KEYWORDS: Modulation, Neural networks, Quadrature amplitude modulation, Neurons, Phase shift keying, Frequency shift keying, Amplitude shift keying, Signal to noise ratio, Digital modulation, Signal processing
The problem of automatic modulation classification is to identify the modulation type of a received signal from the
signal parameters. Modulation classification has both military and civilian applications and has been the subject
of intensive research for more than two decades. In this paper we use a hierarchical neural network in which the
first network identifies the modulation class while a second set of networks identify the constellation size (order)
of that modulation class. The set of features we use include normalized standard deviations of amplitude, phase
and frequency, as well as the fourth and sixth order cumulants of the signal samples. Identifying the constellation
size of quadrature amplitude modulation (QAM) has been particularly difficult in the past. In this paper we
introduce two new approaches for computing the features of a QAM signal. The first uses the concatenated in-
phase and quadrature components of the signal to compute the features. The second method maps the in-phase
and quadrature components to the first quadrant of the constellation by calculating the absolute value of each
separately. The mean of the resulting constellation points is then subtracted before calculating the features.
Simulation results are presented for classification of several digital modulation schemes including FSK, PSK,
ASK and QAM. Our results show that the proposed method significantly improves the classification error.
A new spectral direction of arrival (DOA) estimation algorithm is proposed that can
rapidly estimate the DOA of non-coherent as well as coherent incident signals. As such
the algorithm is effective for DOA estimation in multi-path environments. The proposed
method constructs a data model based on a Hermitian Toeplitz matrix whose rank is
related to the DOA of incoming signals and is not affected if the incoming sources are
highly correlated. The data is rearranged in such a way that extends the dimensionality of
the noise space. Consequently, the signal and noise spaces can be estimated more
accurately. The proposed method has several advantages over the well-known classical
subspace algorithms such as MUSIC and ESPRIT, as well as the Matrix Pencil (MP)
method. In particular, the proposed method is suitable for real-time applications since it
does not require multiple snapshots in order to estimate the DOA's. Moreover, no
forward/backward spatial smoothing of the covariance matrix is needed, resulting in
reduced computational complexity. Finally, the proposed method can estimate the DOA
of coherent sources. The simulation results verify that the proposed method outperforms
the MUSIC, ESPRIT and Matrix Pencil algorithms.
KEYWORDS: Genetic algorithms, Orthogonal frequency division multiplexing, Signal to noise ratio, Detection and tracking algorithms, Modulation, Genetics, Quadrature amplitude modulation, Chemical elements, Computing systems, Telecommunications
In this paper, a novel genetic algorithm application is proposed for adaptive power and subcarrier allocation in multi-user
Orthogonal Frequency Division Multiplexing (OFDM) systems. To test the application, a simple genetic algorithm
was implemented in MATLAB language. With the goal of minimizing the overall transmit power while ensuring the
fulfillment of each user's rate and bit error rate (BER) requirements, the proposed algorithm acquires the needed
allocation through genetic search. The simulations were tested for BER 0.1 to 0.00001, data rate of 256 bit per OFDM
block and chromosome length of 128. The results show that genetic algorithm outperforms the results in [3] in
subcarrier allocation. The convergence of GA model with 8 users and 128 subcarriers performs better in power
requirement compared to that in [4] but converges more slowly.
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