Throughput Maximization in Cloud-Radio Access Networks Using Cross-Layer Network Coding

Abstract

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.

Low-Complexity Power Allocation for Network-Coded User Scheduling in Fog-RANs

“…As a result, all the scheduled UEs can decode the requested files sent by their associated RRBs/F-APs. With a single power control subgraph and efficient power allocations of RRBs/F-APs, our solution offers similar performance of the scheme in [11] for perfectly correlated channels. For the un-correlated channels, our proposed scheme has certain degradation in the throughput performance as compared to the one in [11] (a maximum degradation of 27%).…”

“…With a single power control subgraph and efficient power allocations of RRBs/F-APs, our solution offers similar performance of the scheme in [11] for perfectly correlated channels. For the un-correlated channels, our proposed scheme has certain degradation in the throughput performance as compared to the one in [11] (a maximum degradation of 27%). This degradation in the performance of the solution comes at the achieved significant reduction of computational complexity since it is not related anymore to the number of RRBs and their corresponding NC combinations.…”

“…The throughput maximization problem in FRANs/CRANs has been considered in several works, e.g., [8]- [11]. These solutions can be categorized into uncoded [8], [9], and RA-IDNC [10], [11] methods. As their name indicates, the uncoded approach does not utilize NC and assigns a single UE to each RRB.…”

“…This requires a number of RRBs that is equal to the number of UEs in the network. However, it has been noticed that UEs tend to have a common interest in downloading the same files, especially videos, within a small interval of time [11]. By exploiting such UEs' common interest using NC, work [11] efficiently utilizes the RRBs to maximize the throughput by mixing files.…”

“…The aforementioned work [11] showed that the joint approach provides significant performance gain as compared to existing schemes. However, its complexity increases significantly with the number of NC combinations, F-APs, and RRBs, which may not be feasible for massive networks.…”