TY - GEN
T1 - Distributed energy and resource management for full-duplex dense small cells for 5G
AU - Yadav, Animesh
AU - Dobre, Octavia A.
AU - Ansari, Nirwan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/19
Y1 - 2017/7/19
N2 - We consider a multi-carrier and densely deployed small cell network, where small cells are powered by renewable energy source and operate in a full-duplex mode. We formulate an energy and traffic aware resource allocation optimization problem, where a joint design of the beamformers, power and sub-carrier allocation, and users scheduling is proposed. The problem minimizes the sum data buffer lengths of each user in the network by using the harvested energy. A practical uplink user rate-dependent decoding energy consumption is included in the total energy consumption at the small cell base stations. Hence, harvested energy is shared with both downlink and uplink users. Owing to the non-convexity of the problem, a faster convergence sub-optimal algorithm based on successive parametric convex approximation framework is proposed. The algorithm is implemented in a distributed fashion, by using the alternating direction method of multipliers, which offers not only the limited information exchange between the base stations, but also fast convergence. Numerical results advocate the redesigning of the resource allocation strategy when the energy at the base station is shared among the downlink and uplink transmissions.
AB - We consider a multi-carrier and densely deployed small cell network, where small cells are powered by renewable energy source and operate in a full-duplex mode. We formulate an energy and traffic aware resource allocation optimization problem, where a joint design of the beamformers, power and sub-carrier allocation, and users scheduling is proposed. The problem minimizes the sum data buffer lengths of each user in the network by using the harvested energy. A practical uplink user rate-dependent decoding energy consumption is included in the total energy consumption at the small cell base stations. Hence, harvested energy is shared with both downlink and uplink users. Owing to the non-convexity of the problem, a faster convergence sub-optimal algorithm based on successive parametric convex approximation framework is proposed. The algorithm is implemented in a distributed fashion, by using the alternating direction method of multipliers, which offers not only the limited information exchange between the base stations, but also fast convergence. Numerical results advocate the redesigning of the resource allocation strategy when the energy at the base station is shared among the downlink and uplink transmissions.
KW - 5G
KW - Decoding energy
KW - Energy harvesting communications
KW - Full-duplex communications
KW - Radio resource management
KW - Small cells
KW - Successive parametric convex approximation
UR - http://www.scopus.com/inward/record.url?scp=85027845371&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027845371&partnerID=8YFLogxK
U2 - 10.1109/IWCMC.2017.7986275
DO - 10.1109/IWCMC.2017.7986275
M3 - Conference contribution
AN - SCOPUS:85027845371
T3 - 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017
SP - 133
EP - 139
BT - 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2017
Y2 - 26 June 2017 through 30 June 2017
ER -