Frame aggregation

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

Dimming method and dimming device

Abstract: Disclosed are a dimming method and a dimming device. The dimming method includes dividing an MSDU (Mac service data units) on an MAC (media access control) layer into two or more than two sub MSDU of the same length; packaging the divided sub MSDU into individual MPDU (Mac protocol data units) and generating compensation frames for the MPDU respectively; aggregating the MPDU and the compensation frames which are of the same address as a PSDU (presentation service data) part of a PPDU (physical protocol data unit) and transmitting physical frames. The dimming method is independent of a modulation method and channel encoding scheme utilized in data transmission and accordingly realizes flexible and precise dimming of a visible light communication. Besides, since the MAC frame aggregation technology is utilized to realize intra-frame dimming, cost is saved.

Frame Aggregation and Multi-modal Fusion Framework for Video-Based Person Recognition.

Abstract: Video-based person recognition is challenging due to persons being blocked and blurred, and the variation of shooting angle. Previous research always focused on person recognition on still images, ignoring similarity and continuity between video frames. To tackle the challenges above, we propose a novel Frame Aggregation and Multi-Modal Fusion (FAMF) framework for video-based person recognition, which aggregates face features and incorporates them with multi-modal information to identify persons in videos. For frame aggregation, we propose a novel trainable layer based on NetVLAD (named AttentionVLAD), which takes arbitrary number of features as input and computes a fixed-length aggregated feature based on the feature quality. We show that introducing an attention mechanism into NetVLAD effectively decreases the impact of low-quality frames. For the multi-model information of videos, we propose a Multi-Layer Multi-Modal Attention (MLMA) module to learn the correlation of multi-modality by adaptively updating correlation Gram matrix. Experimental results on iQIYI-VID-2019 dataset show that our framework outperforms other state-of-the-art methods.

Theoretical Analysis of VoIP Transmission Through Different Wireless Access Technologies

Abstract: One of the challenges in today‘s wireless networks is to provide appropriate throughput for data and multimedia application. The physical data rate enhancements can be achieved through new physical capabilities, however to achieve high efficiency and to improve the throughput at the medium access control (MAC) layer, new and innovative MAC mechanisms are required. The disgraceful overhead occurs at the MAC layer prevents the WLANs from achieving desirable performance, this problem becomes more severe in the very high-speed WLAN. We will consider the potential benefits of frame aggregation in order to enhance the throughput. In this paper, the latest MAC layer mechanisms of IEEE 802.11, IEEE 802.11e and 802.11n standards are explained in details. Besides a theoretical analysis is used to evaluate and analyze the theoretical capacity of VoIP over different emerging wireless access technologies, ranging from IEEE 802.11 to IEEE 802.11n. Results confirm our expectation.

A Passive Client Side Control Packet-based WiFi Traffic Characterization Mechanism.

Abstract: WiFi has emerged as a pivotal technology for delivering Quality of Experience (QoE) to mobile devices. Unfortunately, exploding numbers of competing devices, potential encroachment by cellular technology, and dramatic increases in content richness deliver a more variable QoE than desired. Moreover, such variance tends to occur both across time and space making it a difficult problem to debug. Existing active approaches tend to be expensive or impractical while existing passive approaches tend to suffer from accuracy issues. In our paper, we propose a novel passive client-side approach that provides an efficient and accurate characterization by taking advantage of the properties of Frame Aggregation (FA) and Block Acknowledgements (BA). We show in the paper that one can accurately derive important metrics such as airtime and throughput with only a minimal amount of observed BAs. We show through extensive experiments the validity of our approach and conduct validation studies in the dense environment of a campus tailgate.

Leveraging frame aggregation to improve access point selection

Abstract: With the incredible rise in WiFi devices, proper assessment for performance is essential for Quality of Experience (QoE). In the past, many access point (AP) assessment metrics have been exploited to achieve optimal AP selection. However, these conventional metrics (e.g., throughput) are insufficient to capture the full dynamics of the AP load condition. In our paper, we posit that the recent introduction of frame aggregation by 802.11e can offer a compact and efficient representation of expected throughput for improving AP selection. We show that by conveying the characteristics of subframes during frame aggregation, we can uniquely embody the utilization, interference, and backlog traffic pressure for an access point. We validate the effectiveness of the proposed metrics with the commercial off the shelf (COTS) experiments. In addition, we explore an application case of using the metrics by adopting simple machine learning methods.