Deep Tree Search Based Pilot Efficiency SemiBlind Channel Estimation for Massive IoT As the tremendous increasing of Internet of Thing (IoT) deceives, the channel estimation between devices and base station will be a critical problem due to the pilot contamination. To address this issue, in this work, we provide a new perspective to alleviate pilot contamination for massive IoT system by reducing the command of piolt. A deep tree search (DTS) method is a decisionmaking algorithm. The basic principle of proposed algorithm is based on DTS and semiblind channel estimation, which can reduce the number of transmitting pilot sequence and improve transmission efficiency. We first covert the channel state information (CSI) into a ternary quantization form utilizing the sparse channel property of massive IoT system. Then the channel estimation can regarded as a Markov Decision Process (MDP) with the goal of finding the minimal Frobenius norm between estimated CSI and the transmitted signal. We propose a deep neural network (DNN) based tree search method to solve the MDP based CSI estimation problem. Compared with conventional methods, the proposed method exhibits higher estimation efficiency, which reduces by 10% the pilot sequence to achieve the same estimation performance. The experiments show that the SER performance of TDTS can reach about 5.6 xc3x97 10xe2x88x925 at SNR6 dB in massive MIMO systems, which outperforms existing methods.

Learning paradigms for communication and computing technologies in IoT systems Abstract   Wireless communication and computation technologies are becoming increasingly complex and dynamic due to the sophisticated and ubiquitous Internet of things (IoT) applications. Therefore, future wireless networks and computation solutions must be able to handle these challenges and dynamic user requirements for the success of IoT systems. Recently, learning strategies (particularly deep learning and reinforcement learning) are explored immensely to deal with the complexity and dynamic nature of communication and computation technologies for IoT systems, mainly because of their power to predict and efficient data analysis. Learning strategies can significantly enhance the performance of IoT systems at different stages, including at IoT node level, local communication, longrange communication, edge gateway, cloud platform, and corporate data centers. This paper presents a comprehensive overview of learning strategies for IoT systems. We categorize learning paradigms for communication and computing technologies in IoT systems into reinforcement learning, Bayesian algorithms, stochastic learning, and miscellaneous. We then present research in IoT with the integration of learning strategies from the optimization perspective where the optimization objectives are categorized into maximization and minimization along with corresponding applications. Learning strategies are discussed to illustrate how these strategies can enhance the performance of IoT applications. We also identify the key performance indicators (KPIs) used to evaluate the performance of IoT systems and discuss learning algorithms for these KPIs. Lastly, we provide future research directions to further enhance IoT systems using learning strategies