Leveraging intelligence from network CDR data for interference aware energy consumption minimization

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better Qo

Planning Solar in Energy-managed Cellular Networks

There has been a lot of interest recently on the energy efficiency and environmental impact of wireless networks. Given that the base stations are the network elements that use most of this energy, much research has dealt with ways to reduce the energy used by the base stations by turning them off during periods of low load. In addition to this, installing a solar harvesting sys- tem composed of solar panels, batteries, charge con- trollers and inverters is another way to further reduce the network environmental impact and some research has been dealing with this for individual base stations. In this paper, we show that both techniques are tightly coupled. We propose a mathematical model that captures the synergy between solar installation over a network and the dynamic operation of energy-managed base stations. We study the interactions between the two methods for networks of hundreds of base stations and show that the order in which each method is intro- duced into the system does make a difference in terms of cost and performance. We also show that installing solar is not always the best solution even when the unit cost of the solar energy is smaller than the grid cost. We conclude that planning the solar installation and energy management of the base stations have to be done jointly

Energy Efficiency Analysis of Heterogeneous Cache-enabled 5G Hyper Cellular Networks

The emerging 5G wireless networks will pose extreme requirements such as high throughput and low latency. Caching as a promising technology can effectively decrease latency and provide customized services based on group users behaviour (GUB). In this paper, we carry out the energy efficiency analysis in the cache-enabled hyper cellular networks (HCNs), where the macro cells and small cells (SCs) are deployed heterogeneously with the control and user plane (C/U) split. Benefiting from the assistance of macro cells, a novel access scheme is proposed according to both user interest and fairness of service, where the SCs can turn into semi- sleep mode. Expressions of coverage probability, throughput and energy efficiency (EE) are derived analytically as the functions of key parameters, including the cache ability, search radius and backhaul limitation. Numerical results show that the proposed scheme in HCNs can increase the network coverage probability by more than 200% compared with the single- tier networks. The network EE can be improved by 54% than the nearest access scheme, with larger research radius and higher SC cache capacity under lower traffic load. Our performance study provides insights into the efficient use of cache in the 5G software defined networking (SDN)

Energy efficiency in heterogeneous wireless access networks

In this article, we bring forward the important aspect of energy savings in wireless access networks. We specifically focus on the energy saving opportunities in the recently evolving heterogeneous networks (HetNets), both Single- RAT and Multi-RAT. Issues such as sleep/wakeup cycles and interference management are discussed for co-channel Single-RAT HetNets. In addition to that, a simulation based study for LTE macro-femto HetNets is presented, indicating the need for dynamic energy efficient resource management schemes. Multi-RAT HetNets also come with challenges such as network integration, combined resource management and network selection. Along with a discussion on these challenges, we also investigate the performance of the conventional WLAN-first network selection mechanism in terms of energy efficiency (EE) and suggest that EE can be improved by the application of intelligent call admission control policies