Cell-edge Interferometry: Reliable Detection of Unknown Cell-edge Users via Canonical Correlation Analysis
Cell-edge Interferometry: Reliable Detection of Unknown Cell-edge Users via Canonical Correlation Analysis
复制标题
DOI：
10.1109/spawc.2019.8815536
复制
发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
M. S. Ibrahim;N. Sidiropoulos
中科院分区：
文献类型：
--
作者：
M. S. Ibrahim;N. Sidiropoulos
4G和新兴5G系统中的一个关键挑战是可靠地检测靠近小区之间边缘的用户的上行链路传输。这些用户由于路径损耗而遭受显著的信号衰减，并且从一个小区到另一个小区的频繁切换，使得信道估计非常具有挑战性。由于信道估计误差和多用户检测对远近功率不平衡的敏感性，即使使用基站协作的多用户检测也常常不能检测到这样的用户。在这些情况下，是否有可能可靠地解码小区边缘用户的信号？本文表明，也许令人惊讶的是，与一个合适的基站“干涉”的策略，小区边缘用户的信号可以可靠地解码在低信噪比下温和的条件。利用小区边缘用户的信号很弱但对两个基站都是共同的，而靠近基站的用户对该基站是唯一的这一事实，通过典型相关分析（CCA）实现可靠的检测，CCA是一种机器学习技术，即使在存在强个体干扰的情况下也能可靠地估计公共子空间。没有细胞中心干扰，然后可以使用众所周知的代数信号处理技术解开细胞边缘信号的混合。仿真结果表明，所提出的检测器实现了数量级的BER改善相比，一个“预言”迫零连续干扰消除，假设所有信道的完美知识。本文还包括假设生成模型的公共子空间可识别性的证明，这在机器学习/ CCA文献中是奇怪的。
A key challenge in 4G and emerging 5G systems is that of reliably detecting the uplink transmissions of users close to the edge between cells. These users are subject to significant signal attenuation due to path loss, and frequent hand-off from one cell to the other, making channel estimation very challenging. Even multiuser detection using base station cooperation often fails to detect such users, due to channel estimation errors and the sensitivity of multiuser detection to near-far power imbalance. Is it even possible to reliably decode the cell-edge users' signals under these circumstances? This paper shows, perhaps surprisingly, that with a suitable base station ‘interferometry’ strategy, the cell-edge users' signals can be reliably decoded at low SNR under mild conditions. Exploiting the fact that cell-edge users' signals are weak but common to both base stations, while users close to a base station are unique to that base station, reliable detection is enabled by Canonical Correlation Analysis (CCA) - a machine learning technique that reliably estimates a common subspace, even in the presence of strong individual interference. Free from cell-center interference, the resulting mixture of cell-edge signals can then be unraveled using well-known algebraic signal processing techniques. Simulations demonstrate that the proposed detector achieves order of magnitude BER improvement compared to an ‘oracle’ zero-forcing with successive interference cancellation that assumes perfect knowledge of all channels. The paper also includes proof of common subspace identifiability for the assumed generative model, which was curiously missing from the machine learning / CCA literature.