数，用于开发重建模型。我们将开发一个模拟工具，基于从真实网络上通信的ue收集的有限CSI反馈来重建时空通道。该模型将研究频谱共享和分配，以提高频谱复用的效率。这包括分析资源和功率分配，以提高频谱效率和实现其他目标，例如满足不同类型用户的服务质量（QoS）需求。一个重要的研究项目是频谱感知方法的研究，包括适应动态无线电环境的频谱感知技术。我们将提出在低信噪比（SINR）情况下检测和分类信号的新算法。我们将使用真实和模拟信号的混合来提取不同类型信号在空间和时间维度上的特征。我们将提出分布式算法，将在性能方面进行评估，并将结果与现有的资源分配算法进行比较。我们还将评估和比较集中式和分布式自治方式在资源和权力分配方面的性能。将对这些方法的性能和复杂性进行评估，以验证其有效性和可用性。

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.