The vision of next-generation TV is to support media services to achieve sharing of cross-domain experience, and the eMBB scenario of the 5G network is one of its important driving forces. Considering the bandwidth and resource requirements of different services, such as unicast and multicast services of multimedia TV broadcasting, rationally allocating resources while providing high-quality services and realizing green energy savings of base stations is one of the challenges. This paper is aimed at the resource allocation for TV multimedia service in the 5G wireless cloud network (C-RAN) scenario, which can support unicast services for cellular users and multicast services for broadcast services simultaneously, and it proposes the corresponding slice resources allocation architecture based on the concept of a self-organizing network. The management architecture first builds the functions and processes of the corresponding autonomous resource management. Based on the multidimensional data, an effective deep learning model named LSTM (long short-term memory) is used to construct the dynamic traffic model of the multicast service in space-time, which provides a basis for further network resource allocation. Based on the prediction results and the condition of satisfying the changing requirements of users, the corresponding optimization model is constructed with the goal of minimizing the energy usage of the RRHs (remote radio heads) and taking the QoS constraints of the users into account. A deep reinforcement learning (DRL) framework combined with a convex optimization method are then used to complete the users’ bandwidth and power resource allocation. The experimental results show that the proposed method can not only predict the multicast service requirement accurately but also effectively improve the energy efficiency of the network under targeted QoS requirements along with time variations.

中文翻译：

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基于深度学习的 5G 宽带电视服务资源分配

下一代电视的愿景是支持媒体业务实现跨域体验共享，5G网络的eMBB场景是其重要的推动力之一。考虑多媒体电视广播的单播、组播等不同业务的带宽和资源需求，在提供优质服务的同时合理分配资源，实现基站绿色节能是挑战之一。本文针对5G无线云网络(C-RAN)场景下电视多媒体业务的资源分配，可以同时支持蜂窝用户的单播业务和广播业务的组播业务，提出了相应的切片资源分配架构。基于自组织网络的概念。管理架构首先构建相应的自治资源管理的功能和流程。基于多维数据，利用名为LSTM（long short-term memory）的有效深度学习模型构建时空多播业务的动态流量模型，为进一步的网络资源分配提供依据。根据预测结果和满足用户不断变化的需求的条件，以最小化RRH（远程射频头）的能量使用为目标，并考虑用户的QoS约束，构建相应的优化模型。然后使用深度强化学习（DRL）框架结合凸优化方法来完成用户的带宽和功率资源分配。