An experimental study on the capture effect in 802.11a networks

Lee, Jeongkeun; Kim, Won‐Ho; Lee, Sung-Ju; Jo, Daehyung; Ryu, Ji Yoon; Kwon, Taekyoung; Choi, Yanghee

doi:10.1145/1287767.1287772

Cited by 234 publications

(168 citation statements)

References 12 publications

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“…Note that physical capture is still effective without invoking the MIM mode transition when a first packet has a higher SNR than a second packet. A required SNR difference to retrieve stronger packets depends on the data rate at which the stronger packets are transmitted, regardless of the data rates of weaker packets, as observed by Lee et al [14]. Table I (extracted from the empirical results in [14]) shows the different SNR values between two overlapping packets to retrieve the stronger packet is higher at higher data rate of the stronger packet.…”

Section: A Physical Capturementioning

confidence: 77%

“…The stronger packet can be retrieved even if it arrives later (called the "stronger-last collision"). This retrieval attempt in the stronger-last collision case is also called message in message (MIM) mode transition where a node transits its current receiving process to a new packet with a higher SNR [14]. To support the MIM mode transition, a wireless card has a simple mechanism to detect a higher SNR of a new packet while receiving its first packet.…”

Section: A Physical Capturementioning

confidence: 99%

“…We further enhance ns-2 so that physical capture can be achieved as long as the conditions for a successful physical capture are met. The empirical results of physical capture obtained from [14] are used as the conditions for a successful physical capture in ns-2. Our simulation results show that our multirate adaptation scheme consistently outperforms the existing schemes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cross Layer Multirate Adaptation Using Physical Capture

Park

Kasera

Patwari

2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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Abstract-In this paper, to improve the performance of multihop wireless networks, we explore a cross layer multirate adaptation scheme (we call it CROMA) that uses the phenomenon of physical capture at the physical layer for effectively distinguishing losses due to collisions from those due to channel-error. We first estimate the number of packets dropped due to collisions, at each node by counting the number of packets that are not successfully retrieved by physical capture. Next, using a simple algorithm, we assign this collision loss to neighboring sources of packets that might have generated the colliding packets. Using extensive ns-2 simulations, we show that our multirate adaptation scheme consistently outperforms the existing schemes.

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Section: A Physical Capturementioning

confidence: 77%

Section: A Physical Capturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross Layer Multirate Adaptation Using Physical Capture

Park

Kasera

Patwari

2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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“…The survival of the collision depends on the relative signal power and the arrival timing of the involved frames. This phenomenon is related to the Capture Effect [20].…”

Section: Overlapping Channels Interference Detectionmentioning

confidence: 99%

Various Detection Techniques and Platforms for Monitoring Interference Condition in a Wireless Testbed

Liu

Keranidis

Mehari

et al. 2013

Measurement Methodology and Tools

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Abstract. Recently the constant growth of the wireless communication technology has caused a huge demand for experimental facilities. Hence many research institutes setup public accessible experimental facilities, known as testbeds. Compared to the facilities developed by individual researchers, a testbed typically offers more resources, more flexibilities. However, due to the fact that equipments are located remotely and experiments involve more complex scenarios, the required complexity for analysis is also higher. A deep insight on the underlying wireless environment of the testbed becomes necessary for comprehensive analysis. In this paper, we present a framework and associated techniques for monitoring the wireless environment in a large scale wireless testbed. The framework utilizes most common resources in the testbed, such as WI-FI nodes, as well as some high-end software-defined radio platforms. Information from both physical layer and network layer are taken into account. We observe that feature detection is more sensitive than general energy detection for dedicated technologies, and distributed spectrum sensing can further improve the detection sensitivity. Such observations are applied to achieve better interference detection. The performance is mainly analyzed experimentally.

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“…Thus, a larger value of BIF S max is more likely to achieve a higher degree of fairness (a higher value of BIFS duration implies a higher channel access chance of contending BSSs). However, a little bit more of BIF S max may decrease the overall network utilization as there is a trade-off between network utilization and fairness [25]. For this reason, we set BIF S max = 10 · b slot .…”

Section: Adjustment Of Inter-bss Qos Parametersmentioning

confidence: 99%

QoS provisioning for large-scale multi-ap WLANs

Choi¹,

Shin²

2012

Ad Hoc Networks

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Large-scale deployment of IEEE 802.11 wireless LANs (WLANs) with a high density of access points (APs) has become commonplace due mainly to its potential for numerous benefits, such as ubiquitous service coverage, seamless handover, and improved link quality. However, the increased AP density can induce significant channel contention among neighboring cells, thus causing severe performance degradation and throughput imbalance between cells. There have been a plethora of research efforts to improve the WLAN performance, but most of them focused only on single WLAN environments without accounting for intercell contention. The de facto QoS provisioning mechanism for WLANs, i.e., the Enhanced Distributed Channel Access (EDCA), is no exception to this. The EDCA focuses only on inter-flow priority distinction and has not considered the effect of inter-cell contention which significantly restricts its efficiency. This paper presents an enhanced QoS provisioning framework that takes into account inter-cell level differentiation as well as inter-flow level priority, which may be viewed as extension of QoS provisioning from a single-WLAN domain to a multi-WLAN domain. We also propose an architecture for managing multi-AP systems in which a central controller regulates the wireless channel occupancy of APs by adaptively configuring the cell-level QoS parameters. Our extensive simulation results show that the proposed inter-AP cooperative QoS scheme overcomes the limit of legacy 802.11e and provides a high level of fairness in large-scale denselydeployed WLANs.

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An experimental study on the capture effect in 802.11a networks

Cited by 234 publications

References 12 publications

Cross Layer Multirate Adaptation Using Physical Capture

Cross Layer Multirate Adaptation Using Physical Capture

Various Detection Techniques and Platforms for Monitoring Interference Condition in a Wireless Testbed

QoS provisioning for large-scale multi-ap WLANs

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