Reproducible MIMO operation in ns-3 using trace-based wi-fi rate adaptation

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Lamela, V (Author) Other; Fontes, H (Author) Other; José Ruela (Author) FEUP; Manuel Ricardo (Author) FEUP; Campos, R (Author) FEUP

Conference proceedings International

Title: WNS3 2021: 2021 Workshop on ns-3, Virtual Event, USA

Pages: 49-56

13th Workshop on ns-3, WNS3 2021

23 June 2021 through 24 June 2021

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00V-1PV

DOI: 10.1145/3460797.3460804

Abstract (EN): Today, wireless networks are operating in increasingly complex environments, impacting the evaluation and validation of new networking solutions. Simulation, although fully controllable and easily reproducible, depends on simplified physical layer and channel models, which often produce optimistic results. Experimentation is also influenced by external random phenomena and limited testbed scale and availability, resulting in hardly repeatable and reproducible results. Previously, we have proposed the Trace-based Simulation (TS) approach to address the problem. TS uses traces of radio link quality and position of nodes to accurately reproduce past experiments in ns-3. Yet, in its current version, TS is not compatible with scenarios where Multiple-In-Multiple-Out (MIMO) is used. This is especially relevant since ns-3 assumes perfectly independent MIMO radio streams. In this paper, we introduce the Trace-based Wi-Fi Station Manager Model, which is capable of reproducing the Rate Adaptation of past Wi-Fi experiments, including the number of effective radio streams used. To validate the proposed model, the network throughput was measured in different experiments performed in the w-iLab.t testbed, considering Single-In-Single-Out (SISO) and MIMO operation using IEEE 802.11a/n/ac standards. The experimental results were then compared with the network throughput achieved using the improved TS and Pure Simulation (PS) approaches, validating the new proposed model and confirming its relevance to reproduce experiments previously executed in real environments. © 2021 ACM.

Language: English

Type (Professor's evaluation): Scientific