The cloud radio access network (C-RAN) has become the foundational structure for various emerging communication paradigms, leveraging the flexible deployment of distributed access points (APs) and centralized task pro...
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The cloud radio access network (C-RAN) has become the foundational structure for various emerging communication paradigms, leveraging the flexible deployment of distributed access points (APs) and centralized task processing. In this paper, we propose a cross-layer optimization framework based on a practical finite-length coding communication system in C-RAN, aiming at maximizing bandwidth efficiency while providing statistical quality of service (QoS) for individual services. Based on the theoretical results from effective capacity and finite-length coding, we formulate a joint optimization problem involving modulation and coding schemes (MCS), retransmission count, initial bandwidth allocation and AP selection, which reflects the coordinated decision of parameters across the physical layer, data link layer and transport layer. To tackle such a mixed-integer nonlinear programming (MINLP) problem, we firstly decompose it into a transmission parameter decision (TPD) sub-problem and a user association (UA) sub-problem, which can be solved by a binary search-based algorithm and an auction-based algorithm respectively. Simulation results demonstrate that the proposed model can accurately capture the impact of QoS requirements and channel quality on the optimal transmission parameters. Furthermore, compared with fixed transmission parameter setting, the proposed algorithms achieve the bandwidth efficiency gain up to 27.87% under various traffic and channel scenarios.
Erasure networks can usually benefit from both spatial and temporal network coding, i.e., coding across packets both from different network edges as well as from different time slots. In this letter, we propose a join...
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Erasure networks can usually benefit from both spatial and temporal network coding, i.e., coding across packets both from different network edges as well as from different time slots. In this letter, we propose a joint spatial and temporal networking scheme with finite temporal codinglength by extending the batch sparse (BATS) code proposed by Yang and Yeung. For the original BATS code, the batch size is usually set as the temporal codinglength M. With the proposed spatial-temporal BATS code, a larger batch size kM, with k being the min-cut of the network, is used so that the spatial network coding can be applied together with temporal network coding. Simulation results show that the proposed scheme achieves significant throughput gain over the pure spatial or temporal network coding schemes.
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