Network-Coded Macrocell Offloading in Femtocaching-Assisted Cellular Networks
KAUST DepartmentElectrical Engineering Program
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.
Collaborative Multi-Layer Network Coding in Hybrid Cellular Cognitive Radio NetworksMoubayed, Abdallah J.; Sorour, Sameh; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim (Institute of Electrical and Electronics Engineers (IEEE), 2015-05)In this paper, as an extension to , we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated collaboration between the collocated primary and cognitive radio base-stations in order to minimize their own as well as each other's packet recovery overheads, thus by improving their throughput. The proposed scheme ensures that each network's performance is not degraded by its help to the other network. Moreover, it guarantees that the primary network's interference threshold is not violated in the same and adjacent cells. Yet, the scheme allows the reduction of the recovery overhead in the collocated primary and cognitive radio networks. The reduction in the cognitive radio network is further amplified due to the perfect detection of spectrum holes which allows the cognitive radio base station to transmit at higher power without fear of violating the interference threshold of the primary network. For the secondary network, simulation results show reductions of 20% and 34% in the packet recovery overhead, compared to the non-collaborative scheme, for low and high probabilities of primary packet arrivals, respectively. For the primary network, this reduction was found to be 12%. © 2015 IEEE.
A Jackson network model and threshold policy for joint optimization of energy and delay in multi-hop wireless networksXia, Li; Shihada, Basem (Elsevier BV, 2014-11-20)This paper studies the joint optimization problem of energy and delay in a multi-hop wireless network. The optimization variables are the transmission rates, which are adjustable according to the packet queueing length in the buffer. The optimization goal is to minimize the energy consumption of energy-critical nodes and the packet transmission delay throughout the network. In this paper, we aim at understanding the well-known decentralized algorithms which are threshold based from a different research angle. By using a simplified network model, we show that we can adopt the semi-open Jackson network model and study this optimization problem in closed form. This simplified network model further allows us to establish some significant optimality properties. We prove that the system performance is monotonic with respect to (w.r.t.) the transmission rate. We also prove that the threshold-type policy is optimal, i.e., when the number of packets in the buffer is larger than a threshold, transmit with the maximal rate (power); otherwise, no transmission. With these optimality properties, we develop a heuristic algorithm to iteratively find the optimal threshold. Finally, we conduct some simulation experiments to demonstrate the main idea of this paper.
Conflict free network coding for distributed storage networksAl-Habob, Ahmed A.; Sorour, Sameh; Aboutorab, Neda; Sadeghi, Parastoo (Institute of Electrical and Electronics Engineers (IEEE), 2015-06)© 2015 IEEE. In this paper, we design a conflict free instantly decodable network coding (IDNC) solution for file download from distributed storage servers. Considering previously downloaded files at the clients from these servers as side information, IDNC can speed up the current download process. However, transmission conflicts can occur since multiple servers can simultaneously send IDNC combinations of files to the same client, which can tune to only one of them at a time. To avoid such conflicts and design more efficient coded download patterns, we propose a dual conflict IDNC graph model, which extends the conventional IDNC graph model in order to guarantee conflict free server transmissions to each of the clients. We then formulate the download time minimization problem as a stochastic shortest path problem whose action space is defined by the independent sets of this new graph. Given the intractability of the solution, we design a channel-aware heuristic algorithm and show that it achieves a considerable reduction in the file download time, compared to applying the conventional IDNC approach separately at each of the servers.