A fog computing based radio access network (F-RAN) is presented in this article as a promising paradigm for the fifth generation (5G) wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantages of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on fronthaul and avoid large-scale radio signal processing in the centralized baseband unit pool. This article comprehensively presents the system architecture and key techniques of F-RANs.In particular, key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed. Open issues in terms of edge caching, software-defined networking, and network function virtualization, are also identified. SUBMIT TO IEEE NETWORK, VOL. X, NO. Y, MON. 2015 2 I. INTRODUCTIONCompared to the fourth generation (4G) wireless communication system, the fifth generation (5G) wireless communication system should achieve system capacity growth by a factor of at least 1000, and the energy efficiency (EE) growth by a factor of at least 10 [1]. To achieve these goals, the cloud radio access network (C-RAN) has been proposed as a combination of emerging technologies from both the wireless and the information technology industries by incorporating cloud computing into radio access networks (RANs) [2]. C-RANs have come with their own challenges in the constrained fronthaul and centralized baseband unit (BBU) pool. A prerequisite requirement for the centralized processing in the BBU pool is an inter-connection fronthaul with high bandwidth and low latency. Unfortunately, the practical fronthaul is often capacity and time-delay constrained, which has a significant decrease on spectral efficiency (SE) and EE gains.To overcome the disadvantages of C-RANs with the fronthaul constraints, heterogeneous cloud radio access networks (H-CRANs) have been proposed in [3]. The user and control planes are decoupled in such networks, where high power nodes (HPNs) are mainly used to provide seamless coverage and execute the functions of control plane, while remote radio heads (RRHs) are deployed to provide high speed data rate for the packet traffic transmission in the user plane. HPNs are connected to the BBU pool via the backhaul links for interference coordination. Unfortunately, H-CRANs are still challenging in practice. First, since the location based social applications become more and more popular, the traffic data over the fronthaul between RRHs and the centralized BBU pool surges a lot of redundant information, which worsens the fronthaul constraints. Besides, H-CRANs do not take full advantage of processing and storage capabilities in edge devices, such as RRHs and "smart" user equipments (UEs), which is a promising approach to successfully alleviate the burden of the fronthaul and BBU pool. Moreover, operators need to deploy a huge number of fixed RRHs and HPNs in H-CRANs...

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.

Abstract-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 are introduced as well.

ith the rapid development of mobile Internet and the Internet of things (IoTs), the demands for high-speed data applications, such as high-quality wireless video streaming, social networking, and machine-to-machine communication, have been growing exponentially recently. It is envisioned that total daily mobile traffic in the representative Western European countries will grow 67 times from 186 terabyte (TB) to 12540 TB from 2010 to 2020. Total worldwide mobile traffic of 351 exabyte (EB) in 2025 will represent a 174 percent increase compared with 2020 [1]. Currently, the cellular networks including the first generation (1G), second generation (2G), third generation (3G), and fourth generation (4G) are far from satisfying the significant traffic increments and the high energy efficiency (EE) because much of the power consumed by a base station (BS) is used to overcome path loss, which in turn causes interferences to other users. The fifth generation (5G) system deployed initially in 2020 is expected to provide approximately 1000 times higher wireless area capacity and save up to 90 percent of energy consumption per service compared with the current 4G system. More than 1000 Gb/s/km 2 area spectral capacity in dense urban environments, 10 times higher battery life of connected devices, and five times reduced end-to-end (E2E) latency are anticipated in 5G systems. The new 5G air interface and spectrum should be combined together with the long term evolution (LTE) and WiFi to provide universal high-rate coverage and seamless user experience [2].To achieve these goals in 5G systems, advanced radio access technologies and all-Internet Protocol (IP) open Internet network architectures should be evolved smoothly from 4G systems [3]. Accurately, the new breakthroughs in the baseband and radio frequency (RF) are required to enable computationally intensive operations and adapted to new air interfaces in 5G systems. A significant and advanced baseband computation is required to meet the complex requirements of new solutions such as large-scale cooperative signal processing in the physical layer. Meanwhile, the new breakthroughs in the integrated access node and heterogeneous convergence are required to enable the ultra dense radio nodes to work efficiently. The plug-and-play function becomes essential to commercial deployments, in which the available spectral resources should be allocated and the corresponding parameters should be self-organized. Furthermore, the softwaredefined air interface technologies should be seamlessly integrated into the 5G radio access network (RAN) architectures. The cloud computing based radio access infrastructures would provide on-demand resource processing, delay-aware storage, and high network capacity wherever needed. W 6IEEE Network • AbstractCompared with fourth generation cellular systems, fifth generation wireless communication systems are anticipated to provide spectral and energy efficiency growth by a factor of at least 10, and the area throughput growth by a factor of at least 25. T...

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Organotin compounds such as tributyltin (TBT) have been used worldwide in agriculture and industry as biocides, heat stabilizers, and chemical catalysts. However, few studies addressing the effects of TBT on growth and metabolism have been reported. This study was conducted to investigate the effects of TBT at low doses (0.5, 5, and 50 μg/kg) on body weight gain in male mice exposed as from puberty and to determine the alterations in related hormones. The results showed that exposure to TBT for 45 days resulted in an increase in body weight gain and hepatic steatosis accompanied with hyperinsulinemia and hyperleptinemia. Reduction of hepatic adiponectin levels in a dose-dependent manner was related to the lipid increase in the liver. These results suggest that chronic and repeat exposure to low doses of TBT can result in obesity and hepatic steatosis and induce the occurrence of insulin and leptin resistance.

Abstract-In this paper we study the Resource Allocation (RA) in Orthogonal Frequency Division Multiplexing (OFDM)-based Cognitive Radio (CR) networks, under the consideration of many practical limitations such as imperfect spectrum sensing, limited transmission power, different traffic demands of secondary users, etc. The general RA optimization framework leads to a complex mixed integer programming task which is computationally intractable. We propose to address this hard task in two steps. For the first step, we perform subchannel allocation to satisfy heterogeneous users' rate requirements roughly and remove the intractable integer constraints of the optimization problem. For the second step, we perform power distribution among the OFDM subchannels. By exploiting the problem structure to speedup the Newton step, we propose a barrier-based method which is able to achieve the optimal power distribution with an almost linear complexity, significantly better than the complexity of standard techniques. Moreover, we propose a method which is able to approximate the optimal solution with a constant complexity. Numerical results validate that our proposal exploits the overall capacity of CR systems well subjected to different traffic demands of users and interference constraints with given power budget.