An improved TOA estimation algorithm based on denoised MVDR for B5G positioning

This paper proposes an improved minimum variance distortionless response (MVDR) based TOA estimation algorithm for 5G NR signals under multipath environments. The proposed algorithm achieves high resolution by exploiting a large number of subcarriers of 5G signals and reduces the dimension of the covariance matrix involved in MVDR substantially by utilizing a novel smoothing scheme. Since MVDR requires a relatively high signal-to-noise ratio (SNR), a denoising method is used to improve the TOA estimation performance. Simulation results show that the proposed algorithm achieves much higher resolution than the Bartlett beamformer (BF) and the TOA estimation accuracy remains high over a wide range of SNRs.     

基于哈达玛积扩展子空间的到达时间和波达方向联合估计

在窄带阵列天线正交频分复用系统的到达时间和波达方向联合估计中, 针对阵元数目较少时波达方向估计精度不高, 特别是多径数目大于阵元数目导致的波达方向无法估计问题, 提出一种基于哈达玛积扩展子空间的到达时间和波达方向联合估计算法. 该算法首先利用各阵元上的频域信道估计构成扩展信道频域响应矢量, 然后计算扩展信道频域响应矢量自相关矩阵, 并进行特征值分解得到哈达玛积扩展噪声子空间, 最后构造伪谱函数并进行二维谱峰搜索, 从而实现到达时间和波达方向的联合估计. 仿真结果表明, 与现有算法相比, 在复杂度没有大幅提高的前提下, 该算法的估计结果均方根误差更加接近克拉美罗界, 且到达时间和波达方向估计能够自动配对, 在多径数目大于阵元数目时依然适用.     

Multi-task learning approach for modulation and wireless signal classification for 5G and beyond: Edge deployment via model compression

Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.