Phased array smart antennas are becoming the primary choice for wireless communication solutions. Along with the increased usage of this technology, the problems related to mutual electromagnetic coupling between elements become evident. On arrays consisting of only one active element and multiple parasite elements (which steer the radiation pattern), as is the case of the ESPAR (Electronically Steerable Passive Array Radiator) antenna, it is desirable to have maximum mutual coupling between each parasite element and the active one. However, the mutual coupling between parasite elements decreases the overall efficiency of the adaptive algorithm used to optimize the radiating pattern of the array. This work explores the use of metamaterial as a near-field electromagnetic insulator. A novel ESPAR antenna structure, composed by metamaterial between parasite elements, has been proposed. The metamaterial structure transforms the conventional behavior of electromagnetic waves. The wave-metamaterial interaction behaves as a high impedance area, which attenuates electromagnetic waves that move across it. The results show an effective minimization of the mutual coupling between the passive elements, with no significant impact to the mutual coupling between the active element and each passive one.Index Terms -Metamaterial, beamforming, antenna, phased array 1536-1225 (c)

Mobile communications, not infrequently, are disrupted by multipath propagation in the wireless channel. In this context, this paper proposes a new blind concurrent equalization approach that combines a Phase Transmittance Radial Basis Function Neural Network (PTRBFNN) and the classic Constant Modulus Algorithm (CMA) in a concurrent architecture, with a Fuzzy Controller (FC) responsible for adapting the PTRBFNN and CMA step sizes. Differently from the Neural Network (NN) based equalizers present in literature, the proposed Fuzzy Controller Concurrent Neural Network Equalizer (FC-CNNE) is a completely self-taught concurrent architecture that does not need any training. The Fuzzy Controller inputs are based on the estimated mean squared error of the equalization process and on its variation in time. The proposed solution has been evaluated over standard multipath VHF/UHF channels defined by the International Telecommunication Union. Results show that the FC-CNNE is able to achieve lower residual steady-state MSE value and/or faster convergence rate and consequently lower Bit Error Rate (BER) when compared to Constant Modulus Algorithm-Phase Transmittance Radial Basis Function Neural Network (CMA-PTRBFNN) equalizer.

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