In this chapter, we describe a compact array antenna. Beamforming is achieved by tuning the load reactances at parasitic elements surrounding the active central element. The existing beam forming algorithms for this reactively controlled parasitic array antennas require long training time. In comparison with these algorithms, a faster beamforming algorithm, based on simultaneous perturbation stochastic approximation (SPSA) theory with a maximum cross-correlation coefficient (MCCC) criterion, is proposed in this chapter. The simulation results validate the algorithm. In an environment where the signal-to-interference ratio (SIR) is 0 dB, the algorithm converges within 50 iterations and achieves an output SINR of 10 dB. With the fast beamforming ability and its low power consumption attribute, the antenna makes the mass deployment of smart antenna technologies practical. To give a comparison of the beamforming algorithm with one of the standard beamforming algorithms for a digital beamforming (DBF) antenna array, we compare the proposed algorithm with the least mean square (LMS) beamforming algorithm. Since the parasitic array antenna is in nature an analog antenna, it cannot suppress correlated interference. Here, we assume that the interferences are uncorrelated.
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The evolution of wireless communications systems requires new technologies to support better quality communications, new services and applications. Smart antennas have become a hot topic of research. With a smart antenna directive beam patterns can be steered toward the desired signal and deep nulls can be formed toward the interference, thus spatial filtering is realized. This brings the benefits such as lower power transmission, higher spectrum efficiency, better link quality and higher system capacity (Godara, 1997a; Winters, 1998; Tsoulos, 1999; Boukalov, 2000; Jana, 2000; Friodigh, 2001; Ogawa, 2001; Bhobe, 2001; Soni, 2002; Blogh, 2002; Bellofiore, 2002a; Bellofiore, 2002b; Diggavi, 2004).
Various beamforming and direction of arrival (DOA) estimation algorithms have been designed (Widrow, 1967; Van-Veen, 1988; Litva, 1996; Godara, 1997b; Anderson, 1999; Lehne, 1999; Boukalov, 2000; Janaswamy, 2001; Rappaport, 2002; Blogh, 2002; Bellofiore, 2002b). The simulation and experiments carried out by many researchers have shown the abilities of these algorithms (Anderson, 1996a; Anderson, 1996b, Winters, 1997; Tsoulos, 1997; Boukalov, 2000). Most of these algorithms are designed based on the digital beamforming (DBF) antenna arrays. Signals received by individual antenna elements are down-converted into baseband signals. These signals are digitized and fed into digital signal processing (DSP) chip where the algorithms reside in. However, radio-frequency (RF) circuit branches connected to the array elements, analog-to-digital converters (ADCs) and the baseband DSP chip consume a considerable amount of dc power. Furthermore, each channel connected to the array sensor has the same structure, so the cost of fabrication increases with the number of array elements (Ohira, 2000; Boukalov, 2000; Thiel, 2001). Thanks to the recent development of GaAs monolithic microwave integrated circuit (MMIC) technologies, the beamformer could be integrated into a single chip at the RF front end such as MBF (Ohira, 1997), instead of the baseband. The advantages are the reduced quantization errors and the increased dynamic range. However, their costs of fabrication still limit the range of implementations. All these factors make DBF and microwave beamforming (MBF) antennas unsuitable for low power consumption and low cost systems and thus hinder the mass applications of the smart antenna technologies. For example, it could be too costly to equip DBF antenna arrays at battery powered lap-tops or mobile computing terminals within a wireless network.