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Description: Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
Platform: |
Size: 134537 |
Author: zhang |
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Description: This script runs a simulation loop for either a linear or a DFE equalizer. It
uses the RLS algorithm to initially set the weights, then uses LMS thereafter to minimize execution time. It plots the equalized signal spectrum, then generates and plots BER results over a range of Eb/No values. It also fits a curve to the simulated BER points, and plots the burst error performance of the linear and DFE equalizers. The adaptive equalizer objects automatically retain their state between invocations of their \"equalize\" method.
Platform: |
Size: 2063 |
Author: 熊牧野 |
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Description: Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique. -Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
Platform: |
Size: 134144 |
Author: zhang |
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Description: 随着信噪比的增加，线性多用户检测器误码率性能的比较。-With the increase in signal to noise ratio, linear multi-user detector BER performance comparison.
Platform: |
Size: 1024 |
Author: 李金磊 |
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Description: 自适应MMSE多用户检测的误码率性能比较。-Adaptive MMSE multi-user detection BER performance comparison.
Platform: |
Size: 1024 |
Author: 李金磊 |
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Description: This script runs a simulation loop for either a linear or a DFE equalizer. It
uses the RLS algorithm to initially set the weights, then uses LMS thereafter to minimize execution time. It plots the equalized signal spectrum, then generates and plots BER results over a range of Eb/No values. It also fits a curve to the simulated BER points, and plots the burst error performance of the linear and DFE equalizers. The adaptive equalizer objects automatically retain their state between invocations of their "equalize" method.
Platform: |
Size: 2048 |
Author: 熊牧野 |
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Description: This demo shows the BER performance of linear, decision feedback (DFE), and maximum likelihood sequence estimation (MLSE) equalizers when operating in a static channel with a deep null. The MLSE equalizer is invoked first with perfect channel knowledge, then with an imperfect, although straightforward, channel estimation algorithm. The BER results are determined through Monte Carlo simulation. The demo shows how to use these equalizers seamlessly across multiple blocks of data, where equalizer state must be maintained between data blocks.
Platform: |
Size: 102400 |
Author: Lee |
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Description: Agilent Jitter Seminar
1. Jitter Basic and Jitter Components Introduction
2. Jitter Separate Algorithm, Measure, and
Accumulated Eye Pattern
3. EZJIT+ Features, Benefit, and Advance Jitter
Measurement
4. ISI Distortion, and Tx EQ, CTLE, DFE
5. Rx Tolerance Test and BER
Platform: |
Size: 5966848 |
Author: daniel |
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Description: 1. Jitter Basic and Jitter Components Introduction
2. Jitter Separate Algorithm, Measure, and
Accumulated Eye Pattern
3. EZJIT+ Features, Benefit, and Advance Jitter
Measurement
4. ISI Distortion, and Tx EQ, CTLE, DFE
5. Rx Tolerance Test and BER
Platform: |
Size: 5318656 |
Author: daniel |
Hits:
Description: 1. Jitter Basic and Jitter Components Introduction
2. Jitter Separate Algorithm, Measure, and
Accumulated Eye Pattern
3. EZJIT+ Features, Benefit, and Advance Jitter
Measurement
4. ISI Distortion, and Tx EQ, CTLE, DFE
5. Rx Tolerance Test and BER
Platform: |
Size: 8799232 |
Author: daniel |
Hits:
Description: 展示了线性、DFE、MLSE均衡器的信噪比性能，并通过蒙特卡罗模拟信道估计结果确定。-This demo shows the BER performance of linear, decision feedback (DFE), and maximum likelihood sequence estimation (MLSE) equalizers when operating in a static channel with a deep null. The MLSE equalizer is invoked first with perfect channel knowledge, then with an imperfect, although straightforward, channel estimation algorithm. The BER results are determined through Monte Carlo simulation. The demo shows how to use these equalizers seamlessly across multiple blocks of data, where equalizer state must be maintained between data blocks.
Platform: |
Size: 112640 |
Author: |
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