Multi-radio Heterogeneous Networks Research Articles

Outage analysis for multi-radio heterogeneous networks in the presence of aerial jammers

Recently, aerial jammers (AJs) have been actively considered because of their swiftness and adaptability to maximize the impact of jammers. The previous studies mainly focused on the optimization of a single AJ (transmit power, location, etc.) consisting of intended users and eavesdroppers in single-cell environment. In this paper, macroscopic impact of multiple randomly deployed AJs to multiple radio access technologies (multi-RATs) is analyzed, which have been actively studied for future cellular systems. In particular, multi-radio heterogeneous networks consisting of WiFi-enabled small base stations (SBSs) and 5G-enabled macro/micro base stations (MBSs) are considered. An integrated framework is developed and evaluated for multi-RATs in the presence of both co-channel interference and AJ interference based on tools from stochastic geometry. The main challenge is to analyze the optimal control of portion of AJs to interfere two different RATs to maximize the outage probability. Both analytical and simulation results are presented to demonstrate the impacts of various network parameters to the overall outage probability.

Adaptive Resource Management Strategy in Practical Multi-Radio Heterogeneous Networks

The ongoing evolution of mobile wireless communications has resulted in the vision of a multi-radio heterogeneous network (HetNet) that comprises cells of different scales controlled by various radio access technologies (RATs). These emerging architectures call for more advanced methods of cross-RAT radio resource allocation, which are the primary focus of this article. In this paper, based on network flow optimization techniques, we adapt the concept of weighted $\alpha $ -fairness for efficient resource management in future HetNets. The corresponding scheme relies on a certain degree of centralized control of the HetNet architecture and allows to achieve the desired balance between the overall system throughput and the fairness of the resulting resource allocations based on a single parameter. Our analytical findings, validated with detailed system-level simulations, are expected to further advance the understanding of feasible resource control strategies in intelligent multi-radio networks, as well as help optimize the performance of next-generation HetNets.

Intelligent network selection for data offloading in 5G multi-radio heterogeneous networks

In next generation networks, multi-radio networks are emerging in order to deal with exponential data traffic increasing. Integrated Femto-WiFi (IFW) small cells have been introduced by 3GPP to offload data from cellular networks recently. These IFW cells are multi-mode capable (i.e., both licensed bands via cellular interface and unlicensed bands via WiFi interface). Therefore how to offload data effectively has become one of the most significant discussions in 5G Multi-Radio Heterogeneous Network. So far, most researches mainly focus on the generality of UEs, few attention has been paid to UEs' individual requirements. Considering UE's preference vary from individual to individual, in this paper, we present an UE preference-aware network selection scheme for mobile data offloading. It intelligently supports the distribution of heterogeneous classes of services, considers different types of UEs and delay-tolerant flows, and handles the mobility of UEs. The simulation results show the superiority of the proposed algorithm in user fairness, enhanced capacity and energy saving maximization.

Intelligent access network selection in converged multi-radio heterogeneous networks

Heterogeneous multi-radio networks are emerging network architectures that comprise hierarchical deployments of increasingly smaller cells. In these deployments, each user device may employ multiple radio access technologies to communicate with network infrastructure. With the growing numbers of such multi-radio consumer devices, mobile network operators seek to leverage spectrum across diverse radio technologies, thus boosting capacity and enhancing quality of service. In this article, we review major challenges in delivering uniform connectivity and service experience to converged multiradio heterogeneous deployments. We envision that multiple radios and associated device/infrastructure intelligence for their efficient use will become a fundamental characteristic of future 5G technologies, where the distributed unlicensed-band network (e.g., WiFi) may take advantage of the centralized control function residing in the cellular network (e.g., 3GPP LTE). Illustrating several available architectural choices for integrating WiFi and LTE networks, we specifically focus on interworking within the radio access network and detail feasible options for intelligent access network selection. Both network- and user-centric approaches are considered, wherein the control rests with the network or the user. In particular, our system-level simulation results indicate that load-aware usercentric schemes, which augment SNR measurements with additional information about network loading, could improve the performance of conventional WiFi-preferred solutions based on minimum SNR threshold. Comparison with more advanced network-controlled schemes has also been completed to confirm attractive practical benefits of distributed user-centric algorithms. Building on extensive system-wide simulation data, we also propose novel analytical space-time methodology for assisted network selection capturing user traffic dynamics together with spatial randomness of multi-radio heterogeneous networks.