Customer experience is becoming of utmost importance for operators in home network management. Hence, a notion of the customers' QoE is vital, but has mostly been neglected in favor of QoS. In this article, we aim to close a significant gap between QoS and QoE in home networks by proposing a framework for inferring QoE from remotely collected network QoS metrics. We focus on video services (e.g., YouTube application) as the main contributor and generator of indoor network traffic. A case study is performed where an experimentally obtained dataset comprising network and application QoS parameters is obtained under varying conditions (i.e., poor coverage, network overload, contention, and interference). Predictive modeling is then used to build a predictor for multiple QoE classes given the network QoS metrics remotely accessible from access points based on industry adopted standards (i.e., TR-181). This enables operators to infer specific QoE metrics using remotely collected passive network measurement with no knowledge of application-specific parameters. We show that the proposed framework achieves accuracy in the range of 85 to 95 percent depending on the QoE class, hence demonstrating the effectiveness and potential of our approach.

Aerial base stations are a promising technology to increase the capabilities of existing communication networks. However, existing analytical frameworks do not sufficiently characterize the impact of ground interferers on aerial base stations. In order to address this issue, we model the effect of interference coming from coexisting ground networks on the aerial link, which could be the uplink of an aerial cell served by a drone base station. By considering a Poisson field of ground interferers, we characterize aggregate interference experienced by the drone. This result includes the effect of drone antenna pattern, the height-dependent shadowing, and various types of environment. We show that benefits a drone obtains from a better line-of-sight (LoS) at high altitudes is counteracted by a high vulnerability to the interference coming from ground. However, by deriving link coverage probability and transmission rate we show that a drone base station is still a promising technology if the overall system is properly dimensioned according to given density and transmission power of interferers. Particularly, our results illustrate how benefits of such network is maximized by defining the optimal drone altitude and signal-to-interference (SIR) requirement.

Wireless customers expect to have a guaranteed quality of experience at all times, at any location, and through different devices. Wi-Fi has become an access network of preference for service/ network providers and customers as well for public and private access. This sets a challenging requirement for next-generation Wi-Fi technology to provide seamless and uniform network quality of service). Consequently, the necessity for self-optimization of network and radio frequency segments becomes critical. This article surveys challenges and use cases of Wi-Fi self-optimizing networks (Wi-SONs) that have not been presented to date. We address technology and design challenges that shape Wi-SON as a very complex problem.

This paper presents an energy‐efficient relaying scheme for G.hn standard. We propose a multi‐domain bidirectional communication network with network coding at the physical layer in order to increase network coverage. The logical link control stack was also modified and supplemented with additional functionality. This reduces the power consumption in the network and enhances the performance while reducing collisions for inter‐domain network access. We consider domain selection to minimize the total energy consumption of the network and present optimal power allocation for the given QoS of each end node. Energy efficiency is evaluated in terms of transmit energy per bit for relay networks with bidirectional symmetric and asymmetric traffic flows. Simulation results show that the proposed multi‐domain bidirectional communication provides improved performance and higher energy savings than the single‐domain unidirectional network, especially in powerline communication channel, which is the worst medium of the three G.hn media. Finally, it was demonstrated that improved energy efficiency can be achieved with appropriate domain selection. Copyright © 2015 John Wiley & Sons, Ltd.

Broadband data applications can be delivered to homes over the available home network infrastructure (i.e., mediums). Further enhancement of the network performance is possible by exploiting spatial, time, or frequency diversity. Recently, MIMO systems have been proposed for power line communication (PLC) networks. Because of the extremely diverse physical characteristics of the mediums, an implementation of MIMO systems in G.hn across different domains represents a non‐trivial task. In this paper, we present and theoretically evaluate the performance of multiple‐domain diversity mechanism in G.hn using a cross‐layer classification algorithm. In the proposed network architecture, the signal is transmitted over different mediums selected by the classification algorithm in order to meet the quality‐of‐service demands. At the receiver, joint signal combining and equalization are done to take advantage of the multiple‐domain cooperative diversity and increase the signal‐to‐noise ratio. The merit of the proposed multiple‐domain PLC network has been confirmed by analytical performance analysis and computer simulation. Copyright © 2015 John Wiley & Sons, Ltd.