Reference : Resource allocation for uplink OFDMA C-RANs with limited computation and fronthaul ca...
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/10993/47085
Resource allocation for uplink OFDMA C-RANs with limited computation and fronthaul capacity
English
Ha, Vu Nguyen mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Le, Long Bao []
22-May-2016
2016 IEEE International Conference on Communications (ICC) proceedings
IEEE
Yes
978-1-4799-6664-6
Kuala Lumpur
Malaysia
2016 IEEE International Conference on Communications (ICC)
from 22-05-2016 to 27-05-2016
[en] uplink OFDMA C-RAN ; limited computation and fronthaul capacity ; system sum rate maximization
[en] This paper considers the joint fronthaul resource and rate allocation for the OFDMA uplink cloud radio access networks (C-RANs). This amounts to determine users' transmission rates and quantization bit allocation for I/Q baseband signals, which must be transferred from remote radio heads (RRHs) to the cloud over the capacity-limited fronthaul network. Our design aims at maximizing the system sum rate through optimal allocation of fronthaul capacity and cloud computation resources. Toward this end, we propose a novel two-stage approach to solve the underlying non-linear integer problem. In the first stage, we relax the integer variables to attain a relaxed problem, which is solved by employing a pricing-based method. Interestingly, we show that the pricing-based problem is convex with respect to each optimization variable, which can be, therefore, solved efficiently. In addition, we develop a novel mechanism to iteratively update the pricing parameter which is proved to converge. In the second stage, we propose two different rounding strategies, which are applied to the obtained continuous solution of the relaxed problem to achieve a feasible solution for the original problem. Finally, we present numerical results to demonstrate the significant sum-rate gains of our proposed design with respect to a standard greedy algorithm.
http://hdl.handle.net/10993/47085

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