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arXiv 1809.01583cs.ITmath.IT
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sub-6 GHz/mmWave 5G网络半盲上下行解耦分类算法

Classification Algorithms for Semi-Blind Uplink/Downlink Decoupling in sub-6 GHz/mmWave 5G Networks

Hatim Chergui, Kamel Tourki, Redouane Lguensat, Mustapha Benjillali, Christos Verikoukis, Mérouane Debbah

摘要:

未来混合的亚6 GHz/毫米波(mmWave)第五代(5G)无小区大规模多输入多输出(MIMO)网络中的可靠性和延迟挑战是保证上行链路(UL)和下行链路(DL)中的快速无线电资源管理,同时解决可能在阻塞情况下出现的相应传播不平衡。在此上下文中,我们引入半盲UL/DL解耦概念,其中在其初始激活之后,中央处理单元(CPU)收集反映用户设备(UE)的视线(LOS)条件的Rician $K$因子的测量以及2.6 GHz和28 GHz频带两者的DL参考信号接收功率(RSRP),然后训练非线性支持向量机(SVM)算法。CPU最终明确停止毫米波的测量,并将训练好的SVM算法应用于2.6 GHz数据,以盲预测可独立用于UL和DL的目标频率和接入点(AP)。在训练样本较少的情况下,该分类器的准确率达到了$95\%$.

英文摘要:

Reliability and latency challenges in future mixed sub-6 GHz/millimeter wave (mmWave) fifth generation (5G) cell-free massive multiple-input multiple-output (MIMO) networks is to guarantee a fast radio resource management in both uplink (UL) and downlink (DL), while tackling the corresponding propagation imbalance that may arise in blockage situations. In this context, we introduce a semi-blind UL/DL decoupling concept where, after its initial activation, the central processing unit (CPU) gathers measurements of the Rician $K$-factor---reflecting the line-of-sight (LOS) condition of the user equipment (UE)---as well as the DL reference signal receive power (RSRP) for both 2.6 GHz and 28 GHz frequency bands, and then train a non-linear support vector machine (SVM) algorithm. The CPU finally stops the measurements of mmWave definitely, and apply the trained SVM algorithm on the 2.6 GHz data to blindly predict the target frequencies and access points (APs) that can be independently used for the UL and DL. The accuracy score of the proposed classifier reaches $95\%$ for few training samples.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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