This article talks about basics of antenna radiation patterns, beamforming and smart antenna adaptive beamforming concepts. Antenna Radiation Patterns:. Radiation patterns define the directional property of the antenna and are usually represented in form of function or plot. This plot or function defines the variation of power radiated by an antenna in the direction away from the antenna. Script and its output is given below:. Fig 3-D Radiation pattern plot from octave script.
It can be seen from the above plot that radiation pattern consists of a beam like pattern. Further, It can be derived that the beam width of far field radiation pattern of an antenna in a particular direction is inversely proportional to the corresponding antenna dimension.
A tall antenna, for example, will tend to have narrow beam width. So, if we want to direct antenna radiation pattern towards any specific user we would need a tall antenna and a mechanism to steer it towards specific user. Obviously, this is impractical. Another relatively better approach is based on phased array antenna.
Idea is that when the same signal is transmitted from multiple antenna elements, they constructively or destructively add up. This makes a narrow beam at the output in the perpendicular direction of the antenna elements. Moreover, if a time delay is added to each of the signals that is sent out from each antenna element then the direction of the narrow beam changes.
Now, the gradient is obtained by differenfiating the square mm by the rccciver antenna weight. It i s noted that the number of interferences is I , and the number ofreceiver antennas is Reverse link is considered. The modulation method is BPSK, the data sampler are 10,bits. As shown in the figure, the BER performance of the proposed MlMO algorithm is much bcncr than a convention2 adaptive beamforming algorithm, using adaptive bcamforming only at the receiver pan.
The figure also shows that as the number ofanremar at the mnsmitter increases, the performance also improves. The multi-path Rayleigh Fading channel envimnment is exactly the same as in Fig. The figurc shows that the proposed MlMO algorithm has better performance than the conventional rccciver a m y antenna system. The perfarmanee also improves as the array antennas at the mnsmiffnand receiver have more elements. Liberti, Jr. Upper Saddle River, New Jersey , Shilpa, V.
Mats, P. So, this concept is very important to understand. As shown in the figure below:. Assuming that all the signals are zero mean, stationary processes then MxM Correlation matrix is composed of signal and noise samples at time k.
And, it can be defined as follows:. Correlation matrix has M eigen values and associated M eigen vectors. The largest eigenvalues correspond to the signals, while the rest are associated with the noise. So, this correlation matrix can be divided into two subspaces: signal subspace and noise sub-space.
Mx M-D noise subspace and MxD signal subspace. In that case M-D eigen vectors are corresponding to noise and D eigen vectors are corresponding to signal. The signal eigenvectors associated with the correlation matrix are the array weights that have beams in the directions of the signals. The noise eigenvectors associated with the correlation matrix are the array weights that have nulls in the directions of the signals. It is to note that if signals are not zero mean, stationary processes then correlation matrix is also known as a covariance matrix.
So, the heart of the DOA estimation is eigen analysis of covariance matrix. It can be seen above that by doing eigen analysis of Covariance matrix we are able to recover original data. This is very useful and main building block of DOA estimation algorithms. Now we have the basic understanding of smart antenna system.
Out of those four regions, we discussed about radiation pattern of far field region of an antenna. Also, we plotted a 3D radiation pattern of an antenna with a given radiation intensity.
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