SCIENTIA SINICA Informationis, Volume 47, Issue 11: 1566-1582(2017) https://doi.org/10.1360/N112017-00032

## Energy efficient network planning and dynamic control for hyper-cellular network

• ReceivedFeb 14, 2017
• AcceptedJun 7, 2017
• PublishedAug 30, 2017
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### Abstract

Hyper cellular network architecture is proposed to separate control coverage and traffic coverage, to realize flexible and energy-efficient network operations. Specifically, the traffic base stations (BSs), which only handle the data services, can be dynamically switched on/off and offload traffic to adjacent traffic base stations or control BSs for energy saving, according to the network load dynamics. With this new feature leveraged, it is crucial and challenging to revisit the problems of network planning, and dynamic BS sleeping and wireless resource allocation, based on the variation of the network traffic load. For the network planning, based on the stochastic geometry theory, the optimal densities of the control BSs and traffic BSs are derived, with respect to their different network functions and topology features. For energy efficient network control, dynamic BS sleeping and spectrum resource allocation mechanisms are proposed and optimized based on traffic offloading, which can substantially reduce the network energy consumption, with the guarantees on the network coverage and user quality of service.

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• Figure 1

(Color online) Offloading-based base station sleeping under the hyper cellular network architecture

• Figure 2

(Color online) Examples of the topology of the hyper cellular network. (a) Control base station distribution; (b) homogeneous traffic base station distribution

• Figure 3

(Color online) Upper and lower bounds of the optimal traffic base station density [27]@Copyright 2013 IEEE

• Figure 4

(Color online) Illustration of the network planning. (a) Before the capacity expansion; (b) expansion with macro BSs only; (c) expansion with optimal macro BS and micro BS combination

• Figure 5

(Color online) The energy efficiency gain brought by partial spectrum reuse [29]@Copyright 2013 IEEE

• Figure 6

(Color online) Example of spatial clustering for Erlang-3 approximation [38]. (a) Equal inter-subclass distance; (b) equal subclass area @Copyright 2016 Springer

• Figure 7

(Color online) Evaluations of the Erlang-L approximation method [31]@Copyright 2014 IEICE. (a) Approximation error; (b) minimum distance between control BSs

• Figure 8

(Color online) Traffic offloading based BS sleeping [33]. (a) Random sleep; (b) spatial repulsive sleeping @Copyright 2015 IEEE

• Figure 9

(Color online) (a) Daily traffic pattern; (b) ratio of sleeping TBSs [33]@Copyright 2015 IEEE

• Figure 10

(Color online) Comparison of TBS sleeping mechanisms [34]@Copyright 2015 IEEE

• Figure 11

(Color online) Power consumption performance of TBS sleeping mechanisms

• Table 1   Optimal base station density for three typical scenarios (/km$^2$)
 Scenario Optimal BS density Optimal BS density Optimal BS density with noise without noise upper bound Eq. (7) Dense urban 1.2555 1.2390 1.2610 Suburban 0.9229 0.9017 0.9177 Rural 0.1138 0.0542 0.0551
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