TY - GEN
T1 - Energy Harvesting in Heterogeneous Networks with Hybrid Powered Communication Systems
AU - Alsharoa, Ahmad
AU - Celik, Abdulkadir
AU - Kamal, Ahmed E.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2018/2/12
Y1 - 2018/2/12
N2 - In this paper, we investigate an energy efficient and energy harvesting (EH) system model in heterogeneous networks (HetNets) where all base stations (BSS) are equipped to harvest energy from renewable energy sources. We consider a hybrid power supply of green (renewable) and traditional micro-grid, such that traditional micro-grid is not exploited as long as the BSS can meet their power demands from harvested and stored green energy. Therefore, our goal is to minimize the networkwide energy consumption subject to users' certain quality of service and BSS' power consumption constraints. As a result of binary BS sleeping status and user-cell association variables, proposed is formulated as a binary linear programming (BLP) problem. A green communication algorithm based on binary particle swarm optimization is implemented to solve the problem with low complexity time.
AB - In this paper, we investigate an energy efficient and energy harvesting (EH) system model in heterogeneous networks (HetNets) where all base stations (BSS) are equipped to harvest energy from renewable energy sources. We consider a hybrid power supply of green (renewable) and traditional micro-grid, such that traditional micro-grid is not exploited as long as the BSS can meet their power demands from harvested and stored green energy. Therefore, our goal is to minimize the networkwide energy consumption subject to users' certain quality of service and BSS' power consumption constraints. As a result of binary BS sleeping status and user-cell association variables, proposed is formulated as a binary linear programming (BLP) problem. A green communication algorithm based on binary particle swarm optimization is implemented to solve the problem with low complexity time.
UR - http://hdl.handle.net/10754/627846
UR - https://ieeexplore.ieee.org/document/8288140/
UR - http://www.scopus.com/inward/record.url?scp=85045242547&partnerID=8YFLogxK
U2 - 10.1109/VTCFall.2017.8288140
DO - 10.1109/VTCFall.2017.8288140
M3 - Conference contribution
SN - 9781509059355
SP - 1
EP - 5
BT - 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -
