A novel bandwidth estimation algorithm of TCP westwood in typical LTE scenarios

Chen, Zehang; Liu, Yitong; Duan, Yameng; Líu, Hao; Li, Gang; Chen, Yami; Sun, Junshuai; Zhang, Xin

doi:10.1109/iccchina.2015.7448600

Cited by 7 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simulation results show a good performance. Chen et al [5] proposed a novel algorithm called transmission control protocol (TCP) Westwood with Holt-Winters (TCPWH) to predict the bandwidth in LTE. Lema et al [6] analyzed the factors which can influence the CQI of LTE-A with carrier aggregation, and used the signal to interference plus noise ratio (SINR) to represent CQI.…”

Section: Related Work a Lte Communication Quality Predictionmentioning

confidence: 99%

“…, y T +1 ) are also given. In general, building a multivariate time series prediction model means that we need to learn a nonlinear mapping by using X and y to obtain the predicted CQ value y as in (5)…”

Section: B Problem Statementmentioning

confidence: 99%

“…In (5), f (•) is the objective nonlinear mapping function which we aim to find. adjustment, alarm troubleshooting, interference analysis, etc.…”

Section: B Problem Statementmentioning

confidence: 99%

“…Based on formula (4) and (5), the result of variable filter will be input into DNN together with CQ value (''qci1''), while the CQ value at the next time interval is the prediction target. For example, when training the model on dataset BS1, based on Table 3, the input variables should include ''qci1'', ''qci5'' and ''W-qci5''.…”

Section: Variable Filtermentioning

confidence: 99%

See 3 more Smart Citations

Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In recent years, long term evolution for railway (LTE-R) has been a promising technology to meet the growing demand for railway wireless communication. To realize the active maintenance of LTE-R base station, it is of great significance to precisely predict the communication quality (CQ) of LTE-R base station. Given that the existing LTE CQ prediction methods can not support the active maintenance of LTE-R base station. Furthermore, the LTE-R base station has its unique characteristics in time relationship and regional impact, one of the most challenging problems is to effectively integrate the temporal and spatial information to improve the effect of CQ prediction. To solve the above problems, we choose daily evolved radio access bearer (E-RAB) abnormal release ratio as the CQ indicator, and propose a new deep learning-based CQ prediction approach for LTE-R. Considering the influence of adjacent base stations, this method conducts temporal-spatial collaborative prediction on multivariate time series collected from the CQ data of these stations. First, to eliminate the negative effect of redundant variables, a new variable filter method based on max-relevance, and min-redundancy (MRMR) criterion and binary particle swarm optimization (BPSO) is proposed to select a variable set from the CQ data of related base stations. Second, a new recurrent convolutional neural network (RCNN) model with a self-attention mechanism is proposed to extract temporal-spatial features from the selected variable set. With these features, we build a collaborative prediction model for CQ prediction. Experimental results on real-world LTE-R CQ datasets demonstrate the superiority of the proposed method in CQ prediction.

show abstract

Section: Related Work a Lte Communication Quality Predictionmentioning

confidence: 99%

Section: B Problem Statementmentioning

confidence: 99%

“…In (5), f (•) is the objective nonlinear mapping function which we aim to find. adjustment, alarm troubleshooting, interference analysis, etc.…”

Section: B Problem Statementmentioning

confidence: 99%

Section: Variable Filtermentioning

confidence: 99%

See 2 more Smart Citations

Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Since, Wi-Fi networks experiences high bandwidth fluctuation due to high loss rate. Authors in [2], [15] introduce TCP Veno, TCP Westwood to address the bandwidth fluctuation scenarios, but these variants failed to attain higher goodput as compare to TCP CUBIC. Hence, Google proposed TCP BBR [16], which mitigate the losses in high BDP scenario and adapts the fluctuating bandwidth.…”

Section: Introductionmentioning

confidence: 99%

NexGen D-TCP: Next Generation Dynamic TCP Congestion Control Algorithm

et al. 2020

View full text Add to dashboard Cite

With the advancement of wireless access networks and mmWave New Radio (NR), new applications emerged, which requires a high data rate. The random packet loss due to mobility and channel conditions in a wireless network is not negligible, which degrades the significant performance of the Transmission Control Protocol (TCP). The TCP has been extensively deployed for congestion control in the communication network during the last two decades. Different variants are proposed to improve the performance of TCP in various scenarios, specifically in lossy and high bandwidth-delay product (high-BDP) networks. Implementing a new TCP congestion control algorithm whose performance is applicable over a broad range of network conditions is still a challenge. In this article, we introduce and analyze a Dynamic TCP (D-TCP) congestion control algorithm over mmWave NR and LTE-A networks. The proposed D-TCP algorithm copes up with the mmWave channel fluctuations by estimating the available channel bandwidth. The estimated bandwidth is used to derive the congestion control factor. The congestion window is increased/decreased adaptively based on the calculated congestion control factor. We evaluated the performance of D-TCP in terms of congestion window growth, goodput, fairness and compared it with legacy and existing TCP algorithms. We performed simulations of mmWave NR during LOS <-> NLOS transitions and showed that D-TCP curtails the impact of under-utilization during mobility. The simulation results and live air experiment points out that D-TCP achieves 32.9% gain in goodput as compared to TCP-Reno and attains 118.9% gain in throughput as compared to TCP-Cubic.

show abstract

Enhancing the Performance of Software-Defined Wireless Mesh Network

Shastry

Kumar

2020

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

A novel bandwidth estimation algorithm of TCP westwood in typical LTE scenarios

Cited by 7 publications

References 12 publications

Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning

Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning

NexGen D-TCP: Next Generation Dynamic TCP Congestion Control Algorithm

Enhancing the Performance of Software-Defined Wireless Mesh Network

Contact Info

Product

Resources

About