Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs

Barrachina‐Muñoz, Sergio; Wilhelmi, Francesc; Selinis, Ioannis; Bellalta, Boris

doi:10.1109/wd.2019.8734225

Cited by 32 publications

(43 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To that aim, we use the SFCTMN analytical framework and validate the gathered results by means of the 11axHDWLANsSim wireless network simulator. 7 In summary, the main outcomes derived from the analytical analyses performed below in this section are: i) the feasible system states depend on the DCB policies followed by each of the WLANs, ii) maximizing the instantaneous throughput may not be the optimal strategy to maximize the long-term throughput, iii) in non-fully overlapping scenarios, cumulative interference and flow starvation may appear and cause poor performance to some WLANs, iv) there is not a unique optimal DCB policy. Note that, otherwise stated, in this paper the optimal DCB policy D * w for WLAN w is the one that maximizes its throughput, i.e., D * w = argmax D Γ w .…”

Section: Interactions In Frequency and Spacementioning

confidence: 99%

“…Likewise, if the system is in state B 3 3 and A terminates its backoff counter, the CTMN will transit to the feasible states 7. For the sake of saving space, the evaluation setups and corresponding results of the scenarios considered through the paper are detailed in https://github.com/sergiobarra/data repos/tree/master/ barrachina2018performance…”

Section: Is Feasible Because a And B Have Different Primary Channels mentioning

confidence: 99%

“…such complex phenomena by means of illustration through several toy scenarios. Finally, we evaluate the performance of the proposed policies in large high-density 11ax WLAN scenarios by means of simulations using 11axHDWLANsSim, a particular release (v1.0.1) of the Komondor [7] wireless networks simulator. 2 We find that, while AM is normally the best DCB policy for maximizing the individual longterm throughput of a WLAN, it may generate unfair situations where some other WLANs starve.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Channel Bonding in Spatially Distributed High-Density WLANs

Barrachina‐Muñoz

Wilhelmi

Bellalta

2020

IEEE Trans. on Mobile Comput.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Interactions In Frequency and Spacementioning

confidence: 99%

Section: Is Feasible Because a And B Have Different Primary Channels mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Channel Bonding in Spatially Distributed High-Density WLANs

Barrachina‐Muñoz

Wilhelmi

Bellalta

2020

IEEE Trans. on Mobile Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…As for the ML Pipeline, the closed-loop subsystem is orchestrated by the management subsystem. Network simulators (e.g., ns-3, Komondor [14]) are examples of closed-loop subsystems, which can serve two purposes: i) generate synthetic data to be used for training, and ii) conducting a simulation for devising the potential of a given ML method to be applied afterward on the real network.…”

Section: Closed-loop Subsystemmentioning

confidence: 99%

A Flexible Machine-Learning-Aware Architecture for Future WLANs

et al. 2020

Self Cite

View full text Add to dashboard Cite

Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on the ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs.

show abstract

“…Regarding the CA, all the WLANs are set with a random primary channel in the eight basic channels considered in the system (i.e., p w ∼ U [1,8], ∀w). The set of allocated basic channels is assigned uniformly at random as well.…”

Section: System Modelmentioning

confidence: 99%

Online Primary Channel Selection for Dynamic Channel Bonding in High-Density WLANs

Barrachina‐Muñoz

Wilhelmi

Bellalta

2020

IEEE Wireless Commun. Lett.

Self Cite

View full text Add to dashboard Cite

In order to dynamically adapt the transmission bandwidth in wireless local area networks (WLANs), dynamic channel bonding (DCB) was introduced in IEEE 802.11n. It has been extended since then, and it is expected to be a key element in IEEE 802.11ax and future amendments such as IEEE 802.11be. While DCB is proven to be a compelling mechanism by itself, its performance is deeply tied to the primary channel selection, especially in high-density (HD) deployments, where multiple nodes contend for the spectrum. Traditionally, this primary channel selection relied on picking the most free one without any further consideration. In this paper, in contrast, we propose dynamic-wise (DyWi), a light-weight, decentralized, online primary channel selection algorithm for DCB that maximizes the expected WLAN throughput by considering not only the occupancy of the target primary channel but also the activity of the secondary channels. Even when assuming important delay costs due to primary switching, simulation results show a significant improvement both in terms of average delay and throughput.

show abstract

Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs

Cited by 32 publications

References 15 publications

Dynamic Channel Bonding in Spatially Distributed High-Density WLANs

Dynamic Channel Bonding in Spatially Distributed High-Density WLANs

A Flexible Machine-Learning-Aware Architecture for Future WLANs

Online Primary Channel Selection for Dynamic Channel Bonding in High-Density WLANs

Contact Info

Product

Resources

About