TY - GEN
T1 - Learning-based joint power and channel assignment for hyper dense 5G networks
AU - Arani, Atefeh Hajijamali
AU - Mehbodniya, Abolfazl
AU - Omidi, Mohammad Javad
AU - Adachi, Fumiyuki
PY - 2016/7/12
Y1 - 2016/7/12
N2 - Next generation mobile networks will face the unprecedented demand for higher data rates. To satisfy this demand, the dense deployment of heterogeneous wireless networks (HetNets) is a promising solution. One of the major challenges in dense HetNets is to dynamically allocate the resources such as power and channel so that the energy efficiency and throughput of the network improve. One of the important techniques for improving the energy efficiency of the base station (BS) is BS ON-OFF switching which allows the BS to turn off some of its components in lower load situations. On the other side, due to the proximity of BSs in the dense HetNets, co-channel interference (CCI) becomes a critical problem and significantly impacts the performance of the network. In this paper, we propose a dynamic channel assignment based on a learning algorithm (DCA-LA). Moreover, we combine DCA-LA with a BS ON-OFF switching algorithm in order to improve the energy efficiency of the system. In particular, the proposed DCA-LA/ON-OFF switching algorithm is self-organizing and performs in a fully distributed manner. Simulation results indicate that our proposed algorithm balances load among BSs and yields better performance in terms of average energy consumption, average load, average utility per BS and average rate per user, compared to the baseline algorithms.
AB - Next generation mobile networks will face the unprecedented demand for higher data rates. To satisfy this demand, the dense deployment of heterogeneous wireless networks (HetNets) is a promising solution. One of the major challenges in dense HetNets is to dynamically allocate the resources such as power and channel so that the energy efficiency and throughput of the network improve. One of the important techniques for improving the energy efficiency of the base station (BS) is BS ON-OFF switching which allows the BS to turn off some of its components in lower load situations. On the other side, due to the proximity of BSs in the dense HetNets, co-channel interference (CCI) becomes a critical problem and significantly impacts the performance of the network. In this paper, we propose a dynamic channel assignment based on a learning algorithm (DCA-LA). Moreover, we combine DCA-LA with a BS ON-OFF switching algorithm in order to improve the energy efficiency of the system. In particular, the proposed DCA-LA/ON-OFF switching algorithm is self-organizing and performs in a fully distributed manner. Simulation results indicate that our proposed algorithm balances load among BSs and yields better performance in terms of average energy consumption, average load, average utility per BS and average rate per user, compared to the baseline algorithms.
KW - Co-Channel Interference
KW - Energy Efficiency
KW - Heterogeneous Networks
KW - Learning Algorithm
UR - http://www.scopus.com/inward/record.url?scp=84981350434&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84981350434&partnerID=8YFLogxK
U2 - 10.1109/ICC.2016.7511450
DO - 10.1109/ICC.2016.7511450
M3 - Conference contribution
AN - SCOPUS:84981350434
T3 - 2016 IEEE International Conference on Communications, ICC 2016
BT - 2016 IEEE International Conference on Communications, ICC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Communications, ICC 2016
Y2 - 22 May 2016 through 27 May 2016
ER -