Decision and Estimation Theory

Melsa, James L.; Cohn, David L.; Siouris, George M.

doi:10.1109/tsmc.1981.4308698

Cited by 131 publications

(81 citation statements)

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“…Observe in Fig. 1 that the performance of the LR-aided detector was improved by updating both the bias and correlation of s = T −1x according to (11) and (12) and hence it becomes capable of approaching the performance of the ML detector in conjunction with N t = 2, N r = 2 and 16-QAM signaling. As argued in Section III, for a low number of transmit antennas the Gaussian model of the effective symbols is somewhat inaccurate.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Optimal Lattice-Reduction Aided Successive Interference Cancellation for MIMO Systems

Lee

Chun

Hanzo

2007

IEEE Trans. Wireless Commun.

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Abstract-In this letter, we investigated the optimal minimummean-squared-error (MMSE) based successive interference cancellation (SIC) strategy designed for lattice-reduction aided multiple-input multiple-output (MIMO) detectors. For the sake of generating the MMSE-based MIMO symbol estimate at each SIC detection stage, we model the so-called effective symbols generated with the aid of lattice-reduction as joint Gaussian distributed random variables. However, after lattice-reduction, the effective symbols become correlated and exhibit a non-zero mean. Hence, we derive the optimal MMSE SIC detector, which updates the mean and variance of the effective symbols at each SIC detection stage. As a result, the proposed detector achieves a better performance compared to its counterpart dispensing with updating the mean and variance, and performs close to the maximum likelihood detector.

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Section: Simulation Resultsmentioning

confidence: 99%

“…Without loss of generality, we assume that the elements of the effective symbol s = T −1x are detected in the order of {s 1 , s 2 , · · · , s N }, where s k is the kth element of s. In the first SIC detection, we obtain the MMSE detector weights in the form of [12] …”

Section: Mmse-sic Detectionmentioning

confidence: 99%

Optimal Lattice-Reduction Aided Successive Interference Cancellation for MIMO Systems

Lee

Chun

Hanzo

2007

IEEE Trans. Wireless Commun.

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“…4 8 shows the theoretical and experimental curves of average number of observations required by the secondary user to make a decision when there are 10 malicious users in the system 9 . The gap between the experimental and theoretical curves are typical of WSPRT [14]. Note that more observations are required as the threshold on false alarm probability reduces from 0.2 to 0.1.…”

Section: ) False Alarm: When the Actual Transmission Is Madementioning

confidence: 96%

“…(17) provides a practical mechanism to detect PUEA. The average number of observations required to arrive at a decision is given by [14] 7 We omit the details of the derivation due to lack of space.…”

Section: ) False Alarm: When the Actual Transmission Is Madementioning

confidence: 99%

Detecting Primary User Emulation Attacks in Dynamic Spectrum Access Networks

Jin

Anand

Subbalakshmi

2009

2009 IEEE International Conference on Communications

137

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Abstract-In this paper, we present an analytical model as well as a practical mechanism to detect denial of service (DoS) attacks on secondary users in dynamic spectrum access (DSA) networks. In particular, we analyze primary user emulation attacks (PUEA) in cognitive radio networks without using any location information and therefore can do away with any dedicated sensor network. To the best of our knowledge, there are no studies available that detect PUEA using analytical models. We present an analysis using Fenton's approximation and Wald's sequential probability ratio test (WSPRT) to detect PUEA. Simulation results demonstrate that it is possible to keep the probability of missing the primary below a desired threshold while at the same time keeping the probability of successful PUEA low.

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“…[7] 반면에, 간헐 소음은 생물, 강우뿐만 아니라 우박, [8] 눈, [8][9] 퇴 적층 이동, [10][11] 천둥이나 항공기 소음, [12][13] (그림 3a), [24] 이에 상응하는 포락선에서 최대우도법 을 이용하여 역치를 정하여(그림 3b) [25] …”

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Extraction of an Underwater Transient Signal Using Sound Mask-filter

Bok¹,

Kim²,

Paeng³

et al. 2012

The Journal of the Acoustical Society of Korea

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An underwater transient signal is distinguished from an ambient noise. Database for the underwater transient signal is required since the underwater transient signal shows various characteristics depending on acoustic features. In the paper, hence, sound mask-filter was applied to extract the transient signals which exist temporally and locally in the ocean. The standard signal was chosen and cross-correlated with the raw signal. A mask-filter for a transient signal was obtained using the threshold which was decided by the maximum likelihood method in the envelope of the cross-correlated signal. Using the sound mask-filter, the transient signal of a sea catfish {Galeichthys felis (Linnaeus)} was extracted from the underwater ambient noise. Similarly, the man-made signal was added into the noise and it was extracted by the same method. We also have demonstrated the significance of the transient signal through comparing the extracted signals depending on the standard signal. In the results, the proposed method, sound mask-filtering, could be utilized as a database construction of the transient signals in underwater noise. Particularly, this study would be useful to extract the wanted signal from arbitrary signals.

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Decision and Estimation Theory

Cited by 131 publications

References 0 publications

Optimal Lattice-Reduction Aided Successive Interference Cancellation for MIMO Systems

Optimal Lattice-Reduction Aided Successive Interference Cancellation for MIMO Systems

Detecting Primary User Emulation Attacks in Dynamic Spectrum Access Networks

Extraction of an Underwater Transient Signal Using Sound Mask-filter

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