Designing Green C-RAN with limited fronthaul via mixed-integer second order cone programming

This paper considers the downlink transmission of cloud-radio access networks with limited fronthaul capacity constraint. Unlike the existing approaches where power of fronthaul is a quadratic or linear function of respective variables, we consider a more practical model where the power consumed by...

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Veröffentlicht in:IEEE International Conference on Communications (2003) S. 1 - 6
Hauptverfasser: Phuong Luong, Despins, Charles, Gagnon, Francois, Le-Nam Tran
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2017
Schlagworte:
Array signal processing
Cloud radio access networks
energy efficiency
Interference
Iterative methods
limited fronthaul
mixed integer second order cone programming
Numerical models
Power demand
Programming
Wireless communication
ISSN:1938-1883
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Zusammenfassung:This paper considers the downlink transmission of cloud-radio access networks with limited fronthaul capacity constraint. Unlike the existing approaches where power of fronthaul is a quadratic or linear function of respective variables, we consider a more practical model where the power consumed by fronthaul depends on the rate served by the corresponding remote radio head (RRH). Then, we formulate a joint design of RRH selection, RRH-user association, and transmit beamforming for the problem of energy efficiency maximization. The formulated problem is a mixed-integer nonconvex program, which is generally NP-hard. For this nonconvex program, we leverage successive convex approximation (SCA) method to develop efficient iterative algorithms to find a high performance. Particularly, we iteratively approximate the continuous nonconvex constraints by conic ones so that the problem obtained at each iteration is a mixed-integer second order cone programming (MI-SOCP) for which dedicated solvers are available. To further reduce the computational complexity, an algorithm based on continuous relaxation and post-processing is proposed. Results show that our proposed algorithms converge faster and outperform known solutions.
ISSN:1938-1883
DOI:10.1109/ICC.2017.7996785