Millimetre-wave (mmWave) communication is subjected to different types of blockages. Nonline of sight (NLoS) is the key problem that is caused by blockage. This study aimed ayt predicting the occurrence of self-blockage, dynamic blockage and static blockage and performs hybrid beamforming. Self-blockage is addressed by selecting and alternating the optimal access point by using an enhanced artificial flora algorithm. The fitness value estimation for optimal selection is based on the signal-to-noise ratio, distance and elevation angle. A drone base station (BS) is used to overcome dynamic blockages by inferring the drone altitude from free space path loss, transmit power and number of ground users. Static blockage is avoided by selecting the nearest ground BS based on the measurement of the SNR value. For optimum matching, bipartite matching theory is used that matches UEs to more than one ground BS. It avoids the delay of UEs. Once the user is connected and starts to receive signals, hybrid beamforming of regularised channel diagonalisation with the Convolutional Neural Network (CNN) and Kalman filter (KF) is applied to establish a communication beam. In hybrid beamforming the Channel State Information (CSI) is computed accurately by considering the frequency band, location, temperature, humidity, and weather which is performed by hybrid CNN with KF algorithm. Accurate CSI prediction ensures the formation of perfect beams from BSs and serves numerous users participating in the 5th Generation (5G) environment. The proposed system is implemented on a Matlab tool, and its performance is evaluated in terms of latency, blockage probability, number of users served, and spectral efficiency.

毫米波 (mmWave) 通信会受到不同类型的阻塞。非视距 (NLoS) 是由阻塞引起的关键问题。本研究旨在预测自阻塞、动态阻塞和静态阻塞的发生并执行混合波束成形。通过使用增强的人工植物群算法选择和交替最佳接入点来解决自阻塞问题。最优选择的适应值估计基于信噪比、距离和仰角。无人机基站 (BS) 用于通过从自由空间路径损耗、发射功率和地面用户数量推断无人机高度来克服动态阻塞。通过基于 SNR 值的测量选择最近的地面基站来避免静态阻塞。为了优化匹配，使用二分匹配理论将 UE 与多个地面 BS 匹配。它避免了UE的延迟。一旦用户连接并开始接收信号，就会应用正则化信道对角化与卷积神经网络 (CNN) 和卡尔曼滤波器 (KF) 的混合波束形成，以建立通信波束。在混合波束成形中，信道状态信息 (CSI) 通过考虑频带、位置、温度、湿度和天气来准确计算，这是由混合 CNN 和 KF 算法执行的。准确的 CSI 预测可确保从 BS 形成完美的波束，并为参与第 5 代 (5G) 环境的众多用户提供服务。所提出的系统在 Matlab 工具上实现，并根据延迟、阻塞概率、服务的用户数量、