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Throughput Maximization in Cloud-Radio Access Networks Using Cross-Layer Network Coding
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DOI：10.1109/TMC.2020.3012935.png)
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摘要
En 中文
Cloud radio access networks (C-RANs) are promising paradigms for the fifth-generation (5G) networks due to their interference management capabilities. In a C-RAN, a central processor (CP) is responsible for coordinating multiple Remote Radio Heads (RRHs) and scheduling users to their radio resource blocks (RRBs). In this paper, we develop a novel cross-layer network coding (CLNC) approach that proposes to optimize RRH's transmit powers and user's rates in making the coding decisions. As such, cross-layer throughput of the network is maximized. The joint user scheduling, file encoding, and power adaptation problem is solved by designing a subgraph for each RRB, in which each vertex represents potential user-RRH associations, encoded files, transmission rates, and power levels (PLs) for one RRB. It is then shown that the C-RAN throughput maximization problem is equivalent to a maximum-weight clique problem over the union of all such subgraphs, called herein the CRAN-CLNC graph. Numerical results revealed that the proposed joint and iterative schemes offer improved throughput performances as compared to the existing algorithms in the literature. Compared to our proposed joint scheme, our proposed iterative scheme has a certain degradation, roughly in the range of 9%-14%. This small degradation in the throughput performance of the iterative scheme comes at the achieved low computational complexity as compared to the high complexity of the joint scheme.
Keyword:
Throughput
Optimization
Network coding
Radio access networks
Encoding
Degradation
Complexity theory
Cloud radio access networks
coordinated scheduling
cross-layer network coding
file streaming
power allocation
time-critical applications
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5.8K
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1.8W
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