Implementation and Validation of an IEEE 802.11ah Module for ns-3

Proceedings of the 10th Workshop on Ns-3 - WNS3 '18

Šljivo

Santi

et al. 2018

Self Cite

IEEE 802.11ah, marketed as Wi-Fi HaLow, is a new Wi-Fi standard for sub-1Ghz communications, aiming to address the major challenges of the Internet of Things (IoT), namely connectivity among a large number of densely deployed power-constrained stations. The standard was only published in May 2017 and hardware supporting Wi-Fi HaLow is not available on the market yet. As such, research on 802.11ah has been mostly based on mathematical and simulation models. Mathematical models generally introduce several simplifications and assumptions, which do not faithfully reflect real network conditions. As a solution, we previously developed an IEEE 802.11ah module for ns-3, publicly released in 2016. This initial release consisted of physical layer models for sub-1GHz communications and an implementation of the fast association and Restricted Access Window (RAW) channel access method. In this paper, we present an extension to our IEEE 802.11ah simulator. It contains several new features: an online RAW configuration interface, an energy state model, adaptive Modulation and Coding Scheme (MCS), and Traffic Indication Map (TIM) segmentation. This paper presents the details of our implementation, along with experimental results to validate each new feature. The extended Wi-Fi HaLow module can now support different scenarios with both uplink and downlink heterogeneous traffic, together with real-time RAW optimization, sleep management for energy conservation and adaptive MCS.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extension of the IEEE 802.11ah ns-3 simulation module

Proceedings of the 10th Workshop on Ns-3 - WNS3 '18

Šljivo

Santi

et al. 2018

Self Cite

“…Raeesi et al evaluated RAW performance using OMNeT++, the results demonstrate that the RAW feature provides substantial performance improvements (e.g., throughput, energy consumption), particularly in scenarios where there are high number of collisions in a heavily loaded network [4]. Using an IEEE 802.11ah implementation in ns-3 [19], we further evaluated the optimal RAW configuration under a variety of network conditions, such as traffic load, number of stations, and traffic distribution [2]. These works highlight the impact of network conditions on the optimal RAW configuration, and demonstrate there is a need for realtime RAW optimization under dynamic network conditions.…”

Section: Related Work On Ieee 80211ah Rawmentioning

confidence: 99%

“…While in the full design space, there is no such limitation. Our previously developed IEEE 802.11ah ns-3 simulation module [19], [36] is integrated into the Matlab Surrogate Modeling (SUMO) toolbox to train the RAW model. The SUMO Toolbox is a flexible framework for accurate global surrogate modeling [15], and has already been applied successfully to a wide range of applications.…”

Section: • Stopping Criteriamentioning

confidence: 99%

Optimization-Oriented RAW Modeling of IEEE 802.11ah Heterogeneous Networks

IEEE Internet Things J.

López-Aguilera

García-Villegas

et al. 2019

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The new medium access method of IEEE 802.11ah, called Restricted Access Window (RAW), divides stations into different groups, and only allows stations in the same group to access the channel simultaneously, in order to reduce collisions and thus achieve better performance (e.g., throughput). However, the existing station grouping strategies only support homogeneous scenarios where all stations use the same modulation and coding scheme (MCS) and packet size. A surrogate model is an efficient mathematical model that represents the behavior of a complex system, trained with a limited set of labeled input-output data samples. In this paper, we present a surrogate model that can accurately predict RAW performance under a given Restricted Access Window (RAW) configuration in heterogeneous networks. Different from the homogeneous scenario, heterogeneous networks are defined by a large number of parameters, leading to an enormous design space, i.e., the order of 10 17 possible data points. This is too big to achieve feasible training convergence. In this paper, we present a novel training methodology that leads to a new design space with highly reduced size, i.e., the order of 10 5 data points. The surrogate model converges when less than 6000 labeled data points are used for training, which is only a tiny portion of the whole design space. The results show that, the relative error between model prediction and simulation results is less than 0.1 for 95% of the data points, in the areas of the design space studied. Its low complexity and high precision make the proposed model a valuable tool to develop real-time RAW optimization algorithms for heterogeneous IEEE 802.11ah networks.Index Terms-IEEE 802.11ah, RAW, surrogate model, heterogeneous networks.

“…Specifically, a small set of high-throughput wireless cameras are shown to successfully coexist with a large number of best-effort sensor monitoring stations. The demonstration is performed using an updated version of our ns-3 IEEE 802.11ah implementation [3], adding support for bidirectional traffic and QoS differentiation. Moreover, simulation results are presented in near real-time to the audience using a newly developed visualizer [2].…”

Section: Introductionmentioning

confidence: 99%

Supporting Heterogeneous IoT Traffic using the IEEE 802.11ah Restricted Access Window

Santi

Šljivo

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems

et al. 2017

Self Cite

IEEE 802.11ah is a new Wi-Fi standard operating on unlicensed sub-GHz frequencies. It aims to provide long-range connectivity to Internet of Things (IoT) devices. The IEEE 802.11ah restricted access window (RAW) mechanism promises to increase throughput and energy efficiency in dense deployments by dividing stations into different RAW groups and allowing only one group to access the channel at a time. In this demo, we demonstrate the ability of the RAW mechanism to support a large number of densely deployed IoT stations with heterogeneous traffic requirements. Differentiated Quality of Service (QoS) is offered to a small set of high-throughput wireless cameras that coexist with thousands of best-effort sensor monitoring stations. The results are visualized in near real-time using our own developed IEEE 802.11ah visualizer running on top of the ns-3 event-based network simulator.