Link adaptation plays an essential role in optimizing the performance of wireless communications systems. With the emerging services, link adaptation needs to operate with stringent reliability and latency requirements. This paper introduces soft-ACKINACK based link adaptation, which is an adaptation of conventional OLLA to the high reliability and low latency requirements by a range of emerging 5G/6G NR applications, such as URLLC/IIoT, AV/AR, cloud gaming or V2X. We study the limitations of conventional OLLA for these scenarios, and introduce an enhanced OLLA algorithm using soft-ACK/NACK feedback from the UE. We analyse the possible methods to derive soft-ACK/NACK reports, and propose using flipped bit counts based on accuracy and feasibility. The performance gains of soft-OLLA are assessed in performance simulations.

5G NR Rel-15 and Rel-16 delivered good latency and reliability features to enable URLLC and pave the way to more stringent 5G applications like AR/VR and Cloud gaming. However, further enhancement is required to improve the system capacity to support these high data rate demanding applications while meeting the latency and reliability requirements. Carrier aggregation is primarily designed to accommodate higher data rate. However its operation could be further optimized for better latency and hence better capacity particularly on TDD bands.

In this paper, we propose a novel framework to estimate the parameters of a diffeomorphism that aligns a known shape and its distorted observation. Classical registration methods first establish correspondences between the shapes and then compute the transformation parameters from these landmarks. Herein, we trace back the problem to the solution of a system of nonlinear equations which directly gives the parameters of the aligning transformation. The proposed method provides a generic framework to recover any diffeomorphic deformation without established correspondences. It is easy to implement, not sensitive to the strength of the deformation, and robust against segmentation errors. The method has been applied to several commonly used transformation models. The performance of the proposed framework has been demonstrated on large synthetic data sets as well as in the context of various applications.

Images taken from different views of a planar object are related by planar homography. Recovering the parameters of such transformations is a fundamental problem in computer vision with various applications. This paper proposes a novel method to estimate the parameters of a homography that aligns two binary images. It is obtained by solving a system of nonlinear equations generated by integrating linearly independent functions over the domains determined by the shapes. The advantage of the proposed solution is that it is easy to implement, less sensitive to the strength of the deformation, works without established correspondences and robust against segmentation errors. The method has been tested on synthetic as well as on real images and its efficiency has been demonstrated in the context of two different applications: alignment of hip prosthesis X-ray images and matching of traffic signs.

A novel approach is proposed to estimate the parameters of a diffeomorphism that aligns two binary images. Classical approaches usually define a cost function based on a similarity metric and then find the solution via optimization. Herein, we trace back the problem to the solution of a system of nonlinear equations which directly provides the parameters of the aligning transformation. The proposed method works without any time consuming optimization step or established correspondences. The advantage of our algorithm is that it is easy to implement, less sensitive to the strength of the deformation, and robust against segmentation errors. The efficiency of the proposed approach has been demonstrated on a large synthetic dataset as well as in the context of an industrial application.