Energy-efficient resource allocation in C-RAN with fronthaul rate constraints
Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can solve lots of challenges in the next generation mobile communication system, including the demand for higher energy efficiency(EE). In C-RAN EE research field, most of recent work focuses on systematic energy saving...
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          | Published in | International Conference on Wireless Communications and Signal Processing pp. 1 - 6 | 
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| Main Authors | , , , | 
| Format | Conference Proceeding | 
| Language | English | 
| Published | 
            IEEE
    
        01.10.2016
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| Subjects | |
| Online Access | Get full text | 
| ISSN | 2472-7628 | 
| DOI | 10.1109/WCSP.2016.7752729 | 
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| Summary: | Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can solve lots of challenges in the next generation mobile communication system, including the demand for higher energy efficiency(EE). In C-RAN EE research field, most of recent work focuses on systematic energy saving and ignores the needs of users. To improve the performance of C-RAN under fronthaul capacity constraint, signal quantization techniques have been developed. But how to introduce `quantization' into C-RAN EE field is still an open issue. Motivated by this, in this paper, based on informational-optimal Gaussian quantization, we intend to design the suitable algorithms to maximize user-centric EE in the uplink communication of an orthogonal frequency division multiple access (OFDMA) based C-RAN. In the special case of single user and single RRH, we propose a joint optimization algorithm to maximize the uplink user-centric EE by optimizing power and fronthaul rate allocation. In the extended general case of multi-user and multi-RRH, we propose a Modified Particle Swarm Optimization(M-PSO) algorithm to solve the non-linear and non-convex issue for simplicity. Our simulation results show the proposed algorithms can improve the user-centric EE obviously compared with other optimal algorithms. | 
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| ISSN: | 2472-7628 | 
| DOI: | 10.1109/WCSP.2016.7752729 |