In this paper, we investigate the mmWave beam tracking for vehicle-to-infrastructure (V2I) communications to find the optimal beam via sub-6 GHz channel state information (CSI). We consider two scenarios: 1) sub-6 GHz and mmWave transceivers are co-located on the same base station (BS), and 2) sub-6 GHz and mmWave BSs are separated in different places constituting heterogeneous networks (HetNets) where one sub-6 GHz BS controls multiple mmWave BSs. Considering the mobility of the vehicle and time-varying channels, we propose a predictive beam tracking method based on long short-term memory (LSTM) to construct the maps from historical sequential sub-6 GHz CSI to the future optimal mmWave beam. A single LSTM model can handle the beam tracking in the co-located scenario, since there is a one-to-one correspondence between the sub-6 GHz and mmWave transceivers, and the propagation of sub-6 GHz and mmWave signals is similar. However, in the HetNet scenario, it is difficult to select the best one among the beams of multiple mmWave BSs only via the CSI of one sub-6 GHz BS. To address this challenge, we design an LSTM fusion model, which exploits not only the historical sequential sub-6 GHz CSI but also a number of mmWave wide beam measurements, to obtain the optimal mmWave BS and beam in the HetNet. In this case, the collected sub-6 GHz CSI and mmWave wide beam measurements are analyzed by the LSTM and fully connected network (FCN) modules, respectively, providing two beam prediction results. Then the results are fused by an attention-based FCN module to accomplish the final prediction. Simulation results verify the effectiveness and superiority of our LSTM-based beam tracking models compared with other state-of-the-art deep learning beam tracking models that also leverage sub-6 GHz channels. Besides, the robustness and generalization of our proposed LSTM models are illustrated through simulations.

Remote first-aid treatment on ambulances is a promising application of 5G. However, there still exist gaps between the capabilities of current 5G networks and the stringent requirements of remote emergency on ambulances. Dual connectivity (DC) is an efficient technology to fill these gaps by integrating 5G millimeter wave (mmWave) with Sub-6GHz networks. In this paper, we investigate a dual-connectivity handover scheme to enhance the transmission rate of the wireless links for a 5G-enabled ambulance. Due to the long delay caused by signal transmission and processing, the conventional handover schemes based on reference signal received power (RSRP) measured by users are not sufficiently sensitive to the rapidly changing propagation environments surrounding the 5G-enabled ambulance. Instead, considering the randomness of environments and the delay caused by the handover process, we employ a deep Q network (DQN)-based algorithm to find a far-sighted policy for solving the handover problem. However, due to the drawbacks of single-step bootstrapping, value overestimation, and low-efficiency exploration, the vanilla DQN is performance-limited. To this end, we adopt effective techniques including multi-step learning, double DQN, and NoisyNet to improve learning performances, and propose a noisy double DQN (NDDQN)-based dual-connectivity handover scheme. Simulation results verify the effectiveness and superiority of our NDDQN-based handover scheme compared with the vanilla DQN and upper confidence bound (UCB)-based handover schemes, and then show that our handover scheme can adapt to various handover models.

Unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) system will play an essential role in future wireless networks, owing to the flexible deployment of the UAVs and the Line-of-Sight (LoS) dominant channels. In this article, we take the limited onboard energy into account and investigate the three-dimensional (3-D) trajectory of the UAV and the transmission scheduling of the ground devices (GDs) for the UAV-enabled IoT system, where multiple GDs transmit data to the UAV in a time-division multiple access fashion. Specifically, taking the angle-dependent probabilistic LoS channel into account, we derive the mathematical expression of the amount of the transmitted data of the GDs and model the energy consumption of the UAV. Next, considering the fairness among the GDs, we aim to maximize the minimum expected amount of the transmitted data of the GDs, and formulate it as an optimization problem, which is further discretized to a problem with a finite number of variables. Due to the nonconvexity of the discretized problem, we transform the problem into a tractable form, and develop an effective iterative algorithm to solve it by using the block coordinate descent and successive convex approximation methods. The convergence and the complexity of the developed algorithm are analytically evaluated. Simulation results demonstrate that our designed 3-D UAV trajectory can effectively improve the minimum expected amount of the transmitted data of the GDs.