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Adaptation of handover parameters in ultra-dense networks has always been one of the key issues in optimizing network performance. Aiming at the optimization goal of effective handover ratio, this paper proposes a deep Q-learning (DQN) method that dynamically selects handover parameters according to wireless signal fading conditions. This approach seeks good backward compatibility. In order to enhance the efficiency and performance of the DQN method, Long Short Term Memory (LSTM) is used to build a digital twin and assist the DQN algorithm to achieve a more efficient search. Simulation experiments prove that the enhanced method has a faster convergence speed than the ordinary DQN method, and at the same time, achieves an average effective handover ratio increase of 2.7%. Moreover, in different wireless signal fading intervals, the method proposed in this paper has achieved better performance. [ABSTRACT FROM AUTHOR]

Phase errors of phased array antennas can cause gain degradation that necessitates calibration to compensate this effect. Over-the-air (OTA) calibration methods are promising for avoiding interruption of communication applications for specific calibration conditions. However, typical calibration methods have the assumption that precise knowledge about calibration should be exchanged, such as relative positions and cost function (CF) values, which are difficult to obtain in practical mobile communication systems. To overcome this, a sensing-based OTA calibration method is proposed in which sensing methods aided by cameras or infrastructure sensors could locate user equipment (UE) and regard it as calibration reference source. Then, greedy algorithm is integrated into new radio (NR) beam management process, where UE measures directional channel state information-reference signals (CSI-RSs) reserved for calibration and feeds back the optimal beam index instead of CF values. Consequently, UE received power is maximized so as to achieve phase calibration. The simulation results exhibit that even when locating error exists, the proposed method can correct phase errors with a low overhead in terms of reserved NR CSI-RSs. [ABSTRACT FROM AUTHOR]

In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction. [ABSTRACT FROM AUTHOR]