基于擴(kuò)散策略的實(shí)時(shí)分布式協(xié)作頻譜檢測算法

張政保; 姚少林; 許鑫; 劉廣凱

doi:10.11999/JEIT150460

基于擴(kuò)散策略的實(shí)時(shí)分布式協(xié)作頻譜檢測算法

doi: 10.11999/JEIT150460 cstr: 32379.14.JEIT150460

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出版歷程
- 收稿日期: 2015-04-22
- 修回日期: 2015-07-03
- 刊出日期: 2015-12-19

Real-time Distributed Cooperative Spectrum DetectionAlgorithm Based on Diffusion Strategy

摘要

摘要: 針對傳統(tǒng)分布式協(xié)作頻譜檢測算法認(rèn)知用戶不能實(shí)時(shí)檢測問題，該文提出基于擴(kuò)散策略的實(shí)時(shí)分布式協(xié)作檢測算法。算法利用各個(gè)節(jié)點(diǎn)的本地代價(jià)表示全局代價(jià)，通過最小化各個(gè)節(jié)點(diǎn)的代價(jià)使得全局代價(jià)最小。采用最速下降法，利用迭代方式計(jì)算各個(gè)節(jié)點(diǎn)檢測量的最優(yōu)估計(jì)值，得出估計(jì)值的理論穩(wěn)態(tài)均值和方差，得出虛警概率、檢測概率以及檢測門限的封閉表達(dá)式。理論分析和實(shí)驗(yàn)結(jié)果表明，該算法能夠有效解決分布式網(wǎng)絡(luò)認(rèn)知節(jié)點(diǎn)的實(shí)時(shí)檢測問題，并具備快速學(xué)習(xí)和適應(yīng)環(huán)境變化的能力。當(dāng)虛警概率為0.01且檢測概率達(dá)到0.9時(shí)，平均信噪比較平均共識和非實(shí)時(shí)擴(kuò)散策略降低了約6 dB，能夠?qū)崿F(xiàn)在極低信噪比條件下的信號檢測。
- 認(rèn)知無線電 /
- 協(xié)作頻譜感知 /
- 分布式估計(jì) /
- 擴(kuò)散策略 /
- 共識策略
Abstract: Considering the problem of real-time distributed cooperative spectrum detection of cognitive users, a real-time distributed cooperative spectrum detection algorithm based on diffusion strategy is proposed. Global cost function can be approximated by an alternative localized cost that is amenable to distributed optimization. Each individual node optimizes this alternative cost via a steep-descent procedure that relies solely on interaction within the neighborhood of the node. The local estimate value can be calculated via the iteration procedure. A general model for analyzing the mean and variance of the estimates of the diffusion strategy is derived. The formulas of probability of detection, probability of false alarm and detection threshold are derived. Theoretical analysis and experimental results show that the proposed algorithm can effectively solve the problem of real-time detection signal, can quickly learn and adapt to environmental changes. Compared with average consensus strategy and non-real-time diffusion strategy, the average SNR of the proposed algorithm reduces about 6 dB, while the Pfa below 0.01 and Pd reached to 0.9. The diffusion strategy can satisfy the signal detection in very low SNR.
- Cognitive radio /
- Cooperative spectrum sensing /
- Distributed estimation /
- Diffusion strategy /
- Consensus strategy

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參考文獻(xiàn)(16)

Wu Q H, Ding G R, Wang J L, et al.. Consensus-based decentralized clustering for cooperative spectrum sensing in cognitive radio networks[J]. Chinese Science Bulletin, 2012, 57(28/29): 3677-3683.

Zeng Y, Liang Y C, Hoang A T, et al.. A review on spectrum sensing for cognitive radio: challenges and solutions[J]. EURASIP Journal on Advances in Signal Processing, 2010, DOI: 10.1155/2010/381465.

Bogdanovie N, Plata-Chaves J, and Berberidis K. Distributed diffusion-based LMS for node-specific parameter estimation over adaptive networks[C]. 2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Florence, Italy, 2014: 7223-7227.

Zhang W, Wang Z, Guo Y, et al.. Distributed cooperative spectrum sensing based on weighted average consensus[C]. IEEE Global Telecommunications Conference (GLOBECOM 2011), Houston, TX, USA, 2011: 1-6.

Tu S Y and Sayed A H. Diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks[J], IEEE Transactions on Signal Processing, 2012, 60(12): 6217-6234.

Cattivelli F S and Sayed A H. Diffusion LMS strategies for distributed estimation[J]. IEEE Transactions on Signal Processing, 2010, 58(3): 1035-1048.

Chen J and Sayed A H. Diffusion adaptation strategies for distributed optimization and learning over networks[J]. IEEE Transactions on Signal Processing, 2012, 60(8): 4289-4305.

Ainomae A, Trump T, and Bengtsson M. Distributed diffusion LMS based energy detection[C] 2014 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), St. Petersburg, 2014: 176-183.

Cattivelli F S and Sayed A H. Distributed detection over adaptive networks using diffusion adaptation[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 1917-1932.

Takahashi N, Yamada I, and Sayed A H. Diffusion least-mean squares with adaptive combiners: Formulation and performance analysis[J]. IEEE Transactions on Signal Processing, 2010, 58(9): 4795-4810.

Al-Sayed S, Zoubir A M, and Sayed A H. Robust distributed detection over adaptive diffusion networks[C] 2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Florence, Italy, 2014: 7233-7237.

Zeng Y and Liang Y C. Eigenvalue-based spectrum sensing algorithms for cognitive radio[J]. IEEE Transactions on Communications, 2009, 57(6): 1784-1793.

趙曉暉, 李曉燕. 認(rèn)知無線電中基于陣列天線和協(xié)方差矩陣的頻譜感知算法[J]. 電子與信息學(xué)報(bào), 2014, 36(7): 1693-1698.

Zhao Xiao-hui and Li Xiao-yan. Spectrum sensing algorithm in cognitive radio based on array antenna and covariance matrix[J]. Journal of Electronics Information Technology, 2014, 36(7): 1693-1698.

袁龍, 邢祿, 彭濤, 等. 基于精確噪聲估計(jì)的迭代頻譜感知算法[J]. 電子與信息學(xué)報(bào), 2014, 36(3): 655-661.

Yuan Long, Xing Lu, Peng Tao, et al.. An iterative spectrum sensing algorithm based on accurate noise estimation[J]. Journal of Electronics Information Technology, 2014, 36(3): 655-661.

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