Frame aggregation

About: Frame aggregation is a research topic. Over the lifetime, 487 publications have been published within this topic receiving 14295 citations.

Performance analysis of 802.11ac with frame aggregation using NS3

Abstract: 802.11ac is an interesting standard of IEEE bringing multiple advantages than its predecessor 802.11n. 802.11ac is faster and more scalable version of 802.11n offering the capabilities of wireless Gigabit Ethernet. 802.11ac will enable access points (AP) to support more STAs with a better experience for clients and more channel bonding increasing from a maximum of 40 MHz with 802.11n up to 80 or 160 MHz with 802.11ac standard. In this paper, we analyze and evaluate the 802.11ac performance using NS3 simulator (v3.26) relying on several features like channel bonding, modulation and coding schemes, guard interval and frame aggregation. Then, we present the effect of the variation of distance between STAs and AP on the network performance in term of throughput. Finally, we capture the most relevant simulations outcomes and we indicate some research challenges for the future work.

Using a backlogged queue approach for adaptive MAC frame aggregation

Abstract: Frame aggregation is a wireless link optimization mechanism that aims to reduce transmission overheads by sending multiple frames as the payload of a single MAC frame. Static assignment of frame aggregation parameters can result in delay penalties due to variations in traffic type or load levels. Another possible side effect is an increase in packet error rate in noisy environments due to large aggregated frame size. Adaptive aggregation methods have previously been proposed to deal with the above problems independently, but there is still a need for a unified adaptation algorithm that addresses both aspects in an integrated manner. In this study, a backlogged queue (BQ) aggregation approach is proposed that considers both these aspects, and also ensures inter-operability with other WLAN devices that are not capable of frame aggregation. Performance evaluation of the proposed algorithm on the ORBIT testbed shows throughput improvements of up to 56% in the presence of channel noise and 25% in scenarios with high contention over using a simple txop. An experimental case study shows improvement in FTP file transfer times of up to 11% while preserving performance for real time traffic.

Control frame aggregation frame

Abstract: Systems and methods are disclosed that may communicate a control aggregate frame from a first wireless device to a second wireless device. A first wireless device may form a control aggregate frame by aggregating a plurality of first control frames into the control aggregate frame. The control aggregate frame may include a single media access control (MAC) header, a plurality of first aggregation fields, each storing a control frame subtype for a corresponding one of the plurality of first control frames, and a plurality of first payload fields, each storing a corresponding one of the plurality of first control frames. After generating the control aggregate frame, the first wireless device may transmit it to the second wireless device.

Optimal frame aggregation level for IEEE 802.11 PCF protocol

Abstract: In this paper, we analyze the effect of the frame aggregation level on the PCF (Point Coordination Function) MAC performance in IEEE 802.11 wireless LANs and analytically derive the optimal frame aggregation level for maximizing the PCF MAC performance. For various values of unit data frame size and transmission error probability, we propose the optimal frame aggregation levels. By computer simulations, we show that the derived optimal frame aggregation level significantly enhances the PCF MAC performance in IEEE 802.11 wireless LANs.

A Deep Probabilistic Control Machinery for Auto-Configuration of WiFi Link Parameters

Abstract: IEEE 802.11ac high throughput extension for wireless local area network comes with a large number of link layer configuration parameters, such as 4 different channel bonding levels, 10 different modulation and coding schemes, frame aggregation setup etc. However, the optimal combination of link configuration parameters, which maximizes the link layer performance, depends on the perceived channel quality based on the signal strength, channel noise and external interference. Considering the highly dynamic, nonlinear and time-varying nature of wireless channel quality, a dynamic adaptation of link configuration parameters gives a stable and optimized link layer performance. Nevertheless, the existing literature fails to design a robust mechanism for handling all the parameters simultaneously. In this article, we develop a control theoretic approach governed by a deep probabilistic machinery to design a robust and scalable dynamic link parameter adaptation mechanism. We apply deep neural network based Gaussian process regression to predict the link layer throughput and model predictive control based approach to find out the link configuration parameter that optimizes the overall link layer performance. The proposed mechanism is implemented and tested over a testbed setup, and we observe that it can significantly boost up the link layer performance compared to various baseline mechanisms.