Stacked patch antenna with wider impedance bandwidth and high interport isolation for 2.4 GHz IBFD transceiver

This paper reports a bi-port, wideband, parasitic-fed, single/shared patch antenna with enhanced interport isolation for 2.4 GHz in-band full duplex (IBFD) applications. The employed parasitic feeding provides comparatively the wider impedance bandwidth and better gain for the presented antenna. The improved self-interference cancellation (SIC) levels across the required bandwidth are obtained through differentially-driven receive (Rx) mode operation. The differential Rx operation performs effective cancellation of in-band self-interference (SI) through signal inversion mechanism to achieve the additional isolation on the top of the intrinsic isolation of polarization diversity. The validation model for the presented antenna features ≥88 dB peak isolation between the dual-polarized Tx and Rx ports. In addition, the measured Tx–Rx isolation for prototype is >70 dB across the 10 dB return loss bandwidth of 100 MHz (2.42–2.52 GHz). The measured gain for each mode is better than 7.0 dBi. The novelty of this work is that compared to previously reported designs, the presented antenna offers wider impedance bandwidth and improved SIC levels in addition to superior gain performance. To the best of our knowledge, this is the first single/shared patch antenna which provides better than 70 dB interport isolation across the 10 dB return loss bandwidth of 100 MHz along with 7.0 dBi gain for Tx/Rx modes.

Visualizing orientation-specific relaxation-diffusion features mapped onto orientation distribution functions estimated via nonparametric Monte Carlo MRI signal inversion

ABSTRACTDiffusion MRI techniques are widely used to study in vivo changes in the human brain connectome. However, to resolve and characterise white matter fibres in heterogeneous diffusion MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and restrictive constraints. We have recently introduced a 5D relaxation-diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times in order to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation-diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins in order to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work may be useful for investigating the microstructural properties along individual white matter pathways.