Chonggang Wang

Heterogeneous cloud radio access networks: a new perspective for enhancing spectral and energy efficiencies

To mitigate the severe inter-tier interference and enhance the limited cooperative gains resulting from the constrained and non-ideal transmissions between adjacent base stations in HetNets, H-CRANs are proposed as cost-efficient potential solutions through incorporating cloud computing into HetNets. In this article, state-of-the-art research achievements and challenges of H-CRANs are surveyed. In particular, we discuss issues of system architectures, spectral and energy efficiency performance, and promising key techniques. A great emphasis is given toward promising key techniques in HCRANs to improve both spectral and energy efficiencies, including cloud-computing-based coordinated multipoint transmission and reception, large-scale cooperative multiple antenna, cloud-computing-based cooperative radio resource management, and cloud-computing based self-organizing networks in cloud converging scenarios. The major challenges and open issues in terms of theoretical performance with stochastic geometry, fronthaul-constrained resource allocation, and standard development that may block the promotion of H-CRANs are discussed as well.

System architecture and key technologies for 5G heterogeneous cloud radio access networks

Compared 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. To achieve these goals, a H-CRAN is presented in this article as the advanced wireless access network paradigm, where cloud computing is used to fulfill the centralized large-scale cooperative processing for suppressing co-channel interferences. The state-of-the-art research achievements in the areas of system architecture and key technologies for H-CRANs are surveyed. Particularly, Node C as a new communication entity is defined to converge the existing ancestral base stations and act as the base band unit pool to manage all accessed remote radio heads. Also, the software-defined H-CRAN system architecture is presented to be compatible with software-defined networks. The principles, performance gains, and open issues of key technologies, including adaptive large-scale cooperative spatial signal processing, cooperative radio resource management, network function virtualization, and self-organization, are summarized. The major challenges in terms of fronthaul constrained resource allocation optimization and energy harvesting that may affect the promotion of H-CRANs are discussed as well.

Fronthaul-constrained cloud radio access networks: insights and challenges

As a promising paradigm for fifth generation 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 the baseband unit and the remote radio head, requires a high capacity, but is often constrained. This article comprehensively surveys recent advances in fronthaul-constrained CRANs, including system architectures and key techniques. Particularly, major issues relating to the impact of the 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 together with corresponding potential solutions. Open issues in terms of software-defined networking, network function virtualization, and partial centralization are also identified.

Prediction or Not? An Energy-Efficient Framework for Clustering-Based Data Collection in Wireless Sensor Networks

For many applications in wireless sensor networks (WSNs), users may want to continuously extract data from the networks for analysis later. However, accurate data extraction is difficult-it is often too costly to obtain all sensor readings, as well as not necessary in the sense that the readings themselves only represent samples of the true state of the world. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data provide opportunities for reducing the energy consumption of continuous sensor data collection. Integrating clustering and prediction techniques makes it essential to design a new data collection scheme, so as to achieve network energy efficiency and stability. We propose an energy-efficient framework for clustering-based data collection in wireless sensor networks by integrating adaptively enabling/disabling prediction scheme. Our framework is clustering based. A cluster head represents all sensor nodes in the cluster and collects data values from them. To realize prediction techniques efficiently in WSNs, we present adaptive scheme to control prediction used in our framework, analyze the performance tradeoff between reducing communication cost and limiting prediction cost, and design algorithms to exploit the benefit of adaptive scheme to enable/disable prediction operations. Our framework is general enough to incorporate many advanced features and we show how sleep/awake scheduling can be applied, which takes our framework approach to designing a practical algorithm for data aggregation: it avoids the need for rampant node-to-node propagation of aggregates, but rather it uses faster and more efficient cluster-to-cluster propagation. To the best of our knowledge, this is the first work adaptively enabling/disabling prediction scheme for clustering-based continuous data collection in sensor networks. Our proposed models, analysis, and framework are validated via simulation and comparison with competing techniques.

Fog-computing-based radio access networks: issues and challenges

An F-RAN is presented in this article as a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantage 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.

Seamless Wireless Connectivity for Multimedia Services in High Speed Trains

The recent advent of high speed trains introduces new mobility patterns in wireless environments. The LTE-A (Long Term Evolution of 3GPP - Advanced) networks have largely tackled the Doppler effect problem in the physical layer and are able to keep wireless service with 100Mpbs throughput within a cell in speeds up to 350 km/h. Yet the much more frequent handovers across cells greatly increases the possibility of service interruptions, and the problem is prominent for multimedia communications that demand both high-throughput and continuous connections. In this paper, we present a novel LTE-based solution to support high throughput and continuous multimedia services for high speed train passengers. Our solution is based on a Cell Array that smartly organizes the cells along a railway, together with a femto cell service that aggregates traffic demands within individual train cabins. Given that the movement direction and speed of a high-speed train are generally known, our Cell Array effectively predicts the upcoming LTE cells in service, and enables a seamless handover that will not interrupt multimedia streams. To accommodate the extreme channel variations, we further propose a scheduling and resource allocation mechanism to maximize the service rate based on periodical signal quality changes. Our simulation under diverse network and railway/train configurations demonstrates that the proposed solution achieves much lower handover latency and higher data throughput, as compared to existing solutions. It also well resists to network and traffic dynamics, thus enabling uninterrupted quality multimedia services for passengers in high speed trains.

Device-to-Device Underlaid Cellular Networks under Rician Fading Channels

Using Device-to-device (D2D) communications in a cellular network is an economical and effective approach to increase the transmission data rate and extend the coverage. Nevertheless, the D2D underlaid cellular network is challenging due to the presence of inter-tier and intra-tier interferences. With necessarily lower antenna heights in D2D communication links, the fading channels are likely to contain strong line-of-sight components, which are different from the Rayleigh fading distribution in conventional two-tier heterogeneous networks. In this paper, we derive the success probability, spatial average rate, and area spectral efficiency performances for both cellular users and D2D users by taking into account the different channel propagations that they experience. Specifically, we employ stochastic geometry as an analysis framework to derive closed-form expressions for above performance metrics. Furthermore, to reduce cross-tier interferences and improve system performances, we propose a centralized opportunistic access control scheme as well as a mode selection mechanism. According to the analysis and simulations, we obtain interesting tradeoffs that depend on the effect of the channel propagation parameter, user node density, and the spectrum occupation ratio on the different performance metrics. This work highlights the importance of incorporating the suitable channel propagation model into the system design and analysis to obtain the realistic results and conclusions.

Recent Advances in Underlay Heterogeneous Networks: Interference Control, Resource Allocation, and Self-Organization

By deploying additional low power nodes (LPNs) within the coverage area of traditional high power nodes (HPNs) and bringing them closer to users, underlay heterogeneous networks (HetNets) can significantly boost the overall spectral efficiency (SE) and energy efficiency (EE) through a full spatial resource reuse. Considering that the severe intra-tier interference among dense LPNs and inter-tier interference between LPNs and HPNs are challenging the successful rollout and commercial operations of underlay HetNets, a great emphasis is given towards advanced techniques that take interference control, radio resource allocation, and self-organization into account to enhance both SE and EE in this paper. The interference control techniques presented in this paper are classified as the spatial interference coordination at the transmitter and the interference cancelation at the receiver. For the radio resource allocation, the multi-dimensional optimization, cross-layer optimization, and cooperative radio resource management are comprehensively summarized. The self-configuration, self-optimization, and self-healing techniques for the self-organized underlay HetNets are surveyed. Furthermore, this paper outlines the potential open issues for underlay HetNets to improve SE and EE when combining with energy harvesting and cloud computing.

Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

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.

Priority-Based Traffic Scheduling and Utility Optimization for Cognitive Radio Communication Infrastructure-Based Smart Grid

Smart grid can be visualized as an intelligent control system over sensors and communication platforms. Recently, wireless multimedia sensor networks (WMSNs) have shown its advantages for smart grid by providing rich surveillance information for grid failure detection and recovery, energy source monitoring, asset management, security, etc. On the other hand, cognitive radio (CR) networks have been identified as a key wireless technology to reduce the communication interferences and improve the bandwidth efficiency for smart grid communication. There is an essential need to use the CR communication platform to support large-size and time-sensitive multimedia delivery for future smart grid system. In this paper, we consider the heterogeneous characteristics of smart grid traffic including multimedia and propose a priority-based traffic scheduling approach for CR communication infrastructure based smart grid system according to the various traffic types of smart grid such as control commands, multimedia sensing data and meter readings. Specifically, we develop CR channel allocation and traffic scheduling schemes taking into consideration of channel switch and spectrum sensing errors, and solve a system utility optimization problem for smart grid communication system. Our solutions are demonstrated through both analyzes and simulations. This research opens a new vista of future smart grid communications.