CMA Adaptive Array Antenna Using Transversal Filters for Spatial and Temporal Adaptability in Mobile Communications
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The CMA (Constant Modulus Algorithm) was developed for the adaptive receiving systems to capture the desired signal having the constant modulus property such as FM, PSK and FSK signals. The CMA is an algorithm suitable for mobile communications because it has the great advantage of not requiring a reference signal as the LMS aluorithm does.
The CMA adaptive transversal filter can equalize channel distortions affecting the data signal received in multipath environments. However, it cannot eliminate the influence of the co-channel interferences that are not correlated with the desired signal. On the other hand, the CMA adaptive array antenna can suppress the co-channel interferences as well as the multipath signals. Therefore, this paper shows the performance of the CMA adaptive array antenna using the transversal filters (tapped delay lines) in the presence of the multipath signals and co-channel interference. It can be regarded as the spatial and temporal adaptive signal processor.
In this paper, computer simulation is carried out using an antenna array equipped with tapped delay lines for the antenna weights. A π/4-shifted QPSK signal is generated which is transmitted over several multipath channels. Furthermore, another signal of the same modulation type is generated for the co-channel interference.
For optimization of the CMA adaptive system, the steepest descent method and Marquardt method are utilized. The former has mainly been used for the CMA because the cost function is nonlinear with respect to the tap weights. However, the slow convergence of the algorithm has often limited its application in mobile communications where signals must be quickly captured. The latter, on the other hand, is one of the nonlinear least squares algorithms to attain its rapidly-converging and well-conditioned adaptation.
The simulation results demonstrated that the spatial and temporal adaptive system can achieve high quality of communications with low BERs. Also, it is shown that the Marquardt method can contribute to reducing the convergence time.
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