Abstract-The effect of the companding process on QAM signals
has been under investigation for the past several years. The
compander, included in the PCM telephone network to improve
voice performance, has an unusual affect on digital QAM data
signals which are transmitted over the same channel. The quantization
noise, generated by the companding process which is multiplicative
(and asymmetric), degrades the detectability performance
of the outermost points of the QAM constellation more
than that of the inner points.
The combined effect of the companding noise and the inherent
white gaussian noise of the system, leads us to a re-examination of
signal constellation design.
In this paper we investigate the detectability performance of a
number of candidates for signal constellations including, a typical
rectangular QAM constellation, the same constellation with the
addition of a smear-desmear operation, and two new improved
QAM constellation designs with two-dimensional warpi
ofdm信道特性
Channel transmission simulator
Channel transmission simulator
％
％ inputs:
％ sig2 - noise variance
％ Mt - number of Tx antennas
％ Mr - number of Rx antennas
％ x - vector of complex input symbols (for MIMO, this is a matrix, where each column
％ is the value of the antenna outputs at a single time instance)
％ H - frequency selective channel - represented in block-Toeplitz form for MIMO transmission
％ N - number of symbols transmitted in OFDM frame
％
％ outputs:
％ y - vector of channel outputs (matrix for MIMO again, just like x matrix)
％ create noise vector sequence (each row is a different antenna, each column is a
％ different time index) note: noise is spatially and temporally white
he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization.
The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
This function calculates Akaike s final prediction error
％ estimate of the average generalization error.
％
％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the
％ final prediction error estimate (fpe), the effective number of
％ weights in the network if the network has been trained with
％ weight decay, an estimate of the noise variance, and the Gauss-Newton
％ Hessian.
％
This function calculates Akaike s final prediction error
％ estimate of the average generalization error for network
％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive
％ counterparts.
％
％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat)
％ produces the final prediction error estimate (fpe), the effective number
％ of weights in the network if it has been trained with weight decay,
％ an estimate of the noise variance, and the Gauss-Newton Hessian.
％
The exercise should be finished in English.
2. According to Prof. Zhang s requirement, this exercise mainly focuses on the BER performance of some wireless communication system using specific coding and modulation type through the AWGN channel. Signal-to-noise ration (SNR) varies from 5dB to 20dB.
The software is capable to simulate space time code [1] for QPSK modulation using different number of state. Examples of generator matrix up to 256 stetes are provided. Variable signal to noise ratio (SNR) might be applied to produce bit error rate (BER) or frame error rate (FER) curves.
A MATLAB GUI platform for realizing the radiation pattern of narrowband beamformer with random array geometry. User can specify the array geometry, directions of incoming signals, noise power, and the type of beamformer. Useful for gaining insight about collaborative beamforming in sensor networks and random arrays. You need both randomarray.fig and randomarray.m to work.
