TY - JOUR
T1 - Energy-efficient resource allocation for heterogeneous services in OFDMA downlink networks
T2 - Systematic perspective
AU - Xu, Quansheng
AU - Li, Xi
AU - Ji, Hong
AU - Du, Xiaojiang
PY - 2014/6
Y1 - 2014/6
N2 - In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time-frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time-frequency power-resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations.
AB - In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time-frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time-frequency power-resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations.
KW - Energy efficiency (EE)
KW - heterogeneous service
KW - mixed combinatorial and nonconvex optimization
KW - orthogonal frequency-division multiple-access (OFDMA) network
KW - resource allocation
UR - http://www.scopus.com/inward/record.url?scp=84902975885&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84902975885&partnerID=8YFLogxK
U2 - 10.1109/TVT.2014.2312288
DO - 10.1109/TVT.2014.2312288
M3 - Article
AN - SCOPUS:84902975885
SN - 0018-9545
VL - 63
SP - 2071
EP - 2082
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 5
M1 - 6774977
ER -