Anticipatory wireless bitrate control for blocks

Tie, Xiaozheng; Seetharam, A.; Venkataramani, Arun; Ganesan, Deepak; Goeckel, Dennis

doi:10.1145/2030686.2030695

Cited by 4 publications

(7 citation statements)

References 3 publications

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“…As an example, the inter-download times of video segments are predicted in [102], where the output sequences are the interdownload times of the already downloaded segments and the states are the instants of the next download request. ARIMA: [13], [38], [40], [46], [47], [54], [58], [59], [63], [100], [119] Kalman: [32], [ CF: [16], [134], [149] Cluster: [15], [34], [51], [117], [122], [123], [148], [156] Decision trees: [35], [98], [ Functional: [28], [29], [38], [64], [99], [104], [105] SVM: [51], [114], [139] ANN: [14], [48], [106], [ 2) Bayesian inference: This approach allows to make statements about what is unknown, by conditioning on what is known. Bayesian prediction can be summarized in the following steps: 1) define a model that expresses qualitative aspects of our knowledge but has unknown parameters, 2) specify a prior probability distribution for the unknown parameters, 3) compute the posterior probability distribution for the parameters, given the observed data, and 4) make predictions by averaging ove...…”

Section: Statistical Methods For Probabilistic Forecastingmentioning

confidence: 99%

“…While predicting small-scale fading is quite challenging, if not impossible, several papers focuses on predicting path loss and shadowing effects. In [47], the time-varying nonlinear wireless channel model is adopted to predict the channel quality variation anticipating distance and pathloss exponent. The performance evaluation is done using both an indoor and an outdoor testbed.…”

Section: B Link Contextmentioning

confidence: 99%

“…In [40], an exponential weighted moving average, equivalent to ARIMA(0, 1, 1), is used to forecast handoffs. In [13], [47], AR models are applied to predict future signalto-noise ratio values and user positions, respectively. If the variance of the data varies with time, as in [110] for data traffic, and can be expressed using an ARMA, then the whole model is referred to as GARCH.…”

Section: A Time Series Predictive Modelingmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui

Cesana

Hosseini³

et al. 2017

IEEE Commun. Surv. Tutorials

217

137

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A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.

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Section: Statistical Methods For Probabilistic Forecastingmentioning

confidence: 99%

Section: B Link Contextmentioning

confidence: 99%

Section: A Time Series Predictive Modelingmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui

Cesana

Hosseini³

et al. 2017

IEEE Commun. Surv. Tutorials

217

137

View full text Add to dashboard Cite

show abstract

“…Disconnection prediction and topology management in mobile adhoc networks would also be aided by channel quality prediction at a coarse time granularity. Rate control on a block of data is gaining popularity [17] and a successful implementation of a block-based scheme would require a coarse timescale channel model to predict channel variations from one block to the next (a block can take 1-2 seconds to be transmitted) coupled with a fine grained tracking of signal strength fluctuations within a block.…”

Section: Related Work and Applicationsmentioning

confidence: 99%

A Markovian Model for Coarse-Timescale Channel Variation in Wireless Networks

Seetharam

Kurose

Goeckel

2016

IEEE Trans. Veh. Technol.

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A wide range of wireless channel models have been developed to model variations in received signal strength. In contrast to prior work, which has focused primarily on channel modeling on a short, per-packet timescale (millisecond), we develop and validate a finite-state Markovian model that captures variations due to shadowing, which occur at coarser time scales. The Markov chain is constructed by partitioning the entire range of shadowing into a finite number of intervals. We determine the Markov chain transition matrix in two ways: (i) via an abstract modeling approach in which shadowing effects are modeled as a log-normally distributed random process affecting the received power, and the transition probabilities are derived as functions of the variance and autocorrelation function of shadowing; (ii) via an empirical approach, in which the transition matrix is calculated by directly measuring the changes in signal strengths. We test the assumptions of our Markovian model using signal strength measurements collected over an 802.16e (WiMAX) network and a wireless multi-hop network deployed by Rice University. We compare the steady state and transient performance of the model with those computed using the empirically derived transition matrix and those observed in the actual traces themselves.

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“…PBAR [37], OAR [67], CHARM [43], BlockRate [79], and Medusa [71]. Due to frequency selective fading, Zhang et al [103] and Camp et al [17] observe that the SNR measurement needs careful calibration.…”

Section: Chapter 2 Related Workmentioning

confidence: 99%

Enhancing the performance of Wi-Fi system by exploiting physical layer information

Xie¹

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Wi-Fi network has been ubiquitous nowadays and has changed our lifestyle. As a communication system, Wi-Fi delivers more than 50% of IP traffic. Consequently, the demand for higher transmission capacity has been increasing continuously and rapidly. The wireless spectrum for Wi-Fi communication is, however, finite. Therefore, one important research direction is to fully utilize the limited resources and at the same time improve the transmission throughput for Wi-Fi network. On the other hand, a lot of emerging applications, e.g., indoor navigation, human tracking, device free gesture control, are built on top of existing commercial Wi-Fi infrastructures to provide a variety of functionalities except communication. All those applications rely on Wi-Fi's capability of sensing the physical world: strong sensing capability will significantly improve the performance of existing applications and extend the scope of potential applications. Therefore, another important research direction is to enhance the sensing capability of existing Wi-Fi infrastructure. This thesis focuses on these two directions and exploits rich information in the PHY layer to build a better Wi-Fi system that has higher communication speed and stronger sensing capability. We observe that modern wideband Wi-Fi communication has unevenly distributed bit BERs in a packet because of the frequency selective fading. Based on such an observation, we propose UnPKT, a system that can unequally protect Wi-Fi packet bits according to their BERs. By doing so, we can best match the effective transmission rate of each bit to channel condition, and improve throughput. We derive an accurate relationship between the frequency selective channel condition and the decoded packet bit BERs, all the way through the complex 802.11 PHY layer. A cluster-based protection scheme is proposed to protect packet bits using different MAC-layer FEC redundancies based on bit-wise BER estimation to augment wide band 802.11 transmissions. UnPKT is software-implementable and compatible with the existing 802.11 architecture. Extensive evaluations based on Atheros 9580 NICs and GNU-Radio platforms show i

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Anticipatory wireless bitrate control for blocks

Cited by 4 publications

References 3 publications

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

A Markovian Model for Coarse-Timescale Channel Variation in Wireless Networks

Enhancing the performance of Wi-Fi system by exploiting physical layer information

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