Joint space-frequency block codes and signal alignment for heterogeneous networks

In this paper, we propose a new diversity-oriented space-frequency block codes (SFBC) and signal alignment (SA) enabled physical network coding (PNC) method for the uplink of heterogeneous networks. The proposed joint Dual-SFBC with SA-PNC design substantially reduces interference and enables connecting a larger number of users when compared with methods adopting interference alignment (IA) or PNC. The main motivation behind the dual SFBC and SA-PNC design is that it allows the efficient coexistence of macro and small cells without any inter-system channel information requirements. Numerical results also verify that the proposed method outperforms the existing SA-PNC static method without any additional information exchange requirement between the two systems while achieving the main benefits of IA and SA-PNC coordinated methods recently proposed.publishe

DISCO:Interference-Aware Distributed Cooperation with Incentive Mechanism for 5G Heterogeneous Ultra-Dense Networks

Interference and traffic imbalance hinder improved system performance in heterogeneous ultra-dense networks. Network cooperation has become a promising paradigm with sophisticated techniques that can significantly enhance performance. In this article, a coalition game-theoretic framework is introduced to characterize cooperative behaviors, thus exploring these cooperative benefits and diversity gains. First, we introduce the basis of the coalition games. Then we survey its latest applications, in particular, interference mitigation and traffic offloading. Different from most current applications, we concentrate on cooperative incentive mechanism design since node cooperation always means resource consumption and other costs. Moreover, for the incentive mechanism, cooperative spectrum leasing is introduced. To mitigate interference and balance traffic, we propose two schemes under the presented framework: IASL and TOSL. Simulation results show the improved performance of the cooperative gains using the proposed IASL and TOSL schemes

CorteXlab: A Cognitive Radio Testbed for Reproducible Experiments

International audienceThe efficiency and potential gain of cognitive radio and more generally opportunistic cooperative communications have been already demonstrated from a theoretical point of view and supported by various simulation results. Beyond these promising results, several questions remain open from a practical point of view. Addressing these issues is not straightforward because deploying complex heterogeneous systems for cooperative scenarios is tedious, time consuming and hardly reproducible. We propose to make a step in this direction by offering a new ex-perimental facility, called CorteXlab, that allows complex multi-node cognitive radio scenarios deployment from anywhere in the world. Our objective is neither to design new software defined radio (SDR) nodes nor to propose a new software framework, but rather to provide a comprehensive access to a large set of high performance SDR nodes. The CorteXlab facility offers a 167 m 2 electromagnetically (EM) shielded room and integrates a set of 24 universal software radio peripherals (USRPs) from National Instruments, 18 PicoSDR nodes from Nutaq and 42 IoT-Lab wireless sensor nodes from Hikob. CorteXlab is built upon the foundations of the SensLAB testbed and also exploits the free and open-source toolkit GNU Radio. Automation in scenario deployment, experiment start, stop and results collection is performed by an experiment controller, called Minus. CorteXlab is in its final stages of development and is already capable of running specific test scenarios. In this contribution, we show that CorteXlab is able to easily cope with the usual issues faced by other testbeds providing a reproducible experiment environment for CR experimentation

CorteXlab: A Facility for Testing Cognitive Radio Networks in a Reproducible Environment

International audience—While many theoretical and simulation works have highlighted the potential gains of cognitive radio, several technical issues still need to be evaluated from an experimental point of view. Deploying complex heterogeneous system scenarios is tedious, time consuming and hardly reproducible. To address this problem, we have developed a new experimental facility, called CorteXlab, that allows complex multi-node cognitive radio scenarios to be easily deployed and tested by anyone in the world. Our objective is not to design new software defined radio (SDR) nodes, but rather to provide a comprehensive access to a large set of high performance SDR nodes. The CorteXlab facility offers a 167 m 2 electromagnetically (EM) shielded room and integrates a set of 24 universal software radio peripherals (USRPs) from National Instruments, 18 PicoSDR nodes from Nutaq and 42 IoT-Lab wireless sensor nodes from Hikob. CorteXlab is built upon the foundations of the SensLAB testbed and is based the free and open-source toolkit GNU Radio. Automation in scenario deployment, experiment start, stop and results collection is performed by an experiment controller, called Minus. CorteXlab is in its final stages of development and is already capable of running test scenarios. In this contribution, we show that CorteXlab is able to easily cope with the usual issues faced by other testbeds providing a reproducible experiment environment for CR experimentation

Stable Matching based Resource Allocation for Service Provider\u27s Revenue Maximization in 5G Networks

5G technology is foreseen to have a heterogeneous architecture with the various computational capability, and radio-enabled service providers (SPs) and service requesters (SRs), working altogether in a cellular model. However, the coexistence of heterogeneous network model spawns several research challenges such as diverse SRs with uneven service deadlines, interference management, and revenue maximization of non-uniform computational capacities enabled SPs. Thus, we propose a coexistence of heterogeneous SPs and SRs enabled cellular 5G network and formulate the SPs\u27 revenue maximization via resource allocation, considering different kinds of interference, data rate, and latency altogether as an optimization problem and further propose a distributed many-to-many stable matching-based solution. Moreover, we offer an adaptive stable matching based distributed algorithm to solve the formulated problem in a dynamic network model. Through extensive theoretical and simulation analysis, we have shown the effect of different parameters on the resource allocation objectives and achieves 94 percent of optimum network performance

A comprehensive survey on radio resource management in 5G HetNets: current solutions, future trends and open issues

The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e., cross-tier interference, co-tier interference, and user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions for advanced mechanisms are presented

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research