Bringing Mobility-Awareness to WLANs using PHY Layer Information

Li, Sun; Sen, Souvik; Koutsonikolas, Dimitrios

doi:10.1145/2674005.2675017

Cited by 26 publications

(5 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to reduce the overhead of CSI feedback, the sounding interval in an 802.11ac network is on the order of one tenth of a second [43,47]. Therefore, if a user moves at a walking speed between two consecutive sounding period, the last derived CSI is no longer valid in part of the interval between two sounding periods [48]. This unreliable CSI that is used for MU-MIMO beamforming leads to higher Packet Error Rate (PER) and throughput degradation when the moving users are part of the MU-MIMO group [45,47].…”

Section: Overviewmentioning

confidence: 99%

Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi

Pasandi

Amirpour

Nadeem

et al. 2021

Proceedings of the Workshop on Design, Deployment, and Evaluation of Network-Assisted Video Streaming

View full text Add to dashboard Cite

Over the last decade, the bandwidth expansion and MU-MIMO spectral eciency have promised to increase data throughput by allowing concurrent communication between one Access Point and multiple users. However, we are still a long way from enjoying such MU-MIMO MAC protocol improvements for bandwidth hungry applications such as video streaming in practical WiFi network settings due to heterogeneous channel conditions and devices, unreliable transmissions, and lack of useful feedback exchange among the lower and upper layers' requirements. This paper introduces MuViS, a novel dual-phase optimization framework that proposes a Quality of Experience (QoE) aware MU-MIMO optimization for multi-user video streaming over IEEE 802.11ac. MuViS rst employs reinforcement learning to optimize the MU-MIMO user group and mode selection for users based on their PHY/MAC layer characteristics. The video bitrate is then optimized based on the user's mode (Multi-User (MU) or Single-User (SU)). We present our design and its evaluation on smartphones and laptops using 802.11ac WiFi. Our experimental results in various indoor environments and congurations show a scalable framework that can support a large number of users with streaming at high video rates and satisfying QoE requirements.

show abstract

Section: Overviewmentioning

confidence: 99%

Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi

Pasandi

Amirpour

Nadeem

et al. 2021

Proceedings of the Workshop on Design, Deployment, and Evaluation of Network-Assisted Video Streaming

View full text Add to dashboard Cite

show abstract

“…In this section, we compare StateRate with sensor-hint rate adaptation algorithms to evaluate the advantage of employing a deep learning framework in StateRate. Previous work uses PHY hints [30] to improve the performance of rate adaptation. The problem of these algorithms is that they can only distinguish between motion and static scenarios.…”

Section: E Evaluation For Deep Learning Frameworkmentioning

confidence: 99%

“…TiM [41] focuses on inserting modulation schemes between two existing schemes by adding time domain diversity in modulation. A mobility-aware rate adaptation algorithm [30] is proposed to use PHY layer hints extracted from CSI and time of flight (ToF) to improve network performance. SmartLA [42] is a reinforcement learning mechanism for dynamic selection of link parameters, while it does not combine sensors.…”

Section: F Impact Of Environmentsmentioning

confidence: 99%

State-Aware Rate Adaptation for UAVs by Incorporating On-Board Sensors

Wang

Yang

et al. 2020

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Nowadays unmanned aerial vehicles (UAVs) are being widely applied to a wealth of civil and military applications. Robust and high-throughput wireless communication is the crux of these UAV applications. Yet, air-to-ground links suffer from time-varying channels induced by the agile mobility and dynamic environments. Rate adaptation algorithms are generally used to choose the optimal data rate based on the current channel conditions. State-of-the-art approaches leverage physical layer information for rate adaptation, and they work well under certain conditions. However, the above protocols still have limitation under constantly changing flight states and environments for airto-ground links. To solve this problem, we propose StateRate, a state-optimized rate adaptation algorithm that fully exploits the characteristics of UAV systems using a hybrid deep learning model. The key observation is that the rate adaptation strategy needs to be adjusted according to motion-dependent channel models, which can be reflected by flight states. In this work, the rate adaptation protocol is enhanced with the help of the on-board sensors in UAVs. To make full use of the sensor data, we introduce a learning-based prediction module by leveraging the internal state to dynamically store temporal features under variable flight states. We also present an online learning algorithm by employing the pre-trained model that adapts the rate adaptation algorithm to different environments. We implement our algorithm on a commercial UAV platform and evaluate it in various environments. The results demonstrate that our system outperforms the best-known rate adaptation algorithm up to 53% in terms of throughput when the velocity is 2-6 m/s.

show abstract

“…[17][18][19] explore the attenuation characteristics of WiFi signals to locate the position of someone and count the number of people in the indoor environment. Researchers study the signal pattern reflected by a human body to sense human behavior [11,[20][21][22]. These works describe human behavior recognition using coarse-grained RSSI information.…”

Section: Kinect-based Activity Recognitionmentioning

confidence: 99%

HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data

Guo

Wang

Liu

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The joint of WiFi-based and vision-based human activity recognition has attracted increasing attention in the human-computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi-based and Kinect-based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi-based activity recognition dataset named WiAR to provide a benchmark for WiFi-based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton-based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than 93% using WiAR dataset.

show abstract

Bringing Mobility-Awareness to WLANs using PHY Layer Information

Cited by 26 publications

References 46 publications

Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi

Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi

State-Aware Rate Adaptation for UAVs by Incorporating On-Board Sensors

HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data

Contact Info

Product

Resources

About