TY - GEN
T1 - Joint Computation and Communication Resource Allocation for Energy-Efficient Mobile Edge Networks
AU - Opadere, Johnson
AU - Liu, Qiang
AU - Zhang, Ning
AU - Han, Tao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In this paper, an ultra-dense mobile edge network is studied, where base stations (BSs) are equipped with computation resources to execute users' offloaded tasks. Although an ultradense BS deployment provides seamless coverage and reduced computation latency of the offloaded tasks, the cost of network power consumption is increased. We formulate an optimization problem to jointly optimize active BSs set, uplink and downlink beamforming vector selection, and computation resource allocation in order to tackle the power consumption and latency tradeoff. To efficiently solve this problem, we propose a sequential solution framework. Specifically, we first select the active BSs based on communication and computation power-aware selection rule. The computation resources and dual-link beamformers are subsequently optimized for the satisfaction of task computation deadline, network energy savings and improved coverage. Simulation results show that the proposed joint optimization framework significantly reduces the network power consumption.
AB - In this paper, an ultra-dense mobile edge network is studied, where base stations (BSs) are equipped with computation resources to execute users' offloaded tasks. Although an ultradense BS deployment provides seamless coverage and reduced computation latency of the offloaded tasks, the cost of network power consumption is increased. We formulate an optimization problem to jointly optimize active BSs set, uplink and downlink beamforming vector selection, and computation resource allocation in order to tackle the power consumption and latency tradeoff. To efficiently solve this problem, we propose a sequential solution framework. Specifically, we first select the active BSs based on communication and computation power-aware selection rule. The computation resources and dual-link beamformers are subsequently optimized for the satisfaction of task computation deadline, network energy savings and improved coverage. Simulation results show that the proposed joint optimization framework significantly reduces the network power consumption.
KW - Base station sleep-mode
KW - Computation resource optimization
KW - Mobile edge computing
UR - http://www.scopus.com/inward/record.url?scp=85070231115&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070231115&partnerID=8YFLogxK
U2 - 10.1109/ICC.2019.8761886
DO - 10.1109/ICC.2019.8761886
M3 - Conference contribution
AN - SCOPUS:85070231115
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Communications, ICC 2019
Y2 - 20 May 2019 through 24 May 2019
ER -