On the Interactions Between Multiple Overlapping WLANs Using Channel Bonding

Bellalta, Boris; Checco, Alessandro; Zocca, Alessandro; Barceló, Jaume

doi:10.1109/tvt.2015.2400932

Cited by 70 publications

(73 citation statements)

References 32 publications

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“…There have been extensively works on modeling the CSMA protocols in the literature: one group of works concentrates on using an idealized CTMC model for CSMA/CA WiFi networks and approximating their throughput [16]- [22]; while [23], [24] focus on using CTMC to model the interaction between the APs of multiple networks to analyze their performance. The proposed CTMC in our paper is distinct from previous works in that: i) our model is more realistic in that it is built on the physical interference model of the channel, while previous works employ a simple contention-graph based flow model that assumes WiFi signal sensing is deterministic and does not take the randomness of interference into consideration; ii) the objectives of our work are distinct from previous works in that our work aims to use the CTMC model to infer channel statistics, such as the fraction of certain station's transmitting time, and also to detect jammers using the state transition statistics.…”

Section: A Mathematical Modelsmentioning

confidence: 99%

Efficiency and Detectability of Random Reactive Jamming in Carrier Sense Wireless Networks

Weber

2019

IEEE Trans. Commun.

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A natural basis for the detection of a wireless random reactive jammer (RRJ) is the perceived violation by the detector (typically located at the access point (AP)) of the carrier sensing protocol underpinning many wireless random access protocols (e.g., WiFi). Specifically, when the wireless medium is perceived by a station to be busy, a carrier sensing compliant station will avoid transmission while a RRJ station will often initiate transmission. However, hidden terminals (HTs), i.e., activity detected by the AP but not by the sensing station, complicate the use of carrier sensing as the basis for RRJ detection since they provide plausible deniability to a station suspected of being an RRJ. The RRJ has the dual objectives of avoiding detection and effectively disrupting communication, but there is an inherent performance tradeoff between these two objectives. In this paper we capture the behavior of both the RRJ and the compliant stations via a parsimonious Markov chain model, and pose the detection problem using the framework of Markov chain hypothesis testing. Our analysis yields the receiver operating characteristic (ROC) of the detector, and the optimized behavior of the RRJ. While there has been extensive work in the literature on jamming detection, our innovation lies in leveraging carrier sensing as a natural and effective basis for detection.N. An and S. Weber are with the Reactive jamming, Markov chain model, large deviations principle, hypothesis testing I. INTRODUCTIONJamming attacks are a widely recognized threat to wireless networks. As a type of denialof-service attack, wireless jamming leverages the broadcast nature of the wireless medium and emits jamming signals either to prevent other (compliant) users from accessing the network, or to corrupt ongoing transmissions. There are three major types of jammers [2]: i) constant jammer, which constantly sends jamming signals, ii) random jammer, which randomly alternates between jamming and idle states, and iii) reactive jammer, which emits jamming signals upon sensing any ongoing traffic over the wireless channel. Compared with the first two types, the reactive jammer (RJ) is more sophisticated in that it achieves high jamming efficiency by only disrupting ongoing transmissions, which in general also lowers the risk of detection [2]. A RJ faces an inherent tradeoff in the dual objectives of effectively degrading network throughput and in avoiding detection: as the "aggressiveness" of the jamming is increased, it increases the effectiveness of the disruption, but at the same time increases the ease with which behavior not compliant with carrier sensing is detected. This detection is often based upon changes in network performance statistics such as the packet delivery rate (PDR), received signal strength (RSS), packet delivery delay, etc. However, the presence of hidden terminals (HT), i.e., transmissions detectable by the access point (AP) but not the sensing station, complicates the jamming detection problem, as the AP cannot always disambiguate whet...

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Section: A Mathematical Modelsmentioning

confidence: 99%

Efficiency and Detectability of Random Reactive Jamming in Carrier Sense Wireless Networks

Weber

2019

IEEE Trans. Commun.

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“…In practice, energy level is expected to be set lower so that the area is covers would be smaller than that of carrier sense. [11] In this way, multiple nodes may be connected even in a small area. The drawback of this approach is obvious: SNR decreases when we reduce the transmission power and this may lead to higher BER and higher chance to have hidden node problems.…”

Section: Orthogonal Frequency-division Multiple Access [7]mentioning

confidence: 99%

A Survey of IEEE 802.11 Protocols: Comparison and Prospective

Chen¹

2017

Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 20

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Abstract. Since the base version of IEEE 802.11 released in 1997, wireless local area network (WLAN) has been developing rapidly, and it has gone through many versions and amendments. The 802.11 protocol is now very sophisticated through 20 years of modification. Therefore, it is very hard to understand all improvements, which confuses researchers sometimes, making it not easy to discover the trend of development. This paper tries to summarize the key technologies that pushed WLAN forward among all important amendments, and then lists the latest research focus for the next generation of 802.11 family. The paper would provide assist researchers to find their field of interest.

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“…To mitigate such a negative effect, dynamic channel bonding (DCB) policies are used to select the bandwidth in a more flexible way based on the instantaneous spectrum occupancy. A well-known example of DCB policy is alwaysmax (AM) 1 [2], [3], where transmitters select the widest channel found idle when the backoff counter terminates.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Channel Bonding in Spatially Distributed High-Density WLANs

Barrachina‐Muñoz

Wilhelmi

Bellalta

2020

IEEE Trans. on Mobile Comput.

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In this paper, we discuss the effects on throughput and fairness of dynamic channel bonding (DCB) in spatially distributed high-density wireless local area networks (WLANs). First, we present an analytical framework based on continuous-time Markov networks (CTMNs) for depicting the behavior of different DCB policies in spatially distributed scenarios, where nodes are not required to be within the carrier sense range of each other. Then, we assess the performance of DCB in high-density IEEE 802.11ac/ax WLANs by means of simulations. We show that there may be critical interrelations among nodes in the spatial domain -even if they are located outside the carrier sense range of each other -in a chain reaction manner. Results also reveal that, while always selecting the widest available channel normally maximizes the individual long-term throughput, it often generates unfair situations where other WLANs starve. Moreover, we show that there are scenarios where DCB with stochastic channel width selection improves the latter approach both in terms of individual throughput and fairness. It follows that there is not a unique optimal DCB policy for every case. Instead, smarter bandwidth adaptation is required in the challenging scenarios of next-generation WLANs.

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On the Interactions Between Multiple Overlapping WLANs Using Channel Bonding

Cited by 70 publications

References 32 publications

Efficiency and Detectability of Random Reactive Jamming in Carrier Sense Wireless Networks

Efficiency and Detectability of Random Reactive Jamming in Carrier Sense Wireless Networks

A Survey of IEEE 802.11 Protocols: Comparison and Prospective

Dynamic Channel Bonding in Spatially Distributed High-Density WLANs

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