Network-Coded Macrocell Offloading in Femtocaching-Assisted Cellular Networks
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2017-11-08
Print Publication Date2018-03
Permanent link to this recordhttp://hdl.handle.net/10754/626239
MetadataShow full item record
AbstractOpportunistic network coding (ONC) has shown high potential in enhancing the quality-of-experience (QoE) for the clients of cellular networks using their previously downloaded files. In this paper, we study the problem of offloading clients from the macrocell base station (MBS) with the help of femtocaches (FCs) and ONC. We formulate this MBS offloading problem as an optimization problem over an ONC graph, and prove that it is non-deterministic polynomial-time (NP)-hard. Thus, we propose an ONC-broadcast offloading scheme, which utilizes separate ONC graphs at the MBS and FCs in addition to uncoded broadcasting, to offload the clients from the MBS. We analyze the performance of the ONC-broadcast offloading scheme and show that it is asymptotically optimal using random graph theory. Since even this ONC-broadcast offloading scheme is still NP-hard to implement, we devise an efficient heuristic to simplify the implementation. We show that the proposed heuristic reduces the worst-case complexity of implementing the ONC-broadcast offloading scheme from an exponential to a quadratic function of the total number of vertices in the FC ONC graph. Simulation results show that, despite its low complexity, the proposed heuristic achieves similar MBS offloading performance to the ONC-broadcast offloading scheme.
CitationShnaiwer Y, Sorour S, Sadeghi P, Aboutorab N, Al-Naffouri TY (2017) Network-Coded Macrocell Offloading in Femtocaching-Assisted Cellular Networks. IEEE Transactions on Vehicular Technology: 1–1. Available: http://dx.doi.org/10.1109/TVT.2017.2771416.
SponsorsThis research was funded by a grant from the office of competitive research funding (OCRF) at the King Abdullah University of Science and Technology (KAUST). The work was also supported by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, through projects EE002355 and KAUST-002.
Showing items related by title, author, creator and subject.
Location-aware network operation for cloud radio access networkWang, Fanggang; Ruan, Liangzhong; Win, Moe Z. (2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2017-06-20) [Conference Paper]One of the major challenges in effectively operating a cloud radio access network (C-RAN) is the excessive overhead signaling and computation load that scale rapidly with the size of the network. In this paper, the exploitation of location information of the mobile devices is proposed to address this challenge. We consider an approach in which location-assisted channel state information (CSI) acquisition methods are introduced to complement conventional pilot-based CSI acquisition methods and avoid excessive overhead signaling. A low-complexity algorithm is designed to maximize the sum rate. An adaptive algorithm is also proposed to address the uncertainty issue in CSI acquisition. Both theoretical and numerical analyses show that location information provides a new dimension to improve throughput for next-generation massive cooperative networks.
Cross-Layer Cloud Offloading Using Fog Radio Access Networks and Network CodingShnaiwer, Yousef N.; Sorour, Sameh; Al-Naffouri, Tareq Y.; Al-Ghadhban, Samir (2018 IEEE International Conference on Communications Workshops (ICC Workshops), Institute of Electrical and Electronics Engineers (IEEE), 2018-07-05) [Conference Paper]In this work, we propose a cross-layer approach for cloud offloading using network coding enabled fog radio access networks. Exploiting prior popular file downloads by clients, network coding was previously employed to send more efficient coded files from both edge nodes and the cloud, thus achieving further upper-layer offloading gains. The proposed cross-layer approach in this paper extends the study towards achieving actual cloud physical-resource offloading, by considering the download rates of each client from the cloud in the coding-based offloading process. We first formulate the cross- layer cloud offloading problem as an optimization problem over an opportunistic network coding graph, and prove its NP-hardness. We then devise a heuristic algorithm to efficiently solve the problem by dividing it into two subproblems, which are solved sequentially by a greedy vertex search and a sorted greedy coloring algorithms. Simulation results show a superior performance of our proposed cross-layer cloud offloading scheme compared to the traditional upper-layer scheme. Furthermore, the suggested heuristic achieves a close cloud offloading performance to the optimal cross-layer solution.
Virtualized cognitive network architecture for 5G cellular networksElsawy, Hesham; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim (IEEE Communications Magazine, Institute of Electrical and Electronics Engineers (IEEE), 2015-07-17) [Article]Cellular networks have preserved an application agnostic and base station (BS) centric architecture1 for decades. Network functionalities (e.g. user association) are decided and performed regardless of the underlying application (e.g. automation, tactile Internet, online gaming, multimedia). Such an ossified architecture imposes several hurdles against achieving the ambitious metrics of next generation cellular systems. This article first highlights the features and drawbacks of such architectural ossification. Then the article proposes a virtualized and cognitive network architecture, wherein network functionalities are implemented via software instances in the cloud, and the underlying architecture can adapt to the application of interest as well as to changes in channels and traffic conditions. The adaptation is done in terms of the network topology by manipulating connectivities and steering traffic via different paths, so as to attain the applications' requirements and network design objectives. The article presents cognitive strategies to implement some of the classical network functionalities, along with their related implementation challenges. The article further presents a case study illustrating the performance improvement of the proposed architecture as compared to conventional cellular networks, both in terms of outage probability and handover rate.