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摘要:
为有效解决STBC-OFDM信号盲识别过程中存在的低信噪比适应能力弱等问题，在OFDM块大小已知的前提下，提出了一种在时域上构造特征序列的识别算法。该算法推导了空时分组码接收信号的时域特性，以及四阶特征向量，并构造特征序列，通过检测特征序列达到识别4种STBC-OFDM信号的目的。推导和仿真结果表明：所提算法无需信道、噪声、调制方式和OFDM块起始位置等先验信息，在较低信噪比条件下也具有良好的识别性能，且对频偏、多普勒频移和时延的鲁棒性能好，计算量较低，具有较高的实用价值。
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关键词:
- 信号与信息处理 /
- 信号盲识别 /
- 空时分组码(STBC) /
- 四阶特征向量 /
- 时域特征序列
Abstract:In order to effectively solve the problem of low SNR adaptability in the blind identification process of STBC-OFDM signals, an identification algorithm for constructing feature sequences in time domain is proposed under the premise of known OFDM block size. This algorithm deduces the time-domain features and the fourth-order eigenvectors of the STBC received signals and constructs the feature sequences. By detecting the feature sequences, the four STBC-OFDM signals are identified. The derivation and simulation results show that this algorithm does not need priori information such as channel, noise, modulation mode and starting position of OFDM block, and has good recognition performance under low SNR conditions. It has good robustness to frequency offset, Doppler frequency shift and time delay, requires low calculation amount, and thus has high practical value.
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图 2 STBC-OFDM信号的四阶特征向量序列
Figure 2. Fourth-order eigenvector sequence of STBC-OFDM signal
图 4 四种STBC-OFDM信号的正确识别概率
Figure 4. Correct recognition probability of four STBC-OFDM signals
图 5 算法性能与OFDM子载波数的关系
Figure 5. Relationship between algorithm performance and OFDM subcarrier number
图 6 算法性能与OFDM块数量的关系
Figure 6. Relationship between algorithm performance and number of OFDM blocks
图 7 算法性能与循环前缀数的关系
Figure 7. Relationship between algorithm performance and cyclic prefixes
图 9 算法性能与频偏的关系
Figure 9. Relationship between algorithm performance and frequency offset
图 10 算法性能与多普勒频移的关系
Figure 10. Relationship between algorithm performance and Doppler frequency
图 11 算法性能与调制方式的关系
Figure 11. Relationship between algorithm performance and modulation mode
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