Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges

As a promising paradigm for fifth generation (5G) wireless communication systems, cloud radio access networks (C-RANs) have been shown to reduce both capital and operating expenditures, as well as to provide high spectral efficiency (SE) and energy efficiency (EE). The fronthaul in such networks, defined as the transmission link between a baseband unit (BBU) and a remote radio head (RRH), requires high capacity, but is often constrained. This article comprehensively surveys recent advances in fronthaul-constrained C-RANs, including system architectures and key techniques. In particular, key techniques for alleviating the impact of constrained fronthaul on SE/EE and quality of service for users, including compression and quantization, large-scale coordinated processing and clustering, and resource allocation optimization, are discussed. Open issues in terms of software-defined networking, network function virtualization, and partial centralization are also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin note: text overlap with arXiv:1407.3855 by other author

Energy-efficient resource allocation in limited fronthaul capacity cloud-radio access networks

In recent years, cloud radio access networks (C-RANs) have demonstrated their role as a formidable technology candidate to address the challenging issues from the advent of Fifth Generation (5G) mobile networks. In C-RANs, the modules which are capable of processing data and handling radio signals are physically separated in two main functional groups: the baseband unit (BBU) pool consisting of multiple BBUs on the cloud, and the radio access networks (RANs) consisting of several low-power remote radio heads (RRH) whose functionality are simplified with radio transmission/reception. Thanks to the centralized computation capability of cloud computing, C-RANs enable the coordination between RRHs to significantly improve the achievable spectral efficiency to satisfy the explosive traffic demand from users. More importantly, this enhanced performance can be attained at its power-saving mode, which results in the energy-efficient C-RAN perspective. Note that such improvement can be achieved under an ideal fronthaul condition of very high and stable capacity. However, in practice, dedicated fronthaul links must remarkably be divided to connect a large amount of RRHs to the cloud, leading to a scenario of non-ideal limited fronthaul capacity for each RRH. This imposes a certain upper-bound on each user’s spectral efficiency, which limits the promising achievement of C-RANs. To fully harness the energy-efficient C-RANs while respecting their stringent limited fronthaul capacity characteristics, a more appropriate and efficient network design is essential. The main scope of this thesis aims at optimizing the green performance of C-RANs in terms of energy-efficiency under the non-ideal fronthaul capacity condition, namely energy-efficient design in limited fronthaul capacity C-RANs. Our study, via jointly determining the transmit beamforming, RRH selection, and RRH–user association, targets the following three vital design issues: the optimal trade-off between maximizing achievable sum rate and minimizing total power consumption, the maximum energy-efficiency under adaptive rate-dependent power model, the optimal joint energy-efficient design of virtual computing along with the radio resource allocation in virtualized C-RANs. The significant contributions and novelties of this work can be elaborated in the followings. Firstly, the joint design of transmit beamforming, RRH selection, and RRH–user association to optimize the trade-off between user sum rate maximization and total power consumption minimization in the downlink transmissions of C-RANs is presented in Chapter 3. We develop one powerful with high-complexity and two novel efficient low-complexity algorithms to respectively solve for a global optimal and high-quality sub-optimal solutions. The findings in this chapter show that the proposed algorithms, besides overcoming the burden to solve difficult non-convex problems within a polynomial time, also outperform the techniques in the literature in terms of convergence and achieved network performance. Secondly, Chapter 4 proposes a novel model reflecting the dependence of consumed power on the user data rate and highlights its impact through various energy-efficiency metrics in CRANs. The dominant performance of the results form Chapter 4, compared to the conventional work without adaptive rate-dependent power model, corroborates the importance of the newly proposed model in appropriately conserving the system power to achieve the most energy efficient C-RAN performance. Finally, we propose a novel model on the cloud center which enables the virtualization and adaptive allocation of computing resources according to the data traffic demand to conserve more power in Chapter 5. A problem of jointly designing the virtual computing resource together with the beamforming, RRH selection, and RRH–user association which maximizes the virtualized C-RAN energy-efficiency is considered. To cope with the huge size of the formulated optimization problem, a novel efficient with much lower-complexity algorithm compared to previous work is developed to achieve the solution. The achieved results from different evaluations demonstrate the superiority of the proposed designs compared to the conventional work

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

A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions

The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet goes far beyond data, audio and video delivery over fixed and mobile networks, and even beyond allowing communication and collaboration among things. It is expected to enable haptic communication and allow skill set delivery over networks. Some examples of potential applications are tele-surgery, vehicle fleets, augmented reality and industrial process automation. Several papers already cover many of the Tactile Internet-related concepts and technologies, such as haptic codecs, applications, and supporting technologies. However, none of them offers a comprehensive survey of the Tactile Internet, including its architectures and algorithms. Furthermore, none of them provides a systematic and critical review of the existing solutions. To address these lacunae, we provide a comprehensive survey of the architectures and algorithms proposed to date for the Tactile Internet. In addition, we critically review them using a well-defined set of requirements and discuss some of the lessons learned as well as the most promising research directions

A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks

The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non- Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions

Centralized and partial decentralized design for the Fog Radio Access Network

Fog Radio Access Network (F-RAN) has been shown to be a promising network architecture for the 5G network. With F-RAN, certain amount of signal processing functionalities are pushed from the Base Station (BS) on the network edge to the BaseBand Units (BBU) pool located remotely in the cloud. Hence, partially centralized network operation and management can be achieved, which can greatly improve the energy and spectral efficiency of the network, in order to meet the requirements of 5G. In this work, the optimal design for both uplink and downlink of F-RAN are intensively investigated

Software Defined Applications in Cellular and Optical Networks

abstract: Small wireless cells have the potential to overcome bottlenecks in wireless access through the sharing of spectrum resources. A novel access backhaul network architecture based on a Smart Gateway (Sm-GW) between the small cell base stations, e.g., LTE eNBs, and the conventional backhaul gateways, e.g., LTE Servicing/Packet Gateways (S/P-GWs) has been introduced to address the bottleneck. The Sm-GW flexibly schedules uplink transmissions for the eNBs. Based on software defined networking (SDN) a management mechanism that allows multiple operator to flexibly inter-operate via multiple Sm-GWs with a multitude of small cells has been proposed. This dissertation also comprehensively survey the studies that examine the SDN paradigm in optical networks. Along with the PHY functional split improvements, the performance of Distributed Converged Cable Access Platform (DCCAP) in the cable architectures especially for the Remote-PHY and Remote-MACPHY nodes has been evaluated. In the PHY functional split, in addition to the re-use of infrastructure with a common FFT module for multiple technologies, a novel cross functional split interaction to cache the repetitive QAM symbols across time at the remote node to reduce the transmission rate requirement of the fronthaul link has been proposed.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201