TY - JOUR
T1 - Power efficient mobile small cell placement for network-coded cooperation in UDNs
AU - Torre, Roberto
AU - Tayyab, Muhammad
AU - Koudouridis, George
AU - Gelabert, Xavier
AU - Bassoli, Riccardo
AU - Fitzek, Frank H.P.
N1 - Funding Information:
This research has been supported in part by European Union's H2020 research and innovation program under grant agreement H2020-MCSA-ITN-2016-SECRET 722424 [51].
Funding Information:
This research has been supported in part by European Union’s H2020 research and innovation program under grant agreement H2020-MCSA-ITN-2016-SECRET 722424 [51] .
Publisher Copyright:
© 2021 Elsevier B.V.
| openaire: EC/H2020/722424/EU//SECRET
PY - 2021/12/24
Y1 - 2021/12/24
N2 - Ultra-dense Networks (UDNs) massively populate areas with base stations of diverse capabilities, thus increasing the network capacity. Moreover, the radio access network (RAN) architecture moves towards small infrastructure elements such as mobile small cells (MSCs). In this context, Network-coded Cooperation (NCC) leverages the interplay between network coding and cooperative relaying to reliably offload cellular traffic to MSCs and reduce the power consumption in the network. Despite the research done separately on NCC and smart MSC deployment, there is a scarceness of works addressing the smart and on-demand deployment, activation, and deactivation of MSCs to leverage the benefits of both NCC and MSCs. To fill this gap, in this paper, we: (1) estimate the traffic density of New York by adopting an urban zoning (UZ) model; (2) provide a methodology for the on-demand deployment of base stations and MSCs according to a stochastic geometry model; (3) propose two radio resource management (RRM) models, one random and one smart, for the placement and on-demand creation of MSCs, and (4) compare the power consumption of the proposed architecture with 4G edge computing. The results show that the smart RRM model overperforms the random model five-fold in terms of number of pico base stations required, which impact on the power consumed in the network. Moreover, the results show that the smart model achieves between 6%–25% power savings in comparison to 4G edge computing, the random model, and two approaches form the related work, respectively.
AB - Ultra-dense Networks (UDNs) massively populate areas with base stations of diverse capabilities, thus increasing the network capacity. Moreover, the radio access network (RAN) architecture moves towards small infrastructure elements such as mobile small cells (MSCs). In this context, Network-coded Cooperation (NCC) leverages the interplay between network coding and cooperative relaying to reliably offload cellular traffic to MSCs and reduce the power consumption in the network. Despite the research done separately on NCC and smart MSC deployment, there is a scarceness of works addressing the smart and on-demand deployment, activation, and deactivation of MSCs to leverage the benefits of both NCC and MSCs. To fill this gap, in this paper, we: (1) estimate the traffic density of New York by adopting an urban zoning (UZ) model; (2) provide a methodology for the on-demand deployment of base stations and MSCs according to a stochastic geometry model; (3) propose two radio resource management (RRM) models, one random and one smart, for the placement and on-demand creation of MSCs, and (4) compare the power consumption of the proposed architecture with 4G edge computing. The results show that the smart RRM model overperforms the random model five-fold in terms of number of pico base stations required, which impact on the power consumed in the network. Moreover, the results show that the smart model achieves between 6%–25% power savings in comparison to 4G edge computing, the random model, and two approaches form the related work, respectively.
KW - Cellular networks
KW - Mobile small cells
KW - Network traffic
KW - Network-coded cooperation
KW - Power consumption
KW - Stochastic geometry
KW - Urban zoning
UR - http://www.scopus.com/inward/record.url?scp=85119169468&partnerID=8YFLogxK
U2 - 10.1016/j.comnet.2021.108559
DO - 10.1016/j.comnet.2021.108559
M3 - Article
AN - SCOPUS:85119169468
SN - 1389-1286
VL - 201
JO - Computer Networks
JF - Computer Networks
M1 - 108559
ER -