Globally Stable Wireless Data Flow Control

Wigren, Torbjörn; Karaki, Reem

doi:10.1109/tcns.2016.2619906

Cited by 14 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12 Such URLLC use cases thus include industrial robot control, 1 electrical grid control, 1 inter-vehicle automotive safety applications, 1 augmented reality, 1 as well as tactile internet applications. 2 The delay requirements can be supported by techniques for delay control, 13 however, for cases with simultaneous high reliability and low delay, also remaining jitter suppression becomes an issue. This is so since in most closed loop feedback control applications, a delayed packet is a lost packet.…”

Section: Wigrenmentioning

confidence: 99%

Robust networked ℒ2$$ {\mathcal{L}}_2 $$‐stable wireless SINR control

Wigren

2024

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

In the new radio wireless system, use cases for automation, automotive safety and virtual reality will require transmission and reception with very high reliability and very low latency. Radio channel fading and the onset of strong interference combine to make this task hard, which require new methods for mitigation. The alternative studied in this article is to introduce fast signal to interference and noise ratio (SINR) feedback control supported by feed‐forward control from interference estimates, thereby improving the robustness of the networked loop. This challenging nonlinear networked control problem involves delay and coarse control signal quantization. To guarantee that the deployed feedback loop instances are always well‐behaved, the SINR feedback controller is designed to be globally ‐stable using a new loop gain—loop delay product condition, derived from the classical circle criterion. Monte‐Carlo simulations show that the proposed controller is able to reduce the SINR back‐off significantly.

show abstract

Section: Wigrenmentioning

confidence: 99%

Robust networked ℒ2$$ {\mathcal{L}}_2 $$‐stable wireless SINR control

Wigren

2024

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also, once the simulated annealing is used, a high computational effort is required, becoming the flow rate control unfeasible to real-time applications. Aiming to reduce the computational effort, in [22, 23] the authors present a flow control algorithm with low computational complexity for wireless networks. To this end, they propose the use of the Linear Quadratic Gaussian (LQG) to control the network flow rate.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy flow rate control considering multifractal traffic modeling and 5G communications

Cardoso

Vieira

2019

PLoS ONE

View full text Add to dashboard Cite

In this paper, we propose a predictive Generalized OBF (Orthonormal Basis Functions)-Fuzzy flow control scheme for the 5G downlink by deriving an expression for the optimal control rate of the traffic sources considering minimization of data delay and a minimum traffic rate to the users. The adaptive GOBF-Fuzzy model is applied to predict queueing behavior in initial 5G systems. To this end, we propose to obtain orthonormal basis functions related to the real traffic flows via multifractal modeling, inserting these functions into the fuzzy model trained with the LMS (Least Mean Square) adaptive algorithm. Simulations of a F-OFDM (Filtered Orthogonal Frequency Division Multiplexing) based 5G Downlink are carried out to validate the proposed flow control algorithm. Comparisons with other predictive control schemes in the literature prove the efficiency of the adaptive GOBF-fuzzy based control in enhancing the performance of the system downlink as well as guaranteeing some QoS (Quality of Service) parameters.

show abstract

Discrete and data packet delays as determinants of switching stability in wireless sensor networks

Upadhyay

Kumari

2019

Applied Mathematical Modelling

View full text Add to dashboard Cite

Globally Stable Wireless Data Flow Control

Cited by 14 publications

References 19 publications

Robust networked ℒ2$$ {\mathcal{L}}_2 $$‐stable wireless SINR control

Robust networked ℒ2$$ {\mathcal{L}}_2 $$‐stable wireless SINR control

Adaptive fuzzy flow rate control considering multifractal traffic modeling and 5G communications

Discrete and data packet delays as determinants of switching stability in wireless sensor networks

Contact Info

Product

Resources

About