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On Frequency domain Channel estimation using WARP v3 hardware platform
Abstract
Multi antenna wireless systems, which is generally termed as multi-input multi-output (MIMO) systems offers greater channel capacity and reliability compared to the single-input single-output (SISO) systems. It is important for the system to know the channel state information (CSI) for exploiting better performance of the communication system. The instantaneous CSI is acquired in an indoor scenario using the hardware setup. In this paper we use WARP v3 kit to extract the CSI with Orthogonal frequency division multiplexing (OFDM) and without OFDM.
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Future wireless communication system have to be designed to integrate features such as high data rates, high quality of service and multimedia in the existing communication framework. Increased demand in wireless communication system has led to demand for higher network capacity and performance. Higher bandwidth, optimized modulation offer practically limited potential to increase the spectral efficiency. Hence MIMO systems utilizes space multiplex by using array of antenna's for enhancing the efficiency at particular utilized bandwidth. MIMO use multiple inputs multiple outputs from single channel. These systems defined by spectral diversity and spatial multiplexing. The aim of this paper is to design and implement of channel estimation method and modulation technique for MIMO system. The design specifications are obtained using MATLAB. The RTL coding is carried for the design to be implemented on Xilinx FPGA.
In this work we have applied Adapive Boosting (AdaBoost) for channel estimation of Orthogonal Frequency Division Multiplexing (OFDM) systems. The result of the AdaBoost algorithm was compared with other algorithms such Least Square (LS), Best Linear Unbiased Estimator (BLUE) and Minimum Mean Square Error (MMSE).
This paper deals with design and implementation of an end to end MIMO system. Least Square (LS) channel estimation and QPSK techniques are used to implement 2×2 MIMO system.The design is modeled using MATLAB to derive the required specification. MIMO model is developed using Verilog and synthesized for area, power and speed. The HDL code for 2×2 MIMO system is simulated, synthesized and implemented on Virtex2Pro FPGA board and the results are validated against Matlab. The design is targeted on 7 and 30 million gate Virtex to compare its hardware performance. Based on the results obtained, hardware constraints for ASIC implementation is derived and implemented using Synopsys ASIC flow. Channel estimation and QPSK models have the maximum delay, the critical paths in these models are identified and redesigned to minimize the delay using buffer insertion technique. The results are verified for its functionality. With the modifications performed the frequency of opertion is increased to 13.388MHz from 7.27MHz.
Large MIMO systems, with tens to hundreds of antennas, are a promising emerging communication technology. This book provides a unique overview of this technology, covering the opportunities, engineering challenges, solutions and state of the art of large MIMO test beds. There is in-depth coverage of algorithms for large MIMO signal processing, based on meta-heuristics, belief propagation and Monte Carlo sampling techniques, and suited for large MIMO signal detection, precoding and LDPC code designs. The book also covers the training requirement and channel estimation approaches in large-scale point-to-point and multi-user MIMO systems; spatial modulation is also included. Issues like pilot contamination and base station cooperation in multi-cell operation are addressed. A detailed exposition of MIMO channel models, large MIMO channel sounding measurements in the past and present, and large MIMO test beds is also presented. An ideal resource for academic researchers, next generation wireless system designers and developers, and practitioners in wireless communications.
Real Time Channel Estimation For Mimo Systems
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Dsp Implementation of channel estimation algorithms for OFDM systems
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