Exploring Smart Pilot for Wireless Rate Adaptation

Wang, Lu; Qi, Xiaoke; Xiao, Jiang; Wu, Kaishun; Hamdi, Mounir; Zhang, Qian

doi:10.1109/twc.2016.2542807

Cited by 22 publications

(9 citation statements)

References 19 publications

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“…ESNR [14] introduces a new metric named ESNR to combat the frequency-selective fading. In addition, Smart Pilot [17] calibrates CSI by combining packet headers from the upper layer. Softrate [16] and AccuRate [39] bring SoftPHY hints to estimate BER for each packet even when the packet has no errors.…”

Section: F Impact Of Environmentsmentioning

confidence: 99%

“…Traditionally, rate adaptation algorithms have been proposed to optimize communication quality in wireless networks by dynamically tuning the modulation and coding scheme (MCS) according to channel variances. Prior works use either packet loss [8]- [11] or signal-to-noise rate (SNR) [12]- [17] extracted from the received signal strength indicator (RSSI) and channel state information (CSI) to pick the optimal bit rate. Most of the loss-based rate adaptation algorithms are highly inaccurate when applied to the mobile scenario because the packet loss is calculated by hundreds of frames and they fail to consider the impact induced by motion.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Yang

et al. 2020

IEEE Trans. Veh. Technol.

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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.

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Section: F Impact Of Environmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang

Yang

et al. 2020

IEEE Trans. Veh. Technol.

Self Cite

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“…In recent years, mobility management has been studied in many types of networks, including IP and future networks [3,[22][23][24][25], 4G and 5G networks [2,4,10,26], wireless sensor networks [5,[27][28][29][30][31][32], and mobile cloud computing [6,11]. In [3], the authors investigate handover and mobility issues for IP-based multimedia services and applications.…”

Section: Network Mobility Managementmentioning

confidence: 99%

A Novel Indoor Localization Algorithm for Efficient Mobility Management in Wireless Networks

Xiao

Zhang

Zhu

2018

Wireless Communications and Mobile Computing

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Along with the penetration of smart devices and mobile applications in our daily life, how to effectively manage the mobility issues in wireless networks becomes a challenging task. The ability to continuously and accurately track the target object's position plays a vital role in mobility management. In this paper, we propose a novel indoor localization algorithm that fuses multiple signal features as the location fingerprints. The rationale that motivates our algorithm design stems from the following observation: although using one special signal feature (e.g., channel state information (CSI)) might achieve statistically higher accuracy than using another signal feature (e.g., received signal strength (RSS)), the accuracy for individual position estimations is usually diversified when only one signal feature is used in localization. For example, using RSS can obtain more accurate location estimation than using CSI for some individual positions. Thus, we propose a novel indoor localization algorithm that fuses multiple types of signal features as fingerprint of positions, which can effectively improve localization accuracy. We designed several fusion schemes and evaluated their performance. Experiments show that our algorithm achieves localization error below 0.5m and 1.1m in two typical indoor environments, about 30% lower than the accuracy of algorithms by fusing multiple signal features.

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“…Most of the existing works [17][18][19][20][21][22][23] propose effective methods to estimate the cost of a given workload over candidate physical configurations. However, only a few of them consider the situations where data can be replicated [20,21].…”

Section: Related Workmentioning

confidence: 99%

Diverse Mobile System for Location‐Based Mobile Data

Liao

Tan

Luo

et al. 2018

Wireless Communications and Mobile Computing

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The value of large amount of location-based mobile data has received wide attention in many research fields including human behavior analysis, urban transportation planning, and various location-based services. Nowadays, both scientific and industrial communities are encouraged to collect as much location-based mobile data as possible, which brings two challenges: (1) how to efficiently process the queries of big location-based mobile data and (2) how to reduce the cost of storage services, because it is too expensive to store several exact data replicas for fault-tolerance. So far, several dedicated storage systems have been proposed to address these issues. However, they do not work well when the ranges of queries vary widely. In this work, we design a storage system based on diverse replica scheme which not only can improve the query processing efficiency but also can reduce the cost of storage space. To the best of our knowledge, this is the first work to investigate the data storage and processing in the context of big location-based mobile data. Specifically, we conduct in-depth theoretical and empirical analysis of the trade-offs between different spatial-temporal partitioning and data encoding schemes. Moreover, we propose an effective approach to select an appropriate set of diverse replicas, which is optimized for the expected query loads while conforming to the given storage space budget. The experiment results show that using diverse replicas can significantly improve the overall query performance and the proposed algorithms for the replica selection problem are both effective and efficient.

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Exploring Smart Pilot for Wireless Rate Adaptation

Cited by 22 publications

References 19 publications

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

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

A Novel Indoor Localization Algorithm for Efficient Mobility Management in Wireless Networks

Diverse Mobile System for Location‐Based Mobile Data

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