Resource allocation in wireless networks, such as device-to-device (D2D)
communications, is usually formulated as mixed integer nonlinear programming
(MINLP) problems, which are generally NP-hard and difficult to get the optimal
solutions. Traditional methods to solve these MINLP problems are all based on
mathematical optimization techniques, such as the branch-and-bound (B&B)
algorithm that converges slowly and has forbidding complexity for real-time
implementation. Therefore, machine leaning (ML) has been used recently to
address the MINLP problems in wireless communications. In this paper, we use
imitation learning method to accelerate the B&B algorithm. With invariant
problem-independent features and appropriate problem-dependent feature
selection for D2D communications, a good auxiliary prune policy can be learned
in a supervised manner to speed up the most time-consuming branch process of
the B&B algorithm. Moreover, we develop a mixed training strategy to further
reinforce the generalization ability and a deep neural network (DNN) with a
novel loss function to achieve better dynamic control over optimality and
computational complexity. Extensive simulation demonstrates that the proposed
method can achieve good optimality and reduce computational complexity
simultaneously.

使用机器学习中的模仿学习方法，加速分支定界算法并在深度神经网络中使用创新的损失函数来实现更好的动态控制，以同时实现更好的最优性和更少的计算复杂性。

学习如何分支：加速无线网络资源分配

Learning to Branch: Accelerating Resource Allocation in Wireless  Networks

As wireless video transmission is the fastest-growing form of data traffic,
methods for spectrally efficient video on-demand wireless streaming are
essential to service providers and users alike. A key property of video
on-demand is the asynchronous content reuse, such that a few dominant videos
account for a large part of the traffic, but are viewed by users at different
times. Caching of content on devices in conjunction with D2D communications
allows to exploit this property, and provide a network throughput that is
significantly in excess of both the conventional approach of unicasting from
the base station and the traditional D2D networks for regular data traffic.
This paper presents in a semi-tutorial concise form some recent results on the
throughput scaling laws of wireless networks with caching and asynchronous
content reuse, contrasting the D2D approach with a competing approach based on
combinatorial cache design and network coded transmission from the base station
(BS) only, referred to as coded multicasting. Interestingly, the spatial reuse
gain of the former and the coded multicasting gain of the latter yield, somehow
surprisingly, the same near-optimal throughput behavior in the relevant regime
where the number of video files in the library is smaller than the number of
streaming users. Based on our recent theoretical results, we propose a holistic
D2D system design that incorporates traditional microwave (2 GHz) as well as
millimeter-wave D2D links; the direct connections to the base station can be
used to provide those rare video requests that cannot be found in local caches.
We provide extensive simulations under a variety of system settings, and
compare our scheme with other existing schemes by the BS. We show that, despite
the similar behavior of the scaling laws, the proposed D2D approach offers very
significant throughput gains with respect to the BS-only schemes.

本文介绍了关于具有缓存和异步内容重用的无线网络吞吐量的一些最新研究成果，提出了一种基于 D2D 通信的综合系统设计，可以利用设备上内容的缓存来提供比基站传输和传统 D2D 网络更高的网络吞吐量，并通过与基站的直接连接提供稀有视频请求。