2015/05/31 by Dong Liu, Chenyang Yang · 2 citations
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Backhaul (telecommunications) #Base station #Cache #Caching and Content Delivery #Cellular network #Computer network #Computer science #Cooperative Communication and Network Coding #Efficient energy use #Telecommunications #Telecommunications link #Wireless #Wireless network #cs.IT #cs.NI #math.IT
paper · pdf · doi:10.1109/jsac.2016.2549398
Accepted by Journal on Selected Areas in Communications (JSAC), Special Issue on Energy-Efficient Techniques for 5G Wireless Communication Systems
openalex publication_date 2016/03/31 · arxiv created 2016/04/06 · arxiv updated 2016/04/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Caching popular contents at base stations (BSs) can reduce the backhaul cost and improve the network throughput. Yet whether locally caching at the BSs can improve the energy efficiency (EE), a major goal for fifth generation cellular networks, remains unclear. Due to the entangled impact of various factors on EE such as interference level, backhaul capacity, BS density, power consumption parameters, BS sleeping, content popularity, and cache capacity, another important question is what are the key factors that contribute more to the EE gain from caching. In this paper, we attempt to explore the potential of EE of the cache-enabled wireless access networks and identify the key factors. By deriving closed-form expression of the approximated EE, we provide the condition when the EE can benefit from caching, find the optimal cache capacity that maximizes the network EE, and analyze the maximal EE gain brought by caching. We show that caching at the BSs can improve the network EE when power efficient cache hardware is used. When local caching has EE gain over not caching, caching more contents at the BSs may not provide higher EE. Numerical and simulation results show that the caching EE gain is large when the backhaul capacity is stringent, interference level is low, content popularity is skewed, and when caching at pico BSs instead of macro BSs.