Description: We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers. Platform: |
Size: 1265450 |
Author:吴大亨 |
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Description: We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers. Platform: |
Size: 1265664 |
Author:吴大亨 |
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Description: 编写M函数的MATLAB仿真程序，实现M=2，4，8，16，32的M相PSK符号差错概率。-The preparation of M simulation program MATLAB function, M = 2,4,8,16,32 to achieve the M-phase PSK symbol error probability. Platform: |
Size: 12288 |
Author:沈友俊 |
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Description: Script for simulating QPSK(4-QAM) transmission and reception and compare the simulated and heoretical symbol error probability Platform: |
Size: 1024 |
Author:chungnando |
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Description: 一篇有关推导多进制相位调制在高斯信道条件下的符号错误概率，具有重要的参考意义-A derivation of M-ary phase modulation on the Gaussian channel symbol error probability under the condition has important reference value Platform: |
Size: 95232 |
Author:黄云 |
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Description: 在同一张图上仿真多幅度电平M电平PAM在M=2,4,8,16时的符号差错概率。-In the same graph simulation multi-level rate of M-level PAM at M = 2,4,8,16 when the symbol error probability. Platform: |
Size: 5120 |
Author:潘小丽 |
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Description: QAM调制下信道容量的计算，字符错误概率和符号错误概率曲线-QAM modulation in the calculation of channel capacity, character error rate and symbol error probability curve
Platform: |
Size: 10240 |
Author:fgw |
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Description: Program computes the generator polynomial of a RS code. Also performs
encoding and decoding of the RS code or a shortened RS code. Compile
using one of the following options:
In the commands below, using the [-DNO_PRINT] option will ensure that
the program runs without printing a lot of detail that is generally
unnecessary. If the program is compiled without using the option a lot
of detail will be printed. Some other options, for example changing the
probability of symbol error are available by directly editing the program
itself.
-Program computes the generator polynomial of a RS code. Also performs
encoding and decoding of the RS code or a shortened RS code. Compile
using one of the following options:
In the commands below, using the [-DNO_PRINT] option will ensure that
the program runs without printing a lot of detail that is generally
unnecessary. If the program is compiled without using the option a lot
of detail will be printed. Some other options, for example changing the
probability of symbol error are available by directly editing the program
itself.
Platform: |
Size: 11264 |
Author:kdcroger |
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Description: 运行一个Matlab程序，完成一个M＝4的PAM通信系统的仿真。仿真对10000个符号（2万个比特）进行。测量在噪声方差为0,0.1,1.0和2.0时的符号差错概率。通过低通滤波器。画出理论误码率和由Monte Carlo仿真测得的误码率，并比较这些结果。(平均符号能量为1)-Running a Matlab program, complete a M = 4 PAM communication system simulation. The simulation of 10000 symbols (20000 bits). Measurement in the noise variance is 0,0.1, 1.0 and 2.0 when the symbol error probability. Through the low pass filter. Draw the theoretical error rate and Monte Carlo simulation by measured error rate, and compare these results. (average symbol energy for 1)
Platform: |
Size: 1024 |
Author:陈巧云 |
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Description: This program simulate a multi-antenna multi-relay dual-hop amplify
and forward relaying system over Nakagami-m fading channels.
The output of this function is the symbol error
probability.- This program simulate a multi-antenna multi-relay dual-hop amplify
and forward relaying system over Nakagami-m fading channels.
The output of this function is the symbol error
probability. Platform: |
Size: 1024 |
Author:ehsan |
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Description: This program simulate a two-way interference-limited dual-hop amplify
and forward relaying system over Nakagami-m fading channels.
The output of this function is the outage probability and symbol error probability.- This program simulate a two-way interference-limited dual-hop amplify
and forward relaying system over Nakagami-m fading channels.
The output of this function is the outage probability and symbol error probability. Platform: |
Size: 1024 |
Author:ehsan |
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Description: In this paper, we first propose an optimum relay ordering algorithm for the multi-branch multi-hop cooperative diversity networks. This optimum algorithm has a high complexity that makes it hard to implement. Therefore, a suboptimum relay ordering algorithm, which considerably reduces the complexity, is then developed. Furthermore, for a cooperative network with two relays, we analytically evaluate the performance of the suboptimum algorithm by using an approximate end-toend signal-to-noise ratio expression. Specifically, an approximate probability of wrong selection and an approximate expression of the symbol error rate are derived. The analysis and the numerical results demonstrate that the suboptimum algorithm performs very well as the optimum one at a much lower complexity. Platform: |
Size: 229376 |
Author:karim |
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Description: 这是一份关于MIMO无线系统设计的一份分析资料，另外在最后还附带了程序。很不错的！-In this report, we will be mostly talking about QAM. So for completeness, in Fig. 2, we plot MATLAB
simulation results for the probability of symbol error of 16-QAM. As it can be seen, the performance
degradation is as severe as in the BPSK case. Platform: |
Size: 138240 |
Author:SB |
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Description: mimo无线通信Alamouti方案，重复码编码方案，迫零接收及匹配接收方案实现的matlab源程序
-Implement a complete MIMO system with different combinations of the following components:
(a) Alamouti, repetition code, and V-BLAST (at the transmitter)
(b) zero-forcing and MMSE (at the receiver)
(c) nr 1 and nr 2
and simulate the symbol error probability as a function of the channel SNR P .
The symbol error probability versus SNR plots should be made such that symbol error probability reaches 10 3.
Platform: |
Size: 10240 |
Author:wanerying |
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Description: 使用Matlab编程，对教材74页例3.3.1进行编程仿真。其中，正电压A的值、噪声方差值、每个码元周期内的采样点数N自行设定（可设置为可调的变量）。噪声值可使用Matlab中产生高斯随机数的函数来进行仿真。贝叶斯检测判决式中，先验概率P(H1)=P(H0)，判断错误的代价因子设为1，判断正确的代价因子设为0.
按照上述参数的设定进行仿真，实现对仿真数据的贝叶斯检测判决；循环生成仿真数据，并进行判决结果的统计，即记录判决正确的次数，估计正确判决的概率。(The use of Matlab programming, teaching materials 74 pages of 3.3.1 programming simulation. Among them, the value of positive voltage A, the variance of noise, the number of sampling points in each symbol cycle (N) can be set by itself (adjustable variable). Noise values can be simulated using the function of generating Gauss random numbers in Matlab. In the Bias detection decision, the prior probability P (H1) =P (H0), the factor of error judgment is set to 1, and the correct cost factor is set to 0.
According to the simulation parameters and realize the judgment of Bayesian detection simulation data; generating cycle simulation data, statistics and decision, which records the correct number of judgment, the estimated probability of correct decision.) Platform: |
Size: 1024 |
Author:妮妮111
|
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Description: The digital simulation system using MATLAB to illustrate the
Quadrature Amplitude Modulation (QAM). QAM is a modulation scheme to convey two digital bit
streams which can be converted to symbol signals by modulating the amplitudes and the phase of two
carrier waves. First, we transmit a signal composed of two different 1024 bit streams and modulate
them using QAM. Next, at the receiver, we demodulate them using an orthogornality of two carriers
and matched filters. We also conduct the error analysis of two sampled signals in order to observe how
the probability of making bit errors changes depending on the inverse of Power Spectral Density (PSD)
of Additive White Gaussian Noise (AWGN). Platform: |
Size: 713728 |
Author:khang
|
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Search - probability of symbol error