PROJECT TITLE :

Limited Feedback in Single and Multi-User MIMO Systems With Finite-Bit ADCs - 2018

ABSTRACT:

Communication systems with low-resolution analog-to-digital-converters (ADCs) can exploit channel state info at the transmitter and receiver. This Project presents codebook styles and performance analyses for restricted feedback MIMO systems with finite-bit ADCs. A purpose-to-point single-user channel is firstly thought-about. When the received signal is sliced by 1-bit ADCs, the absolute phase at the receiver is vital to align the section of the received signals. A new codebook design for beamforming, which separately quantizes the channel direction and therefore the residual phase, is therefore proposed. For the multi-bit case where the optimal transmission technique is unknown, suboptimal Gaussian signaling and eigenvector beamforming is assumed to get a lower sure of the achievable rate. It is found that to limit the speed loss, more feedback bits are required within the medium SNR regime than the high and low SNR regimes, which is kind of completely different from the standard infinite-bit ADC case. Second, a multi-user system where a multiple-antenna transmitter sends signals to multiple single-antenna receivers with finite-bit ADCs is taken into account. Based mostly on the derived performance loss because of finite-bit ADCs and finite-bit CSI feedback, the quantity of bits per feedback ought to increase linearly with the ADC resolution so as to restrict the rate loss.


Did you like this research project?

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


PROJECT TITLE : Representation Learning from Limited Educational Data with Crowdsourced Labels ABSTRACT: It has been demonstrated that representation learning plays a significant part in the unprecedented success of machine learning
PROJECT TITLE : Representation Learning from Limited Educational Data with Crowdsourced Labels ABSTRACT: It has been demonstrated that representation learning plays a significant part in the unprecedented success of machine learning
PROJECT TITLE : Analyzing Asr Pretraining For Low-Resource Speech-To-Text Translation ABSTRACT: Previous research has demonstrated that pretraining an end-to-end model on automatic speech recognition (ASR) data from a high-resource
PROJECT TITLE :Maximising revenue via optimal control of a concentrating solar thermal power plant with limited storage capacityABSTRACT:Concentrating solar thermal power plants with thermal energy storage could be a potential
PROJECT TITLE :Resource Allocation for Cost Minimization in Limited Feedback MU-MIMO Systems With Delay GuaranteeABSTRACT:During this paper, we tend to design a resource allocation framework for the delay-sensitive multiuser multiple-input

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

Project Enquiry