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, we propose a specific algorithm called MAXchop, which works efficiently when using only non-overlapping wireless channels, but is particularly effective in exploiting partially-overlapped channels that have been proposed in recent literature. We also evaluate how our channel assignment approach complements previously proposed carrier sensing techniques in providing further performance improvements. Through extensive simulations on real hotspot topologies and evaluation of a full implementation of this technique, we demonstrate the efficacy of these techniques for not only fairness, but also the aggregate throughput, metrics.We believe that this is the first work that brings into focus the fairness properties of channel hopping techniques and we hope that the insights from this research will be applied to other domains where a fair division of a system's resources is an important consideration.

Abstract-It is well known that a packet loss in 802.11 can happen either due to collision or an insufficiently strong signal. However, discerning the exact cause of a packet loss, once it occurs, is known to be quite difficult. In this paper we take a fresh look at this problem of wireless packet loss diagnosis for 802.11-based communication and propose a promising technique called COLLIE. COLLIE performs loss diagnosis by using newly designed metrics that examine error patterns within a physical-layer symbol in order to expose statistical differences between collision and weak signal based losses. We implement COLLIE through custom driver-level modifications in Linux and evaluate its performance experimentally. Our results demonstrate that it has an accuracy ranging between 60-95% while allowing a false positive rate of upto 2%. We also demonstrate the use of COLLIE in subsequent link adaptations in both static and mobile wireless usage scenarios through measurements on regular laptops and the Netgear SPH101 Voice-over-WiFi phone. In these experiments, COLLIE led to throughput improvements of 20-60% and reduced retransmission related costs by 40% depending upon the channel conditions.

Serving as the core component in many packet forwarding, differentiating and filtering schemes, packet classification continues to grow its importance in today's IP networks. Currently, most vendors use Ternary CAMs (TCAMs) for packet classification. TCAMs usually use brute-force parallel hardware to simultaneously check for all rules. One of the fundamental problems of TCAMs is that TCAMs suffer from range specifications because rules with range specifications need to be translated into multiple TCAM entries. Hence, the cost of packet classification will increase substantially as the number of TCAM entries grows. As a result, network operators hesitate to configure packet classifiers using range specifications. In this paper, we optimize packet classifier configurations by identifying semantically equivalent rule sets that lead to reduced number of TCAM entries when represented in hardware. In particular, we develop a number of effective techniques, which include: trimming rules, expanding rules, merging rules, and adding rules. Compared with previously proposed techniques which typically require modifications to the packet processor hardware, our scheme does not require any hardware modification, which is highly preferred by ISPs. Moreover, our scheme is complementary to previous techniques in that those techniques can be applied on the rule sets optimized by our scheme. We evaluate the effectiveness and potential of the proposed techniques using extensive experiments based on both real packet classifiers managed by a large tier-1 ISP and synthetic data generated randomly. We observe significant reduction on the number of TCAM entries that are needed to represent the optimized packet classifier configurations.

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Serving as the core component in many packet forwarding, differentiating and filtering schemes, packet classification continues to grow its importance in today's IP networks. Currently, most vendors use Ternary CAMs (TCAMs) for packet classification. TCAMs usually use brute-force parallel hardware to simultaneously check for all rules. One of the fundamental problems of TCAMs is that TCAMs suffer from range specifications because rules with range specifications need to be translated into multiple TCAM entries. Hence, the cost of packet classification will increase substantially as the number of TCAM entries grows. As a result, network operators hesitate to configure packet classifiers using range specifications. In this paper, we optimize packet classifier configurations by identifying semantically equivalent rule sets that lead to reduced number of TCAM entries when represented in hardware. In particular, we develop a number of effective techniques, which include: trimming rules, expanding rules, merging rules, and adding rules. Compared with previously proposed techniques which typically require modifications to the packet processor hardware, our scheme does not require any hardware modification, which is highly preferred by ISPs. Moreover, our scheme is complementary to previous techniques in that those techniques can be applied on the rule sets optimized by our scheme. We evaluate the effectiveness and potential of the proposed techniques using extensive experiments based on both real packet classifiers managed by a large tier-1 ISP and synthetic data generated randomly. We observe significant reduction on the number of TCAM entries that are needed to represent the optimized packet classifier configurations.