24,698 research outputs found
Hybrid Scheduling/Signal-Level Coordination in the Downlink of Multi-Cloud Radio-Access Networks
In the context of resource allocation in cloud-radio access networks, recent
studies assume either signal-level or scheduling-level coordination. This
paper, instead, considers a hybrid level of coordination for the scheduling
problem in the downlink of a multi-cloud radio-access network, as a means to
benefit from both scheduling policies. Consider a multi-cloud radio access
network, where each cloud is connected to several base-stations (BSs) via high
capacity links, and therefore allows joint signal processing between them.
Across the multiple clouds, however, only scheduling-level coordination is
permitted, as it requires a lower level of backhaul communication. The frame
structure of every BS is composed of various time/frequency blocks, called
power-zones (PZs), and kept at fixed power level. The paper addresses the
problem of maximizing a network-wide utility by associating users to clouds and
scheduling them to the PZs, under the practical constraints that each user is
scheduled, at most, to a single cloud, but possibly to many BSs within the
cloud, and can be served by one or more distinct PZs within the BSs' frame. The
paper solves the problem using graph theory techniques by constructing the
conflict graph. The scheduling problem is, then, shown to be equivalent to a
maximum-weight independent set problem in the constructed graph, in which each
vertex symbolizes an association of cloud, user, BS and PZ, with a weight
representing the utility of that association. Simulation results suggest that
the proposed hybrid scheduling strategy provides appreciable gain as compared
to the scheduling-level coordinated networks, with a negligible degradation to
signal-level coordination
Energy-Efficient Resource Allocation in Cloud and Fog Radio Access Networks
PhD ThesisWith the development of cloud computing, radio access networks (RAN) is migrating to fully or partially centralised architecture, such as Cloud RAN (C- RAN) or Fog RAN (F-RAN). The novel architectures are able to support new applications with the higher throughput, the higher energy e ciency and the better spectral e ciency performance. However, the more complex energy consumption features brought by these new architectures are challenging. In addition, the usage of Energy Harvesting (EH) technology and the computation o oading in novel architectures requires novel resource allocation designs.This thesis focuses on the energy e cient resource allocation for Cloud and Fog RAN networks. Firstly, a joint user association (UA) and power allocation scheme is proposed for the Heterogeneous Cloud Radio Access Networks with hybrid energy sources where Energy Harvesting technology is utilised. The optimisation problem is designed to maximise the utilisation of the renewable energy source. Through solving the proposed optimisation problem, the user association and power allocation policies are derived together to minimise the grid power consumption. Compared to the conventional UAs adopted in RANs, green power harvested by renewable energy source can be better utilised so that the grid power consumption can be greatly reduced with the proposed scheme. Secondly, a delay-aware energy e cient computation o oading scheme is proposed for the EH enabled F-RANs, where for access points (F-APs) are supported by renewable energy sources. The uneven distribution of the harvested energy brings in dynamics of the o oading design and a ects the delay experienced by users. The grid power minimisation problem is formulated. Based on the solutions derived, an energy e cient o oading decision algorithm is designed. Compared to SINR-based o oading scheme, the total grid power consumption of all F-APs can be reduced signi cantly with the proposed o oading decision algorithm while meeting the latency constraint. Thirdly, an energy-e cient computation o oading for mobile applications with shared data is investigated in a multi-user fog computing network. Taking the advantage of shared data property of latency-critical applications such as virtual reality (VR) and augmented reality (AR) into consideration, the energy minimisation problem is formulated. Then the optimal computation o oading and communications resources allocation policy is proposed which is able to minimise the overall energy consumption of mobile users and cloudlet server. Performance analysis indicates that the proposed policy outperforms other o oading schemes in terms of energy e ciency. The research works conducted in this thesis and the thorough performance analysis have revealed some insights on energy e cient resource allocation design in Cloud and Fog RANs
Optimizing energy efficiency for supporting near-cloud access region of UAV based NOMA networks in IoT systems
Non-orthogonal multiple access (NOMA) and unmanned aerial vehicle (UAV) are two promising technologies for wireless the fifth generation (5G) networks and beyond. On one hand, UAVs can be deployed as flying base stations to build line-of-sight (LoS) communication links to two ground users (GUs) and to improve the performance of conventional terrestrial cellular networks. On the other hand, NOMA enables the share of an orthogonal resource to multiple users simultaneously, thus improving the spectral efficiency and supporting massive connectivities. This paper presents two protocols namely cloud-base central station (CCS) based
power-splitting protocol (PSR) and time-switching protocol (TSR), for simultaneously wireless information and power transmission (SWIPT) at UAV employed in power domain NOMA based multi-tier heterogeneous cloud radio access network (H-CRAN) of internet of things (IoT) system. The system model with k types of UAVs and two users in which the CCS manages the entire H-CRAN and operates as a central unit in the cloud is proposed in our work. Closed-form expressions of throughput and energy efficiency (EE) for UAVs are derived. In particular, the EE is determined for the impacts of power allocation at CCS, various UAV types and channel environment. The simulation results show that the performance for CCS-based PSR outperforms that for CCS-based TSR for the impacts of power allocation at the CCS. On contrary, the TSR protocol has a higher EE than the PSR in cases of the impact of various UAV types and channel environment. The analytic results match Monte Carlo simulations
Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges
Heterogeneous Vehicular NETworks (HetVNETs) can meet various
quality-of-service (QoS) requirements for intelligent transport system (ITS)
services by integrating different access networks coherently. However, the
current network architecture for HetVNET cannot efficiently deal with the
increasing demands of rapidly changing network landscape. Thanks to the
centralization and flexibility of the cloud radio access network (Cloud-RAN),
soft-defined networking (SDN) can conveniently be applied to support the
dynamic nature of future HetVNET functions and various applications while
reducing the operating costs. In this paper, we first propose the multi-layer
Cloud RAN architecture for implementing the new network, where the multi-domain
resources can be exploited as needed for vehicle users. Then, the high-level
design of soft-defined HetVNET is presented in detail. Finally, we briefly
discuss key challenges and solutions for this new network, corroborating its
feasibility in the emerging fifth-generation (5G) era
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