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.

The rapidly increasing popularity of 802.11 WLANs along with the co-existence of multiple heterogeneous devices in the unlicensed frequency bands have created unprecedented levels of congestion, especially in densely populated urban areas. Under such complex setups, WLAN under-performance issues experienced by end-users are hard to interpret even by experts. In this paper, we develop an intelligent, easy to deploy mechanism that takes advantage of MAC-layer exported data and employs machine learning techniques to accurately diagnose the five most common WiFi pathologies (contention, low-SNR, non-802.11 Interference, etc.). The collected data are fed to four different classification algorithms, which we fine-tune, in order to optimize their hyper-parameters in regards to their precision and accuracy. The resulting solution provides two different mechanisms, with the first targeting low-overhead passive detection and the second offering more accurate performance relying on active probing. Detection performance is evaluated through extensive testbed experiments and exhibits that the K-Nearest Neighbors classifier achieves almost 100% accuracy and precision for the active probing and 95% accuracy and precision for the passive detection across the five considered pathologies.

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.

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.

Efficient association of a station with the appropriate access point has always been a challenging problem. The standard approach of considering only the Received Signal Strength, has recently been substituted by more efficient schemes that consider channel conditions, cell population etc. However, in spite of the large variety of approaches, several factors that determine to a large extent user throughput after association with an access point have been overlooked. In this work, we propose innovative metrics on which association should be based. First, we capture the contention from one-hop and interference from two-hop neighbors that is inherent in IEEE 802.11 WLAN environments. Second we include the PHY transmission rate and show preference to higher rates that reduce the above effects. Third, unlike most relevant approaches, we define an activity factor that reveals the anticipated activity due to backlogged traffic. We devise an association protocol suite, through which messages containing the information above are passed between the AP and the user to support association decisions for the uplink and downlink. We implement the proposed mechanism using the MAD-WiFi open source driver and moreover show through experiments in a wireless testbed that it significantly improves user performance in real conditions.