This letter investigates the resource allocation problem for multiple Unmanned Aerial Vehicles (UAVs)-served Machine-to-Machine (M2M) communications. Our goal is to maximize the sum-rate of UAVs-served M2M communications by jointly considering the transmission power, transmission mode, frequency spectrum, relay selection and the trajectory of UAVs. In order to model the uncertainty of stochastic environments, we formulate the resource allocation problem to be a Markov game, which is the generalization of Markov Decision Process (MDP) for the case of multiple agents. However, owning to the UAVs mobility poses the difficulty of perceiving the environment, we propose a Long Short-Term Memory (LSTM) with Generative Adversarial Networks (GANs) framework to better track and forecast the UAVs mobility and improving the network reward. Numerical results demonstrate that the proposed framework outperforms the conventional LSTM and Deep Q-Network (DQN) algorithms.

中文翻译：

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无人机服务 M2M 通信中资源分配的生成对抗 LSTM 网络学习


这封信调查了多个无人机 (UAV) 服务的机器对机器 (M2M) 通信的资源分配问题。我们的目标是通过综合考虑无人机的发射功率、传输模式、频谱、中继选择和轨迹，最大化无人机服务的M2M通信的总速率。为了对随机环境的不确定性进行建模，我们将资源分配问题表述为马尔可夫博弈，这是马尔可夫决策过程（MDP）在多智能体情况下的推广。然而，由于无人机的移动性带来了感知环境的困难，我们提出了一种带有生成对抗网络（GAN）框架的长短期记忆（LSTM），以更好地跟踪和预测无人机的移动性并提高网络奖励。数值结果表明，所提出的框架优于传统的 LSTM 和深度 Q 网络（DQN）算法。