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In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method. 

Massive MIMO is one of the key technologies in 5G wireless broadband, capable of delivering substantial improvements in capacity of next-generation wireless networks. However, due to its inherent complexity, its operation, reconfiguration, and enhancement present significant challenges and risks. In this paper we present RENEW, a fully programmable and observable massive MIMO network. We present the architectural design for full programmability at every layer of the wireless stack, from the radio hardware, including PHY and MAC layer configurations, all the way up to the network core functionality using network function virtualization. We also present mechanisms to enable observability at every layer of the stack. These include various indicators in the radio and core access network, hence enabling effective monitoring, troubleshooting, and performance evaluation of the network at large.