The trend for dense deployment in future 5G mobile communication networks makes current wired backhaul infeasible owing to the high cost. Millimetre-wave (mm-wave) communication, a promising technique with the capability of providing a multi-gigabit transmission rate, offers a flexible and cost-effective candidate for 5G backhauling. By exploiting highly directional antennas, it becomes practical to cope with explosive traffic demands and to deal with interference problems. Several advancements in physical layer technology, such as hybrid beamforming and full duplexing, bring new challenges and opportunities for mm-wave backhaul. This article introduces a design framework for 5G mm-wave backhaul, including routing, spatial reuse scheduling and physical layer techniques. The associated optimization model, open problems and potential solutions are discussed to fully exploit the throughput gain of the backhaul network. Extensive simulations are conducted to verify the potential benefits of the proposed method for the 5G mm-wave backhaul design.

The diversity of personal interest and preference of mobile users results in a wide spectrum of mobile app usage, and it is important to predict such app preference in order to provide personalized services. However, currently available individual app usage data is very limited, which does not cover a large user base. In this paper, we demonstrate that it is possible to make personalized app usage estimation by learning user's app preference from the social media, i.e., public accessible tweets, which can also reflect user's interest and make up for the sparsity of app usage data. By proposing a novel generative model named IMCF+ to transfer user interest from rich tweet information to sparse app usage, we achieve personalized app recommendations via learning the interest's correlation between apps and tweets. Based on a real-world app usage and tweet dataset over a large population, we evaluate the performance of IMCF+ with a variety of scenarios and parameters. With only 10% training data, our IMCF+ approach achieves 82.5% hitrate in predicting the top ten apps. Moreover, IMCF+ outperforms the other six state-of-theart algorithms by 4.7% and 10% in high sparsity case and user cold-start scenario, indicating the effectiveness of our method. All these results demonstrate that our technique can reliably learn user's interest from tweets to help solve the personalized app recommendation problem. Our study is the first step forward for transferring user's interest learned from social media to app preference, which paves the way for providing higher-quality personalized app recommendation and services for mobile users.

Non-terrestrial networks (NTNs) using flying objects such as satellites play key roles in the next-generation wireless system (6G). The NTN links with long propagation delay and random packet losses pose a great challenge to the performance of Transmission Control Protocol (TCP), which many Internet applications rely on. Performance enhancing proxy (PEP) is an easy-to-deploy approach for improving TCP's performance. In this paper, we design and implement a novel PEP called PEPesc which has two distinctive features. First, it features retransmission-free loss recovery, using an adaptive packet-level forward erasure correction method called streaming coding (SC). Second, as packet losses are recovered by SC, the congestion control problem is simplified to rate control and local acknowledgement between entities based on bandwidth estimation. Based on a queueing theoretic analysis of the design, we carefully devise a protocol and implement PEPesc as an open-source application. Extensive evaluations show that PEPesc can achieve much higher and smoother goodput than the canonical TCP variants and than other existing open-source PEPs in applications including iperf and HTTP-based adaptive streaming, and achieves similar performance in web browsing. Finally, we also present a deployment case over a real-world geostationary satellite link.

In virtual network (VN) embedding problem, researchers usually assume VN requests to be permanent. However, VN requests evolve frequently in practice. In this paper, we focus on re-embedding process after VN requests evolve, and propose an incremental re-embedding scheme which tries to reduce the number of virtual nodes that need to be re-embedded. Simulation results show that our scheme achieves better performance in three performance metrics, compared with three baseline schemes.

To provide communication services in delay-tolerant networks (DTNs) where there may exist no end-to-end paths between mobile node pairs, a variety of relaying and routing algorithms have been proposed under the assumption that the mobile nodes are homogeneously distributed in the network with the same contact rate and delivery cost. However, experimental data have revealed the heterogeneous contact rates between node pairs, and various applications of DTNs have shown that the mobile nodes often belong to different types in terms of energy consumption, communication ability, and other properties. Following the philosophy of exploiting the heterogeneous features of nodes to enhance the routing performance, we design an optimal relaying scheme for DTNs, which takes into account the nodes' heterogeneous contact rates and delivery costs when selecting relays to minimize the delivery cost while satisfying the required message delivery probability. We use the trace-driven simulations to demonstrate the effectiveness of our optimal relaying scheme in various distributions of nodes' delivery costs and mobility environments. Simulation results show that our proposed optimal relaying scheme requires the least delivery cost and achieves the largest maximum delivery probability, compared with the schemes that neglect or do not fully take into account nodes' heterogeneity.