This paper studies the joint design of cloud and edge processing for the downlink of a fog radio access network (F-RAN). In an F-RAN, as in cloud-RAN (C-RAN), a baseband processing unit (BBU) can ...perform joint baseband processing on behalf of the remote radio heads (RRHs) that are connected to the BBU by means of the fronthaul links. In addition to the minimal functionalities of conventional RRHs in C-RAN, the RRHs in an F-RAN may be equipped with local caches, in which frequently requested contents can be stored, as well as with baseband processing capabilities. They are hence referred to as enhanced RRH (eRRH). This paper focuses on the design of the delivery phase for an arbitrary pre-fetching strategy used to populate the caches of the eRRHs. Two fronthauling modes are considered, namely, a hard-transfer mode, whereby non-cached files are communicated over the fronthaul links to a subset of eRRHs, and a soft-transfer mode, whereby the fronthaul links are used to convey quantized baseband signals as in a C-RAN. Unlike the hard-transfer mode in which baseband processing is traditionally carried out only at the eRRHs, the soft-transfer mode enables both centralized precoding at the BBU and local precoding at the eRRHs based on the cached contents, by means of a novel superposition coding approach. To attain the advantages of both approaches, a hybrid design of soft- and hard-transfer modes is also proposed. The problem of maximizing the delivery rate is tackled under fronthaul capacity and per-eRRH power constraints. Numerical results are provided to compare the performance of hard- and soft-transfer fronthauling modes, as well as of the hybrid scheme, for different baseline pre-fetching strategies.
Taking full advantage of both heterogeneous networks and cloud access radio access networks, heterogeneous cloud radio access networks (H-CRANs) are presented to enhance both spectral and energy ...efficiencies, where remote radio heads (RRHs) are mainly used to provide high data rates for users with high quality of service (QoS) requirements, whereas the high-power node (HPN) is deployed to guarantee seamless coverage and serve users with low-QoS requirements. To mitigate the intertier interference and improve energy efficiency (EE) performances in H-CRANs, characterizing user association with RRH/HPN is considered in this paper, and the traditional soft fractional frequency reuse (S-FFR) is enhanced. Based on the RRH/HPN association constraint and the enhanced S-FFR, an energy-efficient optimization problem with the resource assignment and power allocation for the orthogonal-frequency-division-multiple-access-based H-CRANs is formulated as a nonconvex objective function. To deal with the nonconvexity, an equivalent convex feasibility problem is reformulated, and closed-form expressions for the energy-efficient resource allocation solution to jointly allocate the resource block and transmit power are derived by the Lagrange dual decomposition method. Simulation results confirm that the H-CRAN architecture and the corresponding resource allocation solution can enhance the EE significantly.
As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated ...by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues, and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, socialaware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test is introduced as well.
Network slicing for 5G provides Network-as-a-Service (NaaS) for different use cases, allowing network operators to build multiple virtual networks on a shared infrastructure. With network slicing, ...service providers can deploy their applications and services flexibly and quickly to accommodate diverse services' specific requirements. As an emerging technology with a number of advantages, network slicing has raised many issues for the industry and academia alike. Here, the authors discuss this technology's background and propose a framework. They also discuss remaining challenges and future research directions.
Things You Should Know About Fronthaul Pizzinat, Anna; Chanclou, Philippe; Saliou, Fabienne ...
Journal of lightwave technology,
2015-March1,-1, 2015-3-1, 20150301, Volume:
33, Issue:
5
Journal Article
Peer reviewed
This paper provides a review of the new fronthaul network segment that appears in centralized radio access network (C-RAN) architecture. C-RAN drivers are presented under an operational, economic, ...and radio point of view. The different fronthaul interfaces are briefly described as they have to be taken into account to build a fronthaul transport solution. Then, fronthaul requirements are detailed going from the technical ones to the business ones. Finally, different fronthaul solutions are presented. Perspectives for medium term evolution including fronthaul supervision are hinted as well as challenges for future mobile evolution toward 5G.
In order to cope with the explosive growth of mobile traffic, many traffic offloading schemes such as heterogenous networks have been developed to enhance network capacity of the Radio Access Network ...(RAN). Among them, networking of Low-Earth Orbit (LEO) satellites promises to significantly improve the RAN performance due to its economical prospect and advantages in high bandwidth and low latency. In this paper, by introducing the cache-enabled LEO satellite network as a part of RAN, we propose an integrated satellite/terrestrial cooperative transmission scheme to enable an energy-efficient RAN by offloading traffic from base stations through satellite's broadcast transmission. Considering energy-constraints of satellites, we then formulate a nonlinear fractional programming problem aiming at optimizing transmission energy efficiency of the system. In order to effectively solve this problem, we transform it into an equivalent one, and then adopt iteration and sub-problem decomposition to obtain the optimal solution for each optimization variable, i.e., block placement, power allocation, and cache sharing variable. Numerical results show that compared with traditional terrestrial scheme, our cooperative transmission scheme achieves significant performance improvement in terms of traffic offloading and energy efficiency, especially in an environment of high request consistency degree.
Deep learning-based univariate time series classification can improve the user experience of Open Radio Access Network (ORAN)-based Cellular Vehicle-to-Everything (CV2x). However, few institutes ...researching ORAD-based CV2x can satisfy the enormous demand of labeled data. This issue is known as few-shot learning. Thus, we deeply explore the issue of few-shot learning for ORAE-based CV2x. Meta-transfer learning is a good alternative to solving few-shot learning. Most of them, however, are still plagued by catastrophic forgetting. Numerous studies have demonstrated that deliberately applying gradient sparsity can significantly increase a meta-model's capacity for generalization. In this article, we propose a pre-training framework named Distilling for Sparse-Meta-transfer Learning (DSML). It is a combination and enhancement of meta-transfer learning, multi-teacher knowledge distillation, and sparse Model-Agnostic Meta-Learning (sparse-MAML). It utilizes multi-teacher knowledge distillation to address the catastrophic forgetting in the meta-learning phase. Simultaneously, it utilizes sigmoid function to fundamentally address the gradient anomaly problem of sparse-MAML. We conducted ablation experiments on Sparse-MAML and prove that it can actually increase the meta-model's generalization capacity. We also compare DSML with the state-of-the-art algorithm in the univariate time series classification field. The results demonstrate that DSML performs better. Finally, we present two case studies of applying DSML to ORAN-based CV2x.
The fifth generation (5G) of mobile communication system aims to deliver a ubiquitous mobile service with enhanced quality of service (QoS). It is also expected to enable new use-cases for various ...vertical industrial applications-such as automobiles, public transportation, medical care, energy, public safety, agriculture, entertainment, manufacturing, and so on. Rapid increases are predicted to occur in user density, traffic volume, and data rate. This calls for novel solutions to the requirements of both mobile users and vertical industries in the next decade. Among various available options, one that appears attractive is to redesign the network architecture-more specifically, to reconstruct the radio access network (RAN). In this paper, we present an inclusive and comprehensive survey on various RAN architectures toward 5G, namely cloud-RAN, heterogeneous cloud-RAN, virtualized cloud-RAN, and fog-RAN. We compare them from various perspectives, such as energy consumption, operations expenditure, resource allocation, spectrum efficiency, system architecture, and network performance. Moreover, we review the key enabling technologies for 5G systems, such as multi-access edge computing, network function virtualization, software-defined networking, and network slicing; and some crucial radio access technologies (RATs), such as millimeter wave, massive multi-input multi-output, device-to-device communication, and massive machine-type communication. Last but not least, we discuss the major research challenges in 5G RAN and 5G RATs and identify several possible directions of future research.
The relentless growth of wireless applications and data traffic continues to accentuate the long felt need for decentralized, self-managed, and cooperative network architectures. Enlightened by the ...power of blockchain technology, we propose a blockchain radio access network (B-RAN) architecture and develop decentralized, secure, and efficient mechanisms to manage network access and authentication among inherently trustless network entities. We further identify promising advanced functions made possible by adopting blockchain for open radio access networks. Our test results demonstrate the benefits of the B-RAN architecture. We also present a number of challenges and future research directions.
This work studies the joint design of precoding and backhaul compression strategies for the downlink of cloud radio access networks. In these systems, a central encoder is connected to multiple ...multi-antenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art approaches, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals of different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sum-rate with respect to both the precoding matrix and the joint correlation matrix of the quantization noises is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. Moreover, in order to enable the practical implementation of multivariate compression across BSs, a novel architecture is proposed based on successive steps of minimum mean-squared error (MMSE) estimation and per-BS compression. Robust design with respect to imperfect channel state information is also discussed. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on the separate design of precoding and compression or independent compression across the BSs.
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