929 research outputs found
Modeling, Analysis and Design for Carrier Aggregation in Heterogeneous Cellular Networks
Carrier aggregation (CA) and small cells are two distinct features of
next-generation cellular networks. Cellular networks with small cells take on a
very heterogeneous characteristic, and are often referred to as HetNets. In
this paper, we introduce a load-aware model for CA-enabled \textit{multi}-band
HetNets. Under this model, the impact of biasing can be more appropriately
characterized; for example, it is observed that with large enough biasing, the
spectral efficiency of small cells may increase while its counterpart in a
fully-loaded model always decreases. Further, our analysis reveals that the
peak data rate does not depend on the base station density and transmit powers;
this strongly motivates other approaches e.g. CA to increase the peak data
rate. Last but not least, different band deployment configurations are studied
and compared. We find that with large enough small cell density, spatial reuse
with small cells outperforms adding more spectrum for increasing user rate.
More generally, universal cochannel deployment typically yields the largest
rate; and thus a capacity loss exists in orthogonal deployment. This
performance gap can be reduced by appropriately tuning the HetNet coverage
distribution (e.g. by optimizing biasing factors).Comment: submitted to IEEE Transactions on Communications, Nov. 201
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
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