Medium Access Control for dynamic spectrum access in cognitive radio networks: analysis under uncertainty

Dong, Qiumin; Niyato, Dusit; Wang, Ping

doi:10.1002/wcm.2203

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The problem of how to further improve the spectrum utilization, the system capacity, and the quality of services in next generation wireless communications requires urgent solutions. Dynamic spectrum access of cognitive radios has been regarded as a promising technique to improve the spectral efficiency of the next generation of wireless communication networks . Spectrum sensing, whose task is to find the white space in the authorized spectrum, is the basic premise to dynamic spectrum access.…”

Section: Introductionmentioning

confidence: 99%

Robust spectrum sensing algorithm based on free probability theory

Wang

Chen

Jiang

et al. 2015

Wireless Communications

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In low signal-to-noise ratio (SNR) cases, the performance of spectrum sensing algorithms cannot meet the practical needs, which is a major problem faced by spectrum sensing technology in current cognitive radio field. Now, existing algorithms based on random matrix theory (RMT) have high sensing performance, but they require a large number of samples, which are very difficult to satisfy in practice. Free probability theory (FPT) is a main branch of RMT. It describes the asymptotic behavior of large random matrices and portrays a strong link between two matrices and their sum or product matrices. FPT can also be utilized to the digital communication system that can be modeled by random matrices and has been applied to spectrum sensing in simplified ideal channels, for example, additive white Gaussian noise channel. The most pivotal issue and difficulty of the FPT-based methods is to set up and solve the asymptotic freeness equation corresponding to a specific communication model. In this paper, FPT-based spectrum sensing schemes are proposed for some typical wireless communication systems, such as multiple-input multiple-output system, Rayleigh multipath fading system, and orthogonal frequency division multiplexing system. It is shown that the asymptotic freeness behavior of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for these typical systems with low SNR and very limited samples. Simulation results demonstrate that compared with the existing RMT-based spectrum detection methods, for example, the maximum and minimum eigenvalue detectors, the proposed FPT-based schemes offer superior detection performance and are more robust to low SNR cases, especially for a small sample of observations. Robust spectrum sensing algorithm based on free probability theory L. Wang et al. Free probability theory (FPT) [27-31] is a valuable toolfor describing the asymptotic behavior of random matrices. It has grown into a main branch of RMT through the pioneering work of Voiculescu in the 1980s [32,33]. FPT portrays a strong link between two matrices and their sum matrix or product matrix, which is completely different from other branches of RMT. For the purpose of spectrum sensing, it can separate the eigenvalues of real signal matrix from that of the received sample covariance matrix, which includes both signal and noise matrices. This is almost impossible in traditional mathematics. The most pivotal issue of the FPT-based methods is to set up and solve the asymptotic freeness equation corresponding to a specific communication model. In some simplified communication environment, FPT has been managed to utilize in the field of spectrum sensing [34,35]. However, for some typical wireless communication scenarios, such as multiple-input multiple-output (MIMO) Rayleigh channels, MIMO multipath channels, and MIMO-orthogonal frequency division multiplexing (OFDM) channels, it needs to be further studied.

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Section: Introductionmentioning

confidence: 99%

Robust spectrum sensing algorithm based on free probability theory

Wang

Chen

Jiang

et al. 2015

Wireless Communications

View full text Add to dashboard Cite

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“…In order to satisfy that rigorous requirement, cognitive networks are designed to include two crucial components: spectrum sensing [8][9][10] and dynamic spectrum accessing [11][12][13]. Spectrum sensing has two functions: (i) cognitive users should have strong enough ability to detect active primary users to guarantee normal communication of primary users, that is, to increase the probability of detection; Spectrum sensing and accessing X.…”

Section: Introductionmentioning

confidence: 99%

Joint optimization of spectrum sensing and accessing in multiuser multiple‐input single‐output cognitive networks

Chen

Yuen

2014

Wireless Communications

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In this paper, a joint spectrum sensing and accessing optimization framework for a multiuser cognitive network is proposed to significantly improve spectrum efficiency. For such a cognitive network, there are two important and limited resources that should be distributed in a comprehensive manner, namely feedback bits and time duration. First, regarding the feedback bits, there are two components: sensing component (used to convey various users' sensing results) and accessing component (used to feedback channel state information). A large sensing component can support more users to perform cooperative sensing, which results in high sensing precision. However, a large accessing component is preferred as well, as it has a direct impact on the performance in the multiuser cognitive network when multi-antenna technique, such as zero-forcing beamforming, is utilized. Second, the tradeoff of sensing and accessing duration in a transmission interval needs to be determined, so that the sum transmission rate is optimized while satisfying the interference constraint. In addition, the aforementioned two resources are interrelated and inversive under some conditions. Specifically, sensing time can be saved by utilizing more sensing feedback bits for a given performance objective. Hence, the resources should be allocation in a jointly manner. Based on the joint optimization framework and the intrinsic relationship between the two resources, we propose two joint resource allocation schemes by maximizing the average sum transmission rate in a multiuser multi-antenna cognitive network. Simulation results show that, by adopting the joint resource allocation schemes, obvious performance gain can be obtained over the traditional fixed strategies.

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