Joint Route Selection and Content Caching in O-RAN Architecture

The Open Radio Access Network (O-RAN) architecture offers flexible association of distributed network elements, i.e., O-RAN elements, for establishing on-demand radio access network (RAN) stacks [1]. In such as distributed architecture, content caching may help relieve network traffic congestion and end-to-end latency. Leveraging the strengths of both O-RAN architecture and content caching is thus essential for reducing network traffic while meeting user's quality of experience (QoE) requirements. However, integrating content caching into O-RAN architecture is not straightforward as the content placement policy and the O-RAN element association are tightly coupled. In this paper, we investigate the problem of joint O-RAN element association and content placement in the O-RAN architecture, aiming at minimizing the average long-term traffic incurred in the xHaul transport networks. To this end, a line-search based algorithm is proposed to find efficient O-RAN element association solution; meanwhile, a deep reinforcement learning (DRL) based algorithm is proposed to obtain an efficient content placement policy while being able to deal with large action space issue. The efficacy of our proposed framework is confirmed through extensive numerical results.