Open AccessEnergy efficient millimetre‐wave fronthaul and OFDMA resource optimisation in C‐RANs
Author(s) -
Jing Zewei,
Qin Meng,
Yang Qinghai,
Kwak KyungSup,
Rao Ramesh R.
Publication year - 2018
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2018.0101
Subject(s) - computer science , backhaul (telecommunications) , benchmark (surveying) , orthogonal frequency division multiple access , efficient energy use , wireless , resource allocation , spectral efficiency , mathematical optimization , fractional programming , orthogonal frequency division multiplexing , computer network , nonlinear programming , telecommunications , channel (broadcasting) , nonlinear system , mathematics , engineering , physics , geodesy , quantum mechanics , electrical engineering , geography
Recently, millimetre‐wave (mmWave) wireless fronthauls have been regarded as an effective solution to deploy remote radio heads with higher flexibility and efficiency in cloud radio access networks (C‐RANs). Different from the traditional fibre fronthauls, in order to maximise the utilisation of the time‐frequency resource, the mmWave wireless fronthauls are more expected to operate in a dynamic allocation manner. In this study, the energy efficient mmWave fronthaul and OFDMA resource optimisation in C‐RANs is investigated. The TDMA‐based fronthaul allocation mechanism is first presented and then the joint resource optimisation is formulated as an energy efficiency (EE) maximisation problem which is in the form of a mixed‐integer non‐linear fractional programming (MINLFP) problem. By taking advantage of the Dinkelbach method, the MINLFP problem is transformed into a subtractive optimisation problem and solved by using the Lagrange dual decomposition theory. Moreover, a maximal weighted bipartite graph matching approach is proposed to determine the optimal resource block allocation. Finally, extensive simulation results are provided to evaluate the EE performance of the proposed algorithm by comparing with several benchmark schemes, and it shows that the proposed algorithm can achieve great EE performance gain over the benchmark schemes.