MAQ: A Multiple Model Predictive Congestion Control Scheme for Cognitive Radio Networks

Xiao, Kefan; Mao, Shiwen; Tugnait, J.K.

doi:10.1109/twc.2017.2669322

Cited by 24 publications

(9 citation statements)

References 40 publications

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“…The reason for this is that the utility function of each upstream SU must be discounted by the factor e −τt at time t under the differential game structure Γ HHCC . As we expected, the optimal data rate of flow 6 can obtain the minimum rate level within approximately interval [6,26] kbps, and the optimal data rate of flow 2 can gain the maximum value of data rate with nearly interval [30,140] kbps. This can be explained by the fact that the higher Euclidean distance of the successful transmission link of flow 2 will result in a higher transmit power level accordingly.…”

Section: Performance Of Otpu Algorithmsupporting

confidence: 56%

“…■. From Theorem 1, we can observe that the existence and uniqueness of the Nash equilibrium point to ℙ1 are guaranteed under the constraint of analytical solution in (25) and (26). It is also revealed that the optimal transmit power p # i ðtÞ has been characterized by a fixed and unique value in (25).…”

Section: Optimal Solution To γ Plpcmentioning

confidence: 90%

“…To the best of our knowledge, some studies on congestion control for CRNs have been reported recently, although the mainstream research effort is aimed at the problems of the physical and MAC layers. In [26], Xiao et al developed a robust active queue management scheme to stabilize the TCP queue length at base station in an infrastructure-based CRN. By using the multiple model predictive control, the proposed scheme absorbed the disturbances caused by busty background traffic and capacity variation.…”

Section: Related Workmentioning

confidence: 99%

“…By using the multiple model predictive control, the proposed scheme absorbed the disturbances caused by busty background traffic and capacity variation. It is found that [26] is not suitable for decentralized CRANET scenario due to a lack of centralized control and global information. Unlike the condition of infrastructure-based CRN, other studies in [18,27,28] have been undertaken in multi-hop CRN scenarios.…”

Section: Related Workmentioning

confidence: 99%

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A cross-layer optimization framework for congestion and power control in cognitive radio ad hoc networks under predictable contact

Zhang

Zhuo

2018

J Wireless Com Network

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In this paper, we investigate the cross-layer optimization problem of congestion and power control in cognitive radio ad hoc networks (CRANETs) under predictable contact constraint. To measure the uncertainty of contact between any pair of secondary users (SUs), we construct the predictable contact model by attaining the probability distribution of contact. In particular, we propose a distributed cross-layer optimization framework achieving the joint design of hopby-hop congestion control (HHCC) in the transport layer and per-link power control (PLPC) in the physical layer for upstream SUs. The PLPC and the HHCC problems are further formulated as two noncooperative differential game models by taking into account the utility function maximization problem and the linear differential equation constraint with regard to the aggregate power interference to primary users (PUs) and the congestion bid for a bottleneck SU. In addition, we obtain the optimal transmit power and the optimal data rate of upstream SUs by taking advantage of dynamic programming and maximum principle, respectively. The proposed framework can balance transmit power and data rate among upstream SUs while protecting active PUs from excessive interference. Finally, simulation results are presented to demonstrate the effectiveness of the proposed framework for congestion and power control by jointly optimizing the PLPC-HHCC problem simultaneously.

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Section: Performance Of Otpu Algorithmsupporting

confidence: 56%

Section: Optimal Solution To γ Plpcmentioning

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A cross-layer optimization framework for congestion and power control in cognitive radio ad hoc networks under predictable contact

Zhang

Zhuo

2018

J Wireless Com Network

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“…Network with high traffic load leads to network congestion [1][2][3][4] and makes the buffer of forwarding router full. Queue size is an important factor affecting network quality of service [5][6][7][8]. When the queue is full, it will sharply increase the loss-rate, drastically reduce throughput, and greatly fluctuate transmission delay.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Queue Management Based On the Change Trend of Queue Size

Tang¹,

Tan²

2019

KSII TIIS

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Most active queue management algorithms manage network congestion based on the size of the queue but ignore the network environment which makes queue size change. It seriously affects the response speed of the algorithm. In this paper, a new AQM algorithm named CT-AQM (Change Trend-Adaptive Queue Management) is proposed. CT-AQM predicts the change trend of queue size in the soon future based on the change rate of queue size and the network environment, and optimizes its dropping function. Simulation results indicate that CT-AQM scheme has a significant improvement in loss-rate and throughput.

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Robust congestion control in cognitive radio network using dynamic event‐triggered sliding mode

Majumder

Mishra

Sinha

et al. 2019

Int J Communication

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The ever-increasing service demand from wireless users under restricted bandwidth necessitates the development of suitable congestion control scheme for throughput maximization. A robust congestion controller design has been addressed in this work to sustain necessary Quality of Service in Cognitive Radio Network. The goal is to reduce packet loss while ensuring optimum utilization of available resources. Sliding mode controller proclaimed for its disturbance rejection capabilities and robustness has been used to design the proposed controller. Using the benefits of sliding mode control, dynamic event-triggering strategies have been combined for better utilization in resource-constrained environments. Control execution requires minimal updates while guaranteeing the desired closed-loop system behavior. The controller efficacy has been validated in simulations.

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MAQ: A Multiple Model Predictive Congestion Control Scheme for Cognitive Radio Networks

Cited by 24 publications

References 40 publications

A cross-layer optimization framework for congestion and power control in cognitive radio ad hoc networks under predictable contact

A cross-layer optimization framework for congestion and power control in cognitive radio ad hoc networks under predictable contact

Adaptive Queue Management Based On the Change Trend of Queue Size

Robust congestion control in cognitive radio network using dynamic event‐triggered sliding mode

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