**1. Real Time Implementation of Pilot Aided Channel Estimation for SISO OFDM Systems**

Multiple-input multiple-output Orthogonal frequency division multiplexing (MIMO-OFDM) has been considered as a strong candidate for the B3G (Beyond 3G) wireless communication systems, due to the high data rate wireless transmission but all advantages possessed by MIMO- OFDM systems rely on the precise knowledge of channel state information (CSI). Channel estimation is a signal processing operation used to estimate the channel parameters and thereby to model the transfer function (or impulse response) of communication cannel. Channel estimation plays most crucial role in coherent detection of information symbols in a digital communication link. The aim of this project is to simulate the pilot aided channel estimation for SISO-OFDM system using least square (LS) criterion on Matlab using Simulink model block set and to implement it in a real time on a TI’s DSK TMS320C6713 kit.

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**2. Real Time Implementation of Multiband-OFDM Baseband Transceiver**

Ultra‐wideband (UWB) is a wireless transmission standard that will soon revolutionize consumer electronics. UWB is interesting because of its inherent low power consumption, high data rates of up to 480 Mbps, and large spatial capacity. Furthermore, the power spectral density is low enough to prevent interference with other wireless services. Multi-band OFDM is a leading technology operating in UWB. The main goal of the project is to Implement Baseband transceiver of multiband-OFDM .In this project MB-OFDM Physical layer proposal for IEEE 802.15 3a is discussed, followed by implementation, and results. Texas Instruments DSP platforms were used in conjunction with Simulink and Code Composter Studio to implement t he scaled‐down baseband System.

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**3. Implementation of Adaptive Line Enhancer (ALE) with LMS algorithm/Normalized LMS**

The ALE is a special form of adaptive noise canceler that is designed to suppress the wide-band noise component of the input, while pass-ing the narrow-band signal component with little attenuation. An ALE consists of the interconnection of a delay element and a linear predictor, as is illustrated in the block diagram in Fig. below. The input signal d(n) is formed of the desired signal s(n) which is periodic, i.e. narrow-banded, and the disturbing noise v(n) which is colored, i.e., wide-banded. The predictor output ^ d(n) is subtracted from the input signal d(n) to pro-duce the estimation error e(n). This estimation error is, in turn, used to adaptively control the predictor.

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**4. System Identification Using LMS Algorithm/Normalized LMS**

System identification is one of the most interesting applications for adaptive filters, especially for the Least Mean Square algorithm, due to its robustness and calculus simplicity. Based on the error signal, the filter’s coefficients are updated and corrected, in order to adapt, so the output signal has the same values as the reference signal. The application enables remarkable developments and research, creating an opportunity for automation and prediction. Figure below presents a block diagram of system identification application using adaptive filtering. The objective is to change (adapt) the coefficients of a filter W (which can be a FIR or an IIR one), to match as closely as possible the response of an unknown system H. The unknown system and the adapting filter process the same input signal x[n] and have as outputs: d[n] (also referred to as the desired signal) and y[n]. The filter W is adapted using the least mean-square algorithm, which is the most widely used adaptive filtering algorithm.

For more details mail to us at matlabprojects.in@gmail.com