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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.


Related in: MedlinePlus

The BER performance comparison of the SVM with threshold decision by different sampling rate. We use dashed-dotted lines for the SVM BER, solid lines for the threshold decision BER. We represent the BER for fs = 4fc with ◊, fs = 6fc with *, and fs = 10fc with ○, respectively.
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f9-sensors-13-04327: The BER performance comparison of the SVM with threshold decision by different sampling rate. We use dashed-dotted lines for the SVM BER, solid lines for the threshold decision BER. We represent the BER for fs = 4fc with ◊, fs = 6fc with *, and fs = 10fc with ○, respectively.

Mentions: The BER performance comparison of the SVM with TD by different sampling rates is plotted in Figure 9. Compared to the TD, the SVM method can upgrade more than 8 dB, 7 dB and 5 dB for fs = 4fc, fs = 6fc and fs = 10fc at BER = 10−3, respectively. This means the performance of the SVM detector improved significantly while the sampling rate is low, and it is more robust to sampling rate than TD.


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

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

The BER performance comparison of the SVM with threshold decision by different sampling rate. We use dashed-dotted lines for the SVM BER, solid lines for the threshold decision BER. We represent the BER for fs = 4fc with ◊, fs = 6fc with *, and fs = 10fc with ○, respectively.
© Copyright Policy
Related In: Results  -  Collection

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

f9-sensors-13-04327: The BER performance comparison of the SVM with threshold decision by different sampling rate. We use dashed-dotted lines for the SVM BER, solid lines for the threshold decision BER. We represent the BER for fs = 4fc with ◊, fs = 6fc with *, and fs = 10fc with ○, respectively.
Mentions: The BER performance comparison of the SVM with TD by different sampling rates is plotted in Figure 9. Compared to the TD, the SVM method can upgrade more than 8 dB, 7 dB and 5 dB for fs = 4fc, fs = 6fc and fs = 10fc at BER = 10−3, respectively. This means the performance of the SVM detector improved significantly while the sampling rate is low, and it is more robust to sampling rate than TD.

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