No abstract

We validate the IEEE 802.11 MAC layer model in ns3 by means of measurements on the EXTREME testbed. We consider different scenarios: communications within a single pair of nodes, multiuser communications using either VoIP or saturated traffic, and communications in the presence of hidden nodes. For each scenario we describe in detail our testbed and simulation setup, and compare the results provided by the ns3 simulator with the performance measured on the testbed.

In this paper we present a cognitive AP selection scheme based on a supervised learning approach. In our proposal the mobile station collects measurements regarding the past link conditions and throughput performance, and leverages on this data in order to learn how to predict the performance of the available APs in order to select the best one. The prediction capabilities in our scheme are achieved by employing a Multi-layer Feed-forward Neural Network (MFNN) to learn the correlation between the observed environmental conditions and the obtained performance. Our experimental performance evaluation carried out in a testbed using the IEEE 802.11 technology shows that our solution effectively outperforms legacy AP selection strategies in a variety of scenarios. I. INTRODUCTIONMobile users are often located in areas where many Access Points (APs) are available. The traditional scenario in which this fact arises is that of IEEE 802.11 WLAN, due to the popularity of this technology and the high density of deployed APs in home, enterprise and public areas. A more recent scenario with similar characteristics is that of 3G femtocells deployed with the open subscriber group paradigm, where the mobile user needs to select the femtocell to attach to among several alternatives with partially overlapping coverage areas. Depending on the propagation environment and the traffic load, the performance that the mobile user can get from each AP may vary significantly; as a consequence, it is interesting for the mobile user to identify and select the AP that will provide the best performance.Regardless of the technology, the fundamental issue of the AP selection problem is that the performance achievable from a particular AP depends on so many environmental factors and with such complicate relationships that is not feasible to model it analytically without gross simplifications. For example, in the case of the 802.11 technology, AP schemes based solely on theoretical considerations [1]-[3] work fine only in specific situations, but fail to work properly over the big variety of conditions that are encountered in realistic scenarios, as we will show in this paper.To solve this problem, we propose a Cognitive Networking approach [4] based on learning from the past experience. In our approach, the mobile station is equipped with a cognitive engine that learns from its past experience how the environmental conditions influence the performance of each available AP; the cognitive engine then uses this knowledge to select the AP that is expected to provide the best performance. The learning is said to be supervised since it is based on known training data, which in our case consists of the measurement data gathered by the mobile station in the past.

No abstract

Call Admission Control (CAC) is recognized as one of the key strategies to achieve satisfactory QoS support for VoIP over IEEE 802.11 WLANs. However, most of the prior CAC solutions for VoIP over WLAN are centralized, and the few distributed CAC schemes proposed so far do not account for issues such as the loss of channel time due to medium contention and the coexistence of VoIP traffic with background traffic such as TCP data flows. In this paper, we describe a distributed CAC scheme which aims at addressing these issues by leveraging on channel monitoring techniques, thus being readily implementable in today's consumer devices. The proposed scheme is thoroughly evaluated by means of testbed experiments as well as network simulations in different scenarios, and it is shown to yield a better CAC performance with respect to prior solutions.