Dynamic spectrum allocation for heterogeneous cognitive radio networks with multiple channels

The rapid growth of wireless communication technology has resulted in the increasing demand on spectrum resources. However, according to a recent study, most of the allocated frequency experiences significant underutilization. One important issue associated with spectrum management in heterogeneous...

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Bibliographic Details
Main Authors: Zhang, Wenjie, Sun, Yingjuan, Deng, Lei, Yeo, Chai Kiat, Yang, Liwei
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2019
Subjects:
Online Access:https://hdl.handle.net/10356/82507
http://hdl.handle.net/10220/48002
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Institution: Nanyang Technological University
Language: English
Description
Summary:The rapid growth of wireless communication technology has resulted in the increasing demand on spectrum resources. However, according to a recent study, most of the allocated frequency experiences significant underutilization. One important issue associated with spectrum management in heterogeneous cognitive radio networks is: How to appropriately allocate the spectrum to secondary sender-destination (S-D) pair for sensing and utilization. In this paper, the authors investigate the spectrum allocation problem under a more practical scenario where the heterogeneous characteristics of both the secondary S-D and primary channels are taken into consideration. With the objective to maximize the achievable throughput for secondary S-D, we formulate the spectrum allocation problem as a linear integer optimization problem under spectrum availability constraint, spectrum span constraint, and interference free constraint. This problem is proven to be Non-deterministic Polynomial (NP)-complete, and a recent result in theoretical computer science called randomized rounding algorithm with polynomial computational complexity is developed to find the $\rho$-approximation solution. Evaluation results show that our proposed algorithm can achieve a close-to-optimal solution at a low level of computation complexity.