Closed‐Loop transmit diversity (transmit beamforming) for mitigation of interference and multipath fading in wireless communication systems

The wireless communication channel suffers from many impairments such as the thermal noise often modeled as Additive White Gaussian Noise (AWGN), the path loss in power as the radio signal propagates, the shadowing due to the presence of fixed obstacles in the radio path, and the fading which combines the effects of multiple propagation paths and the rapid movement of mobile units reflectors. Deploying multiple  antennas at the transmitter has been shown to increase diversity and therefore improve signal quality with increased throughput. This paper proposes a transmit diversity scheme, where multiple transmit antennas are used at the transmitter. A feedback path is provided from the receiver to communicate the channel seen by the receiver to the transmitter (closed-loop). When closed]loop transmit diversity is applied, the symbol from each transmit antenna is multiplied with a complex number  corresponding to the inverse of the phase of the channel so as to ensure that the signals add constructively at the receiver. From this research it was found that sending the same information on multiple transmit antenna does not always provide diversity gain. However if the transmitted symbols are multiplied by a complex phase to ensure that the phases align at the receiver, there is diversity gain though the bit error rate performance seems to be slightly poorer than the maximal ratio combining case.Key words: Closed-loop, transmit diversity, transmit beamforming,  single-input single-output, multipath fading, mmaximal ratio combinin

Application of adaptive neuro-fuzzy inference system technique in design of rectangular microstrip patch antennas

The recent explosion in information technology and wireless communications has created many opportunities for enhancing the performance of existing signal transmission and processing systems and has provided a strong motivation for developing novel devices and systems. An indispensable element of any wireless communication system is the antenna. microstrip patch antenna (MPA) is well suited for wireless communication due to its light weight, low volume and low profile planar configuration which can be easily conformed to the host surface. In this paper, an adaptive neuro‐fuzzy inference systems (ANFIS) technique is used in design of MPA. This artificial Intelligence (AI) technique is used in determining the parameters used in the design of a rectangular microstrip patch antenna. The ANFIS has the advantages of expert knowledge of fuzzy inference system (FIS) and the learning capability of artificial neural network (ANN). By determining the patch dimensions and the feed point of a rectangular microstrip antenna, this paper shows that ANFIS produces good results that are in agreement with Antenna Magus simulation results.Key words: Artificial intelligence (AI), microstrip patch antennas (MPAs), adaptive neuro‐fuzzy inference system (ANFIS