Survey and Analysis of VoIP Frame Aggregation Methods over A-MSDU IEEE 802.11n Wireless Networks

The IEEE 802.11n standard has provided prominent features that greatly contribute to ubiquitous wireless networks.Over the last ten years,voice over IP(VoIP)has become widespread around the globe owing to its low-cost or even free call rate.The combination of these technologies(VoIP and wireless)has become desirable and inevitable for organizations.However,VoIP faces a bandwidth utilization issue when working with 802.11 wireless networks.The bandwidth utilization is inefficient on the grounds that(i)80 bytes of 802.11/RTP/UDP/IP header is appended to 10–730 bytes of VoIP payload and(ii)765μs waiting intervals follow each 802.11 VoIP frame.Without considering the quality requirements of a VoIP call,be including frame aggregation in the IEEE 802.11n standard has been suggested as a solution for the bandwidth utilization issue.Consequently,several aggregation methods have been proposed to handle the quality requirements of VoIP calls when carried over an IEEE 802.11n wireless network.In this survey,we analyze the existing aggregation methods of VoIP over the A-MSDU IEEE 802.11n wireless standard.The survey provides researchers with a detailed analysis of the bandwidth utilization issue concerning the A-MSDU 802.11n standard,discussion of the main approaches of frame aggregation methods and existing aggregation methods,elaboration of the impact of frame aggregation methods on network performance and VoIP call quality,and suggestion of new areas to be investigated in conjunction with frame aggregation.The survey contributes by offering guidelines to design an appropriate,reliable,and robust aggregation method of VoIP over 802.11n standard.

Segment differential aggregation representation and supervised compensation learning of ConvNets for human action recognition

With more multi-modal data available for visual classification tasks,human action recognition has become an increasingly attractive topic.However,one of the main challenges is to effectively extract complementary features from different modalities for action recognition.In this work,a novel multimodal supervised learning framework based on convolution neural networks(Conv Nets)is proposed to facilitate extracting the compensation features from different modalities for human action recognition.Built on information aggregation mechanism and deep Conv Nets,our recognition framework represents spatial-temporal information from the base modalities by a designed frame difference aggregation spatial-temporal module(FDA-STM),that the networks bridges information from skeleton data through a multimodal supervised compensation block(SCB)to supervise the extraction of compensation features.We evaluate the proposed recognition framework on three human action datasets,including NTU RGB+D 60,NTU RGB+D 120,and PKU-MMD.The results demonstrate that our model with FDA-STM and SCB achieves the state-of-the-art recognition performance on three benchmark datasets.

非饱和业务下帧聚合性能分析(英文)

This paper proposes an analysis model of frame aggregation in error-free channel with unsaturated traffic and fixed aggregation size. Integrated with model of channel access, calculation of MAC (Media Access Control) average service time and queue model of frame aggregation, our model can get the stable result with a recursive algorithm, and it further derive the throughput and latency of frame aggregation in steady state. As the impact of traffic, frame length, collision probability, buffer size, aggregation size and interactive effects are taken into consideration, the effect of every parameter could be evaluated and the major factor which degrades the performance of frame aggregation can be determined in different situation with this model. By the simulation and numerical analysis, this model confirmed its accuracy. The proposed model can be used in the design, optimization and deployment of WLAN (Wireless Local Area Network) and WMN (Wireless Mesh Network) widely.