In this paper, we present a UE-driven light-weight mechanism for fast handover decision and efficient WLAN selection in the context of 5G networks. As the network deployments are expected to be denser and the mobile user will be offered a multitude of alternative short coverage range options to have her mobile traffic served, her roaming decision will be performance critical. While the current 3GPP standardization considers the use of network performance statistics of nearby WLANs for the UE-driven roaming selection to address the uncertainty of the shared wireless medium, their collection and processing inevitably affects the mobile user performance and inserts an accuracy-performance tradeoff. We introduce a spectrum assessment framework, that is based on commercial hardware and open-source software, to evaluate the conditions on the nearby WLANs and let the UE to infer their performance with minimum overhead relying on Duty Cycle evaluation and the RSSI metrics. Our ready-to-be deployed solution leverages the use of off-the-shelf equipment and commercial devices and enables fast decision procedures for the WLAN selection with low collection and processing overhead. We evaluate our mechanism by conducting testbed experiments. The results reveal performance gains in terms of UE's achieved throughput when enabling the proposed framework to infer the spectral WLAN conditions and decide for the AP to roam.

Performance experienced by end-users supporting the popular 802.11 protocol is significantly degraded in densely populated urban areas, mainly due to the extensive spectrum sharing and the resulting 802.11 impairments such as ”hidden-terminals”, overlapping channel interference, etc. Moreover, as the unlicensed spectrum is also home for other wireless technologies and a large range of RF devices, the experienced channel conditions further deteriorate due to cross-technology interference. While the various resulting phenomena can be efficiently mitigated by isolating affected links from interference sources over spectrum, 802.11 networks currently lack unified mechanisms for characterizing the impact of different interference sources across the available channel configurations. In this work, we take advantage of spectral measurements available at the PHY-layer of commercial 802.11 equipment, in order to develop a highly accurate spectral analysis mechanism that is able to quantify the impact of interference on WLAN performance. The developed distributed mechanism concurrently operates on all network nodes and characterizes the band of interest with minimal overhead. Through the implementation of our approach on commercial 802.11n chipsets and its detailed experimental evaluation, we showcase its applicability in characterizing the impact of spectrum congestion and interference in a unified way, towards driving efficient spectrum adaptation decisions.

As the growth of mobile data traffic places significant strain on cellular networks, plans for exploiting under-utilized network resources become increasingly attractive. In this paper, we propose, design, and evaluate a data offloading architecture, where mobile users are offloaded to mesh networks, which are built and managed by residential users. Such networks are often developed in the context of community networks or, recently, as commercial services. Mobile network operators can lease capacity from these networks and offload traffic to reduce their servicing costs. We introduce an analytical framework that determines the offloading policy, i.e., which mobile users should be offloaded, based on the energy cost induced to the cellular base stations. Accordingly, we design a minimum-cost servicing policy for the mesh networks. Clearly, such architectures are realizable only if the mesh nodes agree with each other to jointly serve the offloaded traffic. To achieve this, we employ the Shapley value rule for dispensing the leasing payment among the mesh nodes. We evaluate this paper by simulating the operation of the LTE-A network, and conducting test bed experiments for the mesh network. The results reveal significant savings for eNBs power consumption and reimbursements for mesh users.

The recent mobile data explosion has increased the interest for mobile user-provided networks (MUPNs), where users share their Internet access by exploiting the diversity in their needs and resource availability. Although promising, MUPNs raise unique challenges. Namely, the success of such services relies on user participation which in turn can be achieved on the basis of a fair and efficient resource (i.e., Internet access and battery energy) exchange policy. The latter should be devised and imposed in a very fast time scale, based on near real-time feedback from mobile users regarding their needs, resources, and network conditions that are rapidly changing. To address these challenges we design and implement a novel cloud-controlled MUPN system, that employs software defined networking support on mobile terminals, to dynamically apply data forwarding policies with adaptive flow-control. We devise these policies by solving a coalitional game that is played among the users. We prove that the game has a non-empty core and hence the solution, which determines the servicing policy, incentivizes the users to participate. Finally, we evaluate the performance of the service in a prototype, where we investigate its performance limits, quantify the implementation overheads, and justify our architecture design choices.

As the unprecedented growth of mobile data traffic places significant strain on cellular networks, alternative plans for exploiting already existing and under-utilized wireless infrastructure, become quite attractive. In this paper, we study cellular-to-mesh (C2M) data offloading for LTE-A cellular mobile users to WiFi mesh networks, which are built and managed collaboratively by users. Such networks are developed in the context of community networks or, recently, as commercial services among residential users. Mobile network operators can lease these mesh networks to offload their traffic and reduce their servicing cost. In this context, we introduce an analytical framework that determines which mobile users should be offloaded, based on the energy cost incurred to the cellular base stations (eNB) for serving their demands. Accordingly, we design a routing policy that the mesh network can employ so as to serve the offloaded traffic with the minimum possible cost. Moreover, the reimbursement offered by the operator should be dispensed to the different mesh users, according to their contribution and added-value significance. We address this issue by employing the Shapley value profit sharing rule, which ensures the participation of the mesh nodes in this joint task. We evaluate our work by simulating the operation of the LTE-A network, and conducting testbed experimentation for the mesh network. The results reveal significant savings for eNBs power consumption and compensation profits for mesh users.

In this work, we present the AGILE Spectrum Adaptation system that is able to dynamically tune the channel central frequency and bandwidth of wireless links in an adaptive to the interference and traffic conditions way. The developed system is able to detect under-utilised spectrum fragments and optimally adjust the occupied spectrum. Through the online execution of 3 specifically designed experimental scenarios, we demonstrate the ability to implement distributed spectrum adaptation in commercial WLAN deployments, along with the obtained performance benefits.

Cognitive radio systems have gathered a lot of research interest during the last decade. Accuracy of spectrum sensing and efficiency of free spectrum utilization are considered as the primary objectives in this emerging technology, which promises a boost in wireless network performance, through exploitation of underutilized licensed frequency bands. As the focus of researchers is usually on these two major challenges, other aspects have been in part underestimated. In this work, we consider two factors that are rather important for evaluation of cognitive platforms, namely sensing delay and energy efficiency. The first is related to the latency induced by the spectrum sensing process and its impact on sensing efficiency, which is tightly connected to both the QoS performance of secondary users and the protection of primary users. On the other hand, energy consumption is considered as a crucial issue in all types of wireless communications, due to restricted battery autonomy of mobile devices, as well as for moving towards "greener" solutions in telecommunications. Therefore, it is important to extend existing testbed experimentation tools and develop new ones, in order to equip cognitive testbeds with such advanced monitoring capabilities. In this work, we present a monitoring procedure that has been directly integrated in the experimentation tools of the CREW testbed federation and demonstrate how it aids in the online evaluation of four different cognitive platforms in terms of the aforementioned metrics.