Many wireless channels in different technologies are known to have partial overlap. However, due to the interference effects among such partially overlapped channels, their simultaneous use has typically been avoided. In this paper, we present a first attempt to model partial overlap between channels in a systematic manner. Through the model, we illustrate that the use of partially overlapped channels is not always harmful. In fact, a careful use of some partially overlapped channels can often lead to significant improvements in spectrum utilization and application performance. We demonstrate this through analysis as well as through detailed application-level and MAC-level measurements. Additionally, we illustrate the benefits of our developed model by using it to directly enhance the performance of two previously proposed channel assignment algorithms — one in the context of wireless LANs and the other in the context of multi-hop wireless mesh networks. Through detailed simulations, we show that use of partially overlapped channels in both these cases can improve end-to-end application throughput by factors between 1.6 and 2.7 in different scenarios, depending on wireless node density. We conclude by observing that the notion of partial overlap can be the right model of flexibility to design efficient channel access mechanisms in the emerging software radio platforms.

We develop a general model to estimate the throughput and goodput between arbitrary pairs of nodes in the presence of interference from other nodes in a wireless network. Our model is based on measurements from the underlying network itself and is thus more accurate than abstract models of RF propagation such as those based on distance. The seed measurements are easy to gather, requiring only O(N) measurements in an N-node networks. Compared to existing measurement-based models, our model advances the state of the art in three important ways. First, it goes beyond pairwise interference and models interference among an arbitrary number of senders. Second, it goes beyond broadcast transmissions and models the more common case of unicast transmissions. Third, it goes beyond homogeneous nodes and models the general case of heterogeneous nodes with different traffic demands and different radio characteristics. Using simulations and measurements from two different wireless testbeds, we show that the predictions of our model are accurate in a wide range of scenarios.

Abstract — The popularity of IEEE 802.11 WLANs has led to dense deployments in urban areas. High density leads to suboptimal performance unless the interfering networks learn how to optimally use and share the spectrum. This paper proposes two fully distributed algorithms that allow (i) multiple interfering 802.11 Access Points to select their operating frequency in order to minimize interference, and (ii) users to choose the Access Point they attach to, in order to get their fair share of the whole network bandwidth. The proposed algorithms rely on Gibbs sampler, and do not require explicit coordination among the wireless devices. They only require the participating wireless nodes to measure local quantities such as interference and transmission delay. The algorithms are shown to lead to optimal bandwidth sharing, where optimality is defined according to the minimal potential delay. We analytically prove the convergence of the proposed algorithms, and study their performance by simulation.

Wireless 802.11 hotspots have grown in an uncoordinated fashion with highly variable deployment densities. Such uncoordinated deployments, coupled with the difficulty of implementing coordination protocols, has often led to conflicting configurations (e.g., in choice of transmission power and channel of operation) among the corresponding Access Points (APs). Overall, such conflicts cause both unpredictable network performance and unfairness among clients of neighboring hotspots. In this paper, we focus on the fairness problem for uncoordinated deployments. We study this problem from the channel assignment perspective. Our solution is based on the notion of channel-hopping, and meets all the important design considerations for control methods in uncoordinated deployments — distributed in nature, minimal to zero coordination among APs belonging to different hotspots, simple to implement, and interoperable with existing standards. In particular,

WLANs are indispensable for providing Internet access to users at locations such as universities, corporate offices, conferences, airports, and coffee shops. Many of these environments often experience flash crowds, which we define to be a sudden surge in the number of users simultaneously attempting to access the WLAN. When flash crowds occur, WLANs are likely to suffer from destructive interference, excessive channel load, and unsustainable packet processing at access points (APs). These conditions lead to a plethora of problems, such as a deterioration in network throughput, heavy packet loss, intermittent connectivity, overwhelmed APs, and sometimes, a network collapse. To verify these claims, we present two case studies of operational WLANs that experienced the aforementioned problems. The two WLANs each consisted of over 100 APs and more than 1000 simultaneous users, deployed at recently held 62 nd and 64 th Internet

Dense deployments of WLANs suffer from increased interference and as a result, reduced capacity. There are three main functions used to improve the overall network capacity: a) intelligent frequency allocation across APs, b) load-balancing of user affiliations across APs, and c) adaptive power-control for each AP. Several algorithms

Abstract — Campus and enterprise wireless networks are increasingly characterized by ubiquitous coverage and rising traffic demands. Efficiently assigning channels to access points (APs) in these networks can significantly affect the performance and capacity of the WLANs. The state-of-the-art approaches assign channels statically, without considering prevailing traffic demands. In this paper, we show that the quality of a channel assignment can be improved significantly by incorporating observed traffic demands at APs and clients into the assignment process. We refer to this as traffic-aware channel assignment. We conduct extensive trace-driven and synthetic simulations and identify deployment scenarios where traffic-awareness is likely to be of great help, and scenarios where the benefit is minimal. We address key practical issues in using traffic-awareness, including measuring an interference graph, handling non-binary interference, collecting traffic demands, and predicting future demands based on historical information. We present an implementation of our assignment scheme for a 25-node WLAN testbed. Our testbed experiments show that traffic-aware assignment offers superior network performance under a wide range of real network configurations. On the whole, our approach is simple yet effective. It can be incorporated into existing WLANs with little modification to existing wireless nodes and infrastructure. I.

Dyson is a new software architecture for building customizable WLANs. While research in wireless networks has made great strides, these advancements have not seen the light of day in real WLAN deployments. One of the key reasons is that today’s WLANs are not architected to embrace change. For example, system administrators cannot fine-tune the association policy for their particular environment: an administrator may know certain nodes in certain locations interfere with each other and cause a severe degradation in throughput, and hence, such associations must be avoided in the particular deployment. Dyson defines a set of APIs that allow clients and APs to send pertinent information such as radio channel conditions to a central controller. The central controller processes this information, to form a global view of the network. This global view, combined with historical information about spatial and temporal usage patterns, allows the central controller enact a rich set of policies to control the network’s behavior. Dyson provides a Python-based scripting API that allows the central controller’s policies to be extended for site-specific customizations and new optimizations that leverage historical knowledge. We have built a prototype implementation of Dyson, which currently runs on a 28-node testbed distributed across one floor of a typical academic building. Using this testbed, we examine various aspects of the architecture in detail, and demonstrate the ease of implementing a wide range of policies. Using Dyson, we demonstrate optimizing associations, handling VoIP clients, reserving airtime for specific users, and optimizing handoffs for mobile clients. 1

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Abstract—Driven by their low cost and ease of deployment, as well as their operation in unlicensed spectrum bands, IEEE 802.11-based Wireless Local Area Networks (WLANs), also termed Wi-Fi, have been established as the de facto access technology for local area wireless connectivity. Especially in densely-populated urban areas, WLAN presence is ubiquitous. Residential WLAN owners, municipalities and venue owners, among others, set up wireless hotspots for private or public use. However, one can identify two important problems that have to be efficiently tackled. On the one hand, while Wi-Fi is present practically everywhere in modern metropolitan areas, access to roaming users is typically restricted. At the same time, the broadband Internet connections WLAN Access Points (APs) are attached to may have excess capacity, thus leaving resources underutilized. On the other hand, unplanned Wi-Fi deployment leads to significant interference problems among neighbor WLANs. In this work, we exploit our prior work on WLAN sharing communities to jointly tackle the above problems. The solution we propose is based on offering users QoS benefits as an incentive to perform spectrum sensing and supply interference reports to the WLAN APs they are connected to. We present the design of our mechanisms, discuss some of their properties and investigate their expected performance overhead, especially on delay-sensitive applications like VoIP. I.