Reference : Energy-efficient coordinated transmission for cloud-RANs: Algorithm design and trade-off
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
http://hdl.handle.net/10993/47091
Energy-efficient coordinated transmission for cloud-RANs: Algorithm design and trade-off
English
Ha, Vu Nguyen mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Le, Long Bao []
Dao, Ngoc-Dung []
19-Mar-2014
2014 48th Annual Conference on Information Sciences and Systems (CISS)
IEEE
Yes
International
978-1-4799-3001-2
Princeton, NJ
USA
2014 48th Annual Conference on Information Sciences and Systems (CISS)
from 19-03-2014 to 21-03-2014
[en] Cloud radio access network (Cloud-RAN) ; beamforming ; clustering
[en] In this paper, we consider the energy-efficient co-ordinated transmission design for downlink transmission in the cloud radio access network (Cloud-RAN) considering fronthaul capacity and user QoS constraints. Specifically, we assume that baseband signals are processed in the cloud, which are delivered to remote radio heads (RRHs) equipped with multiple antennas over fronthaul links for transmissions to single-antenna users. The design amounts to determine the set of RRHs to serve each user as well as the precoding and power levels for downlink transmission while maintaining the fronthaul capacity and user QoS constraints. Toward this end, we study the two closely-related problems, namely pricing-based total power and fronthaul capacity tradeoff (PFT) and fronthaul-constrained power minimization (FCPM) problems. We employ the concave approximation and gradient search methods to solve the PFT problem for the given pricing coefficients, which capture the power and fronthaul capacity tradeoff. Then, we develop an efficient algorithm to address the FCPM problem by iteratively solving the PFT problem while intelligently updating the pricing coefficients. Numerical results confirm the excellent performance of the our proposed algorithms and illustrate underlying tradeoffs among total transmission power, fronthaul capacity, and cluster size.
http://hdl.handle.net/10993/47091

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