Performance evaluation and optimization of cognitive radio networks with adjustable access control for multiple secondary users

Yuan Zhao; Wuyi Yue

doi:10.3934/jimo.2018029

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2019, Volume 15, Issue 1: 1-14. Doi: 10.3934/jimo.2018029

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Performance evaluation and optimization of cognitive radio networks with adjustable access control for multiple secondary users

Yuan Zhao^1, , and
Wuyi Yue^2,

1.
School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
2.
Department of Intelligence and Informatics, Konan University, Kobe 658-8501, Japan

* Corresponding author: Yuan Zhao
* Corresponding author: Yuan Zhao

Received: January 31, 2017

Revised: June 30, 2017

Early access: February 2018

Published: December 2018

The reviewing process of this paper was handled by Yutaka Takahashi

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Abstract

In this paper, we consider a cognitive radio network with multiple secondary users (SUs). The SU packets in the system can be divided into two categories: SU1 packets and SU2 packets, where SU1 packets have transmission priority over SU2 packets. Considering the absolute priority of the primary users (PUs), the PU packets have the highest priority in the system to transmit. In order to guarantee the Quality of Service (QoS) of the network users, as well as reduce the average delay of the SU2 packets, we propose an adjustable access control scheme for the SU2 packets. A newly arriving SU2 packet can access the system with an access probability related to the total number of packets in the system. A variable factor is also introduced to adjust the access probability dynamically. Based on the working principle of the adjustable access control scheme, we build a discrete-time queueing model with a finite waiting room and an adjustable joining rate. With a steady-state analysis of the queueing model, using a three-dimensional Markov chain, we derive some performance measures, such as the total channel utilization, the interruption rate, the throughput, and the average delay of the SU2 packets. Moreover, we show the influence of the adjustment factor on different system performance measures by using numerical results. Finally, considering the trade-off between the throughput and the average delay of the SU2 packets with respect to the adjustment factor, we build a net benefit function and show an optimal algorithm to optimize the adjustment factor.

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Mathematics Subject Classification: Primary: 68M10, 68M20; Secondary: 60J10.

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Figure 1. Diagram for the proposed adjustable access control scheme

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Figure 2. Total channel utilization $\delta$ vs. adjustment factor $\tau$

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Figure 3. Interruption rate $\gamma$ of the SU2 packets vs. adjustment factor $\tau$

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Figure 4. Throughput $\theta$ of the SU2 packets vs. adjustment factor $\tau$

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Figure 5. Average delay $\sigma$ of the SU2 packets vs. adjustment factor $\tau$

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Table 1. Optimal adjustment factor $\tau^*$ and the maximum net benefit $B(\tau^*)$

Buffer capacity	Arrival rates of packets	Optimal adjustment factor	Maximum net benefit
$K$	$\lambda_1, \lambda_{21}, \lambda_{22}$	$\tau^*$	$B(\tau^*)$
$5$	$\lambda_1=0.1, \lambda_{21}=0.1, \lambda_{22}=0.2$	0.0006	7.4084
	$\lambda_1=0.2, \lambda_{21}=0.1, \lambda_{22}=0.2$	0.1178	3.5230
	$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.2$	0.3232	0.0113
	$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.3$	0.5887	2.4589
$10$	$\lambda_1=0.1, \lambda_{21}=0.1, \lambda_{22}=0.2$	0.0252	7.3280
	$\lambda_1=0.2, \lambda_{21}=0.1, \lambda_{22}=0.2$	0.1341	3.4942
	$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.2$	0.3342	0.0031
	$\lambda_1=0.2, \lambda_{21}=0.2, \lambda_{22}=0.3$	0.6012	2.4494

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