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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.

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The BER performance of SVM detector with difference bit duration. We represent the threshold decision for bit duration N = 5 with ▼) and N = 20 with ○, respectively; the SVM for N = 4 with *, N = 5 with ◊ and N = 20 with ✩ , respectively.
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f8-sensors-13-04327: The BER performance of SVM detector with difference bit duration. We represent the threshold decision for bit duration N = 5 with ▼) and N = 20 with ○, respectively; the SVM for N = 4 with *, N = 5 with ◊ and N = 20 with ✩ , respectively.

Mentions: The BER performance of the EBPSK system will be diverse with different bit durations and sampling rates. To prove the effectiveness of the proposed method, various simulations were conducted. In Figure 8 we compare the BER performance of the SVM detector with TD for different bit durations N. The SVM-K2N5, SVM-K2N4, SVM-K2N20 and TD-K2N20 BER plots in Figure 8 perform significantly better than TD-K2N5. Compared to SVM-K2N5, the performance of SVM-K2N4 deteriorated greatly, and the former performed slightly worse than the SVM-K2N20. On the one hand, we should use N > 4 in order to get the desired performance; on the other hand, the shorter the bit duration N the higher the data rate. Thus, N = 5 is the best choice for our system with the SVM technique. Unless specified otherwise, all simulations assume N = 5. Moreover, we can appreciate that the BER performance of SVM-K2N5 is even better than that of TD-K2N20. This means that the performance of conventional TD is greatly affected in the case of short bit duration N. In this sense, SVM detector with higher bit rate outperforms the TD.


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

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

The BER performance of SVM detector with difference bit duration. We represent the threshold decision for bit duration N = 5 with ▼) and N = 20 with ○, respectively; the SVM for N = 4 with *, N = 5 with ◊ and N = 20 with ✩ , respectively.
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-13-04327: The BER performance of SVM detector with difference bit duration. We represent the threshold decision for bit duration N = 5 with ▼) and N = 20 with ○, respectively; the SVM for N = 4 with *, N = 5 with ◊ and N = 20 with ✩ , respectively.
Mentions: The BER performance of the EBPSK system will be diverse with different bit durations and sampling rates. To prove the effectiveness of the proposed method, various simulations were conducted. In Figure 8 we compare the BER performance of the SVM detector with TD for different bit durations N. The SVM-K2N5, SVM-K2N4, SVM-K2N20 and TD-K2N20 BER plots in Figure 8 perform significantly better than TD-K2N5. Compared to SVM-K2N5, the performance of SVM-K2N4 deteriorated greatly, and the former performed slightly worse than the SVM-K2N20. On the one hand, we should use N > 4 in order to get the desired performance; on the other hand, the shorter the bit duration N the higher the data rate. Thus, N = 5 is the best choice for our system with the SVM technique. Unless specified otherwise, all simulations assume N = 5. Moreover, we can appreciate that the BER performance of SVM-K2N5 is even better than that of TD-K2N20. This means that the performance of conventional TD is greatly affected in the case of short bit duration N. In this sense, SVM detector with higher bit rate outperforms the 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