Deep Learning-Based Resource Allocation for 5G Broadband TV Service

Abstract

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.