PROJECT TITLE :

Robust Interference Exploitation-Based Precoding Scheme With Quantized CSIT

ABSTRACT:

We tend to propose a replacement precoding theme that may exploit interference constructively when a base station (BS) is given quantized channel state data (CSI) in multiuser multiple-input single-output (MU-MISO) systems. Within the proposed theme, interference is decomposed into predictable interference, manipulated constructively by a BS, and unpredictable interference, caused by the quantization error. To cut back performance loss by unpredictable interference, we have a tendency to first derive the higher sure of the unpredictable interference. Then, the BS aligns the predictable interference so that its power is a lot of bigger than the derived upper sure. During this process, to intensify the received signal power, the BS simultaneously aligns the predictable interference therefore that it's constructively superimposed with the required signal. Simulation results show that the proposed theme achieves improved image error rates (SERs) compared with existing precoding schemes, especially with a high modulation level.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Accurate and Robust Video Saliency Detection via Self-Paced Diffusion ABSTRACT: In order to estimate video saliency in the short term, traditional video saliency detection algorithms usually follow the common
PROJECT TITLE : Robust Lane Detection from Continuous Driving ScenesUsing Deep Neural Networks ABSTRACT: For autonomous vehicles and sophisticated driver assistance systems, lane recognition in driving scenes is a critical element.
PROJECT TITLE : Robust Unsupervised Multi-view Feature Learning with Dynamic Graph ABSTRACT: By modeling the affinity associations with a graph to lower the dimension, graph-based multi-view feature learning algorithms learn a
PROJECT TITLE : A Spatially Constrained Probabilistic Model for Robust Image Segmentation ABSTRACT: In probabilistic model based segmentation, the hidden Markov random field (HMRF) is used to describe the class label distribution
PROJECT TITLE : An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics ABSTRACT: One of the most important preprocessing steps for high-level tasks in the field of image analysis and computer vision is

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry