It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communication performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality. Nonetheless, as wireless networks become increasingly diverse and complex, for example, in the high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning, with many success stories in various disciplines, represents a promising alternative due to its remarkable power to leverage data for problem solving. In this article, we discuss the key motivations and roadblocks of using deep learning for wireless resource allocation with application to vehicular networks. We review major recent studies that mobilize the deep-learning philosophy in wireless resource allocation and achieve impressive results. We first discuss deep-learning-assisted optimization for resource allocation. We then highlight the deep reinforcement learning approach to address resource allocation problems that are difficult to handle in the traditional optimization framework. We also identify some research directions that deserve further investigation.

中文翻译：

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基于深度学习的无线资源分配与车载网络的应用

长期以来，人们一直认为明智的资源分配对于减轻干扰、提高网络效率和最终优化无线通信性能至关重要。传统智慧是将资源分配明确地表述为优化问题，然后利用数学规划将问题解决到一定的最优水平。尽管如此，随着无线网络变得越来越多样化和复杂，例如在高移动性车载网络中，当前的设计方法面临着重大挑战，因此需要重新思考传统的设计理念。同时，深度学习在各个学科都有许多成功案例，由于其利用数据解决问题的非凡能力，它代表了一种很有前途的替代方案。在本文中，我们讨论了将深度学习用于无线资源分配并应用于车载网络的主要动机和障碍。我们回顾了近期的主要研究，这些研究在无线资源分配中运用了深度学习理念并取得了令人瞩目的成果。我们首先讨论用于资源分配的深度学习辅助优化。然后，我们重点介绍了深度强化学习方法来解决传统优化框架中难以处理的资源分配问题。我们还确定了一些值得进一步研究的研究方向。我们回顾了近期的主要研究，这些研究在无线资源分配中运用了深度学习理念并取得了令人瞩目的成果。我们首先讨论用于资源分配的深度学习辅助优化。然后，我们重点介绍了深度强化学习方法来解决传统优化框架中难以处理的资源分配问题。我们还确定了一些值得进一步研究的研究方向。我们回顾了近期的主要研究，这些研究在无线资源分配中运用了深度学习理念并取得了令人瞩目的成果。我们首先讨论用于资源分配的深度学习辅助优化。然后，我们重点介绍了深度强化学习方法来解决传统优化框架中难以处理的资源分配问题。我们还确定了一些值得进一步研究的研究方向。