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Nonlinear detection for a high rate extended binary phase shift keying system.

Chen XQ, Wu LN - Sensors (Basel) (2013)

Bottom Line: Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector.However, unlike the TD detector, the SVM detector concentrates not only on reducing the BER of the detector, but also on providing accurate posterior probability estimates (PPEs), which can be used as soft-inputs of the LDPC decoder.We find that the SVM is suitable for extended binary phase shift keying (EBPSK) signal detection and can provide accurate posterior probability for LDPC decoding.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Engineering, University of Southeast, Nanjing 210096, China. xqchen@seu.edu.cn

ABSTRACT
The algorithm and the results of a nonlinear detector using a machine learning technique called support vector machine (SVM) on an efficient modulation system with high data rate and low energy consumption is presented in this paper. Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector. The two detectors detect the received signals together with the special impacting filter (SIF) that can improve the energy utilization efficiency. However, unlike the TD detector, the SVM detector concentrates not only on reducing the BER of the detector, but also on providing accurate posterior probability estimates (PPEs), which can be used as soft-inputs of the LDPC decoder. The complexity of this detector is considered in this paper by using four features and simplifying the decision function. In addition, a bandwidth efficient transmission is analyzed with both SVM and TD detector. The SVM detector is more robust to sampling rate than TD detector. We find that the SVM is suitable for extended binary phase shift keying (EBPSK) signal detection and can provide accurate posterior probability for LDPC decoding.

No MeSH data available.


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Cross-validation result of the SVM detector in RBF kernel.
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f6-sensors-13-04327: Cross-validation result of the SVM detector in RBF kernel.

Mentions: In this subsection, the performance of the SVM detector, using the kernel functions (10) and (11), introduced in Section 3, is compared. The 10-fold cross-validation sweep from the training samples was used to find the optimum parameters of C and γ for the RBF kernel. Figure 6 shows that the width γ has a more dominating effect on the error rate than the penalty parameter C.


Nonlinear detection for a high rate extended binary phase shift keying system.

Chen XQ, Wu LN - Sensors (Basel) (2013)

Cross-validation result of the SVM detector in RBF kernel.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3673086&req=5

f6-sensors-13-04327: Cross-validation result of the SVM detector in RBF kernel.
Mentions: In this subsection, the performance of the SVM detector, using the kernel functions (10) and (11), introduced in Section 3, is compared. The 10-fold cross-validation sweep from the training samples was used to find the optimum parameters of C and γ for the RBF kernel. Figure 6 shows that the width γ has a more dominating effect on the error rate than the penalty parameter C.

Bottom Line: Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector.However, unlike the TD detector, the SVM detector concentrates not only on reducing the BER of the detector, but also on providing accurate posterior probability estimates (PPEs), which can be used as soft-inputs of the LDPC decoder.We find that the SVM is suitable for extended binary phase shift keying (EBPSK) signal detection and can provide accurate posterior probability for LDPC decoding.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Engineering, University of Southeast, Nanjing 210096, China. xqchen@seu.edu.cn

ABSTRACT
The algorithm and the results of a nonlinear detector using a machine learning technique called support vector machine (SVM) on an efficient modulation system with high data rate and low energy consumption is presented in this paper. Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector. The two detectors detect the received signals together with the special impacting filter (SIF) that can improve the energy utilization efficiency. However, unlike the TD detector, the SVM detector concentrates not only on reducing the BER of the detector, but also on providing accurate posterior probability estimates (PPEs), which can be used as soft-inputs of the LDPC decoder. The complexity of this detector is considered in this paper by using four features and simplifying the decision function. In addition, a bandwidth efficient transmission is analyzed with both SVM and TD detector. The SVM detector is more robust to sampling rate than TD detector. We find that the SVM is suitable for extended binary phase shift keying (EBPSK) signal detection and can provide accurate posterior probability for LDPC decoding.

No MeSH data available.


Related in: MedlinePlus