Network slicing appears as a key enabler for the future 5G networks. Mobile Network Operators create various slices for Service Providers (SP) to accommodate customized services. As network slices are operated on a common network infrastructure owned by some Infrastructure Provider (InP), sharing the resources across a set of network slices is important for future deployment. Moreover, in many situations, slices have to be deployed over some geographical area: coverage as well as minimum per-user rate constraints have then to be taken into account. Usually, the various Service Function Chains (SFCs) belonging to a slice are deployed on a best-effort basis. Nothing ensures that the InP will be able to allocate enough resources to cope with the increasing demands of some SP. This paper takes the InP perspective and proposes a slice resource provisioning approach to cope with multiple slice demands in terms of computing, storage, coverage, and rate constraints. The resource requirements of the various Service Function Chains to be deployed within a slice are aggregated within a graph of Slice Resource Demands (SRD). Coverage and rate constraints are also taken into account in the SRD. Infrastructure nodes and links have then to be provisioned so as to satisfy all types of resource demands. This problem leads to a Mixed Integer Linear Programming formulation. A two-step deployment approach is considered, with several variants, depending on whether the constraints of each slide to be deployed are taken into account sequentially or jointly. Once provisioning has been performed, any slice deployment strategy may be considered on the reduced-size infrastructure graph representing the nodes and links on which resources have been provisioned. Simulation results demonstrate the effectiveness of the proposed approach compared to a more classical direct slice embedding approach.
In @cite_34 , a network slicing framework for (H-CRAN) is introduced. The sharing of radio resources in terms of data rate is considered, with some constraints related to the fronthaul capacity, the transmission power budget of RRHs, or the tolerable interference threshold of an RRH on a sub-channel. Slicing is formulated as a weighted throughput maximization problem, which aims at maximizing the total rate obtained by users connected to given RRHs on given sub-channels. Nevertheless, the proposed framework does not consider computing and memory resource associated to the processing within the BBUs. Such resource is assumed to be properly scaled so as to support the required service rate. Moreover, the proposed framework addresses only downlink data services.
@cite_34
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Research on network slicing for multi-tenant heterogeneous cloud radio access networks (H-CRANs) is still in its infancy. In this paper, we redefine network slicing and propose a new network slicing framework for multi-tenant H-CRANs. In particular, the network slicing process is formulated as a weighted throughput maximization problem that involves sharing of computational resources, fronthaul capacity, physical remote radio heads and radio resources. The problem is then jointly solved using a sub-optimal greedy approach and a dual decomposition method. Simulation results demonstrate that the framework can flexibly scale the throughput performance of multiple tenants according to the user priority weights associated with the tenants. A key problem in the design of cloud radio access networks (CRANs) is that of devising effective baseband compression strategies for transmission on the fronthaul links connecting a remote radio head (RRH) to the managing central unit (CU). Most theoretical works on the subject implicitly assume that the RRHs, and hence the CU, are able to perfectly recover time synchronization from the baseband signals received in the uplink, and focus on the compression of the data fields. This paper instead dose not assume a priori synchronization of RRHs and CU, and considers the problem of fronthaul compression design at the RRHs with the aim of enhancing the performance of time and phase synchronization at the CU. The problem is tackled by analyzing the impact of the synchronization error on the performance of the link and by adopting information and estimationtheoretic performance metrics such as the rate-distortion function and the Cramer-Rao bound (CRB). The proposed algorithm is based on the Charnes-Cooper transformation and on the Difference of Convex (DC) approach, and is shown via numerical results to outperform conventional solutions. This paper studies the application of non-orthogonal multiple access (NOMA) to cache-enabled cloud radio access network (CRAN) with mixed multicast and unicast transmission. Users requesting the same content are grouped together and served with a cluster of remote radio heads (RRHs) using distributed beamforming. In addition, the user with better channel condition in each group is allowed to request an extra unicast content via the NOMA protocol. Each RRH has a local cache which enables it to acquire the requested contents either from the local cache or from the central processor via the fronthaul link. Taking the maximum fronthaul capacity into consideration, we investigate the subchannel (SC) allocation problem to both RRHs and multicast groups to improve the weighted network sum rate. The optimal solution requires exhaustive search, which become prohibitively complicated as the number of RRHs and groups increases. To tackle this problem effectively, we formulate this problem as a three-sided matching problem among SCs, RRHs and multicast groups, and propose a novel low-complexity matching algorithm. We prove mathematically that the proposed algorithm converges to a stable matching within limited number of iterations. Numerical results unveil that the proposed algorithm closely approaches the optimal solution and outperforms the conventional orthogonal multiple access (OMA)-based CRAN. This paper studies the resource allocation algorithm design for secure information and renewable green energy transfer to mobile receivers in distributed antenna communication systems. In particular, distributed remote radio heads (RRHs antennas) are connected to a central processor (CP) via capacity-limited backhaul links to facilitate joint transmission. The RRHs and the CP are equipped with renewable energy harvesters and share their energies via a lossy micropower grid for improving the efficiency in conveying information and green energy to mobile receivers via radio frequency signals. The considered resource allocation algorithm design is formulated as a mixed nonconvex and combinatorial optimization problem taking into account the limited backhaul capacity and the quality-of-service requirements for simultaneous wireless information and power transfer (SWIPT). We aim at minimizing the total network transmit power when only imperfect channel state information of the wireless energy harvesting receivers, which have to be powered by the wireless network, is available at the CP. In light of the intractability of the problem, we reformulate it as an optimization problem with binary selection, which facilitates the design of an iterative resource allocation algorithm to solve the problem optimally using the generalized Bender's decomposition (GBD). Furthermore, a suboptimal algorithm is proposed to strike a balance between computational complexity and system performance. Simulation results illustrate that the proposed GBD-based algorithm obtains the global optimal solution and the suboptimal algorithm achieves a close-to-optimal performance. In addition, the distributed antenna network for SWIPT with renewable energy sharing is shown to require a lower transmit power compared with a traditional system with multiple colocated antennas.