Spectrum Sensing Using Average of Scaled Off-Diagonal Entries of a Sample Covariance Matrix for Multiantenna Cognitive Radio
- 주제(키워드) Blind spectrum sensing; Cognitive radio; Cooperative spectrum sensing; Covariance Matrix; Multi-antenna
- 발행기관 아주대학교
- 지도교수 Chaewoo Lee
- 발행년도 2017
- 학위수여년월 2017. 2
- 학위명 석사
- 학과 및 전공 일반대학원 전자공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000024370
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Though in cognitive radio networks, cooperative spectrum sensing is an indispensable task that is intended for increasing spectrum utilization by accessing a licensed spectrum band or a primary user’s (PU) signal in an opportunistic manner, it is done in uncertain approach that the uncertainty is a recipe for a degradation of cognitive network users' throughput and interference to a PU’s signal. In this paper – heuristically – we devise a means for spectrum sensing for multiantenna based cognitive radio. By first dividing samples received at each antenna by their corresponding biased sample standard deviation, a sample covariance matrix (SCM) is computed. From the SCM, an average of the off-diagonal entries are used in establishing test statistics (TS), thereby enabling in formation of a TS that can combat effect of uncalibrated receivers which is a cause for differences in white noise variances. We approximated decision threshold of the TS using central limit theorem. The merit of the decision threshold of the work is that operational threshold setting at a certain desired false alarm probability does not rely on prior knowledge of noise variances. This mechanism in turn can enable in overcoming problem of noise uncertainty. Furthermore, knowledge of channel state information, and PU signal’s distribution are not needed. Correlating with our overarching goal, simulation results – which conducted at low signal to noise ratio realm– indicate that the proposed technique significantly improved performance as compared with existing methods.
more목차
1. Preliminaries … 1
1.1 Background … 1
1.2 Components … 2
1.3 Network Architecture … 3
1.3.1 Infrastructures Based CRN Architecture … 3
1.3.2 Ad-hoc Architecture … 4
1.3.3 Mesh CRN Architecture … 4
1.4 Spectrum Sensing … 4
1.4.1 Local Sensing … 4
1.4.2 Cooperative spectrum sensing (CSS) … 4
1.4.2.1 Soft Combining … 6
1.4.2.2 Hard Combining … 6
1.4.2.3 Soft-Hard Combining … 7
1.5 Spectrum Sensing Challenges … 7
1.5.1 Common Control Channel (CCC) problem … 7
1.5.2 Noise variance uncertainty problem … 7
1.6 Probabilistic terms … 8
1.6.1 Test statistics … 8
1.6.2 Decision threshold … 8
1.6.3 False alarm probability … 8
1.6.4 Detection probability … 8
1.6.5 IID white noise … 8
1.6.6 IND white noise … 8
2. Multi-antenna Based Spectrum Sensing … 9
2.1 Introduction … 9
2.2 Related Works … 12
2.3 Problem Formulation … 14
2.4 Proposed Work … 15
2.4.1 Test Statistics … 16
2.4.2 Operational Threshold … 17
2.5 Simulation Results … 21
2.5.1 Accuracy of Detection Threshold … 22
2.5.2 Detection Probability … 28
3. Conclusion … 29
4. References … 30
