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UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of links. Directional transmission is one way to efficiently use available wireless channels to achieve the desired range. While multiple-input multiple-output (MIMO) systems can digitally steer the beam through channel matrix manipulation without needing directional awareness, the power resources required for operating multiple radios on a UAV are often logistically challenging. An alternative approach to streamline resources is the use of phased arrays to achieve directionality in the analog domain, but this requires beam sweeping and results in search-time delay. The complexity and search time can increase with the dynamic mobility pattern of the UAVs in aerial networks. However, if the direction of the receiver is known at the transmitter, the search time can be significantly reduced. In this work, multi-antenna channels between two UAVs in A2A links are analyzed, and based on these findings, an efficient machine learning-based method for estimating the direction of a transmitting node using channel estimates of 4 antennas (2 × 2 MIMO) is proposed. The performance of the proposed method is validated and verified through in-field drone-to-drone measurements. Findings indicate that the proposed method can estimate the direction of the transmitter in the A2A link with 86% accuracy. Further, the proposed direction estimation method is deployable for UAV-based massive MIMO systems to select the directional beam without the need to sweep or search for optimal communication performance. [ABSTRACT FROM AUTHOR]

Eight inbred lines along with twenty-eight F1 hybrids were used to study the combining ability for 13 morphological traits in periwinkle (Catharanthus roseus). The parental lines were P1 (Magenta with white centre), P2 (Magenta), P3 (Cherry pink), P4 (Peach), P5 (Purple), P6 (Pink with dark pink centre), P7 (Light pink with white centre) and P8 (White with red spot). The mean squares due to combining ability exhibited highly significant general combining ability (GCA) and specific combining ability (SCA) effects for most of the traits. Parental lines P5 and P8 had desirable GCA effects for most of the traits. Regarding SCA, the majority of the significant effects were obtained in cross combinations involving poor or average lines crossed to good or poor lines. Additive gene action was found to be influencing plant height, leaf width, petiole length, days to flowering and corolla tube length. However, plant spread, number of branches per plant, leaf length, internodal length, flowering duration, number of flowers per plant, flower diameter and follicle length were found to be controlled by non-additive genetic components. Therefore, selection or hybridisation can be employed for the improvement of these traits on the basis of their genetic control. [ABSTRACT FROM AUTHOR]