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This work proposes a real-time movement control algorithm for massive unmanned aerial vehicles (UAVs) that provide emergency cellular connections in an urban disaster site. While avoiding the inter-UAV collision under temporal wind dynamics, the proposed algorithm minimizes each UAV’s energy consumption per unit downlink rate. By means of a mean-field game theoretic flocking approach, the velocity control of each UAV only requires its own location and channel states.

In this paper we consider Millimeter wave (mmWave) Massive MIMO systems where a large antenna array at the base station (BS) serves a few scheduled terminals. The high dimensional null space of the channel matrix to the scheduled terminals is utilized to broadcast system information to the non-scheduled terminals on the same time-frequency resource.

In this paper we consider Millimeter wave (mmWave) Massive MIMO systems where a large antenna array at the base station (BS) serves a few scheduled terminals. The high dimensional null space of the channel matrix to the scheduled terminals is utilized to broadcast system information to the non-scheduled terminals on the same time-frequency resource.

In this paper, we consider a simple downlink channel with a multi-antenna base station and two single-antenna receivers. We assume that the channel is deterministic and known to all the nodes. Our contribution is two-fold. First, we show that linear precoding with private streams can have unbounded gap to the capacity of the channel. Second, we show that using rate-splitting with a simple power allocation one can achieve the sum capacity to within a constant gap for any channel realization.

In this work, we study the optimal trajectory of an unmanned aerial vehicle (UAV) acting as a base station (BS) to serve multiple users. Considering multiple flying epochs, we leverage the tools of reinforcement learning (RL) with the UAV acting as an autonomous agent in the environment to learn the trajectory that maximizes the sum rate of the transmission during flying time. By applying Q-learning, a model-free RL technique, an agent is trained to make movement decisions for the UAV. We compare table-based and neural network (NN) approximations of the Q-function and analyze the results.

Much of security research focuses on preventing an adversary from deciphering a message's content, but there are a number of applications that motivate the more challenging goal of ``covert'' communications: transmitter Alice conveying information to legitimate receiver Bob while preventing a capable and attentive adversary Willie from detecting the presence of the message.