Signal Processing

Signal Processing (MATLAB)
1. Design and implementation of OFDM transceiver system using M-PSK encoding techniques

Abstract: Multi-carrier or orthogonal frequency division multiplexing (OFDM) has become the chosen modulation technique for wireless communications because it provides a high data rate wireless transmission. Some examples of applications using OFDM include ETSI BRAN in Europe, IEEE802.11 in United States, and ARIB MMAC in Japan .Therefore, many research centers in the world have specialized teams working in the optimization of OFDM for countless applications. This paper is to demonstrate the concept and feasibility of an OFDM system, and investigate how its performance is changed by varying some of its major parameters by using a MATLAB program to simulate an OFDM system. From the process of this development, the mechanism of an OFDM system can be studied; and with a completed MATLAB program, the characteristics of an OFDM system can be explored.

Conclusion

An OFDM system is successfully simulated using MATLAB; in this work, all major components, concept and feasibility of an OFDM system are covered.  It was noted that for some combinations of OFDM parameters, the simulation may fail for some trials but may succeed for repeated trails with the same parameters. It is because the random noise generated on every trial differs. The more complex OFDM system is, the higher IFFT size it has, thus a higher number of carriers can be used, and higher data. The higher order of PSK leads to larger symbol size, thus less number of symbols needed to be transmitted, and higher data rate is achieved. But this result in a higher BER, and received phases have higher chances to be decoded incorrectly. Future work includes adding ability to accept input source data in a word size other than 8-bit, adding anoption to use QAM (Quadrature amplitude modulation) instead of M-PSK.

2. Equiripple Band pass FIR Filter Design for Speech Signals. Order Optimization for frequency range of 300 Hz to 4000 Hz

Abstract—Speech signal varies from a frequency range of 300Hz to 4000Hz and can be filtered using various types of filters. This paper demonstrates the design of Equiripple Band pass FIR filter specific to the application of speech signal by optimizing the order, first stop band frequency and second stop band frequency of the filter simultaneously using MATLAB Filter Design & Analysis Tool and mathematical computations for a range of values. A 3-dimensional data analysis approach has been considered to design the required stable filter.

Keywords—Equiripple; FIR Filter; Order Optimized; FDA tool Matlab; Speech; Band pass;

Conclusion

This paper describes a method to find the first stop band frequency and second stop band frequency optimizing the order of Equiripple Band pass filter simultaneously. Using the MATLAB FDA tool, 3 dimensional data visualization and analysis and mathematical computations, the first stop frequency for a speech signal of range 300Hz to 4000Hz was found to be 66Hz and the second stop frequency was found to be 4385Hz with the order of 405. The generated stable filter design was simulated and verified and it’s various aspects such as magnitude response, phase response etc. were examined. This method can be used to determine the first and second stop frequencies of not only speech signal constraints but also other frequency ranges for Equiripple Band pass Filter design. The future works using this method can be on Kaiser Window Band pass filter design.

3. Downlink Erlang Capacity of Cellular OFDMA

Abstract—In this paper, we present a novel approach to evaluate the downlink Erlang capacity of a cellular Orthogonal Frequency Division Multiple Access (OFDMA) system with 1:1 frequency reuse. Erlang capacity analysis of traditional cellular systems like Global System for Mobile communications (GSM) cannot be applied to cellular OFDMA because in the latter, each incoming call requires a random number of subcarriers. To address this problem, we divide incoming calls into classes according to their subcarrier requirement. Then, we model the system as a multi-dimensional Markov chain and evaluate the Erlang capacity. We draw an interesting analogy between the problem considered, and the concept of stochastic knapsack, a generalization of the classical knapsack problem. Techniques used to solve the stochastic knapsack problem simplify the analysis of the multi-dimensional Markov chain.

Index Terms—Cellular OFDMA, Blocking Probability, Erlang Capacity.

Conclusion:

In this paper, we have determined the downlink Erlang capacity of a cellular OFDMA system with 1:1 frequency reuse. We have divided incoming calls into classes according to their sub carrier requirement. Then, we have modeled the system as a multi-dimensional Markov chain and applied the techniques used to solve the analogous stochastic knapsack problem to simplify the computation of blocking probability. We have evaluated the worst case Erlang capacity under the assumption that the allotted sub carriers are used by the MS for the entire call duration. However if voice activity factor is taken into account, the inter-cell interference will be reduced, thus causing an increase in the Erlang capacity. This could be a possible avenue for future investigations. Another research direction could be to determine the capacity of relay-assisted cellular systems by the proposed approach.

4. NOISE DETECTION IN IIR DIGITAL FILTER USING MATLAB

ABSTRACT-Filters play a trifling role in every electronic system. The basic serviceable need for filtering is to pass an assortment of frequencies while rejecting others. This need for filtering has a lot of technical uses in the digital signal processing (DSP) areas of data communications, imaging, digital video, and voice communications. The idea of this paper is to design butterworth IIR filter for the signal analysis using MATLAB. By this approach we will denoise the digital signal. We also consider different parameters of Butterworth low pass filter such as Cut off frequency and order of the filter and see the variation of this parameter on noise.

Keywords:- IIR, FIR, FDA

RESULT AND CONCLUSION:

We can analyze the variation of noise with the cut off frequency from the above plots, We can see in fig 2 that round of noise is increased in the signal when we consider cut off frequency Fc= 10. The signal remains noisy up to certain value of cut off frequency. This is shown in fig 3 and 4 where the value of Fc is 100 and 1000 respectively. From above plot we can also analyze that the noise which is more at the start become slightly less as the value of Fc is increase. Further we can see that when we consider Fc= 1200 the noise is reduced in the signal and it would remain less for a range of values. This is shown in fig 5 to 6 and the values of cut off frequency are 1200, and 1400 respectively. So this is the range of Fc where the noise is less in the signal. After that when we increase the value of Fc the noise is again increase in the signal. This is shown in fig 7 and 8 where the value of Fc is 1500 and 2000 respectively. So we can conclude that cut off frequency is not the exact value which separates the pass band and stop band, basically cut off frequency is a range which means it would take some values to separate the pass band and stop band. We can also analyze the variation of noise with the order of filter. The order of filter is an important parameter for designing of any filter. We will see here that how this orders of filter effects the noise in the signal. We can analyze from fig 9 that when the order of filter, N=1 then noise in the signal is very high. As we increased the order of filter, the noise in the signal is reduced as we can see from fig 9 to 12 where the order of filter N= 5, 10, and 15 respectively.

 5. STC-MIMO Block Spread OFDM in Frequency Selective AWGN Channels

 ABSTRACT: OFDM is a method of encoding data on multiple carrier frequencies in digital domain and it is developed into a popular scheme for wide band digital communication, which is essentially a Frequency-Division Multiplexing (FDM) scheme used as a digital multi-carrier modulation method. There is tremendous technological growth towards exploiting the bandwidth of a system. Especially, in wireless domain, 60 GHz RF band has a great scope which can offer a bandwidth of 5 GHz. OFDM systems transmit multiple parallel low bandwidth channels of data through a wideband channel. This technique achieves high data rate providing transmission using low bandwidth sub channels within the allocated channel. The more the number of sub-carriers the better will be the immunity to the frequency selective fading of signals and similarly higher will be the data-rates for that complex architecture with large number of oscillators and filters are required to implement an OFDM system in hardware. Initially after coding as per the Space Time code (STC), we multiply the symbols with the channel and then add white Gaussian noise to it, and then equalize the received symbols. Perform hard decision decoding and count the bit errors. Finally by repeating the same for multiple values of we obtain the plot for simulation and theoretical results. The code for simulation is done in MATLAB.

Keywords: OFDM, STC, frequency selective fading, multi carrier modulation, White Gaussian noise.

CONCLUSION:

From the performance, with different antenna configurations and propagation conditions the proposed MIMO-OFDM (STC) gives potentially higher spectral efficiency. Initially after coding as per the Space Time code (STC), we multiply the symbols with the channel and then add white Gaussian noise to it, and then equalize the received symbols. Perform hard decision decoding and count the bit errors. Finally by repeating the same for multiple values of we obtain the plot for simulation and theoretical results. The code for simulation is done in MATLAB. This design can provides high data rate and high performance over wireless channels that may be time selective and frequency-selective and satisfies our requirement to enhance the high data rates. The spectral efficiency can be improved using above design by reducing cross talks.

 6. Design and implementation of UWB systems with timing synchronization in MATLAB Simulink

 Abstract: Ultra wide band (UWB) bandwidth is much higher than the system bandwidth requirement. Due to large bandwidth in UWB, its systems must have time resolution for system time distribution. But we need to improve data rate and efficiency of the system which uses ultra wide band channels as data rate may trade for power spectral density and performance in multipath. In order to increase data rate, here in this project the orthogonal frequency division multiplexing (OFDM) system is used. But we need to overcome the drawbacks in OFDM like peak to average power ratio (PAPR) [5], carrier frequency offset (CFO), inter symbol interference (ISI), inter carrier interference (ICI). In order to control the effect of PAPR and CFE the single carrier with frequency domain equalization (SC-FDE) can be used, as it has lower PAPR and lower sensitivity to CFO compared to OFDM but the problem is that it is less robust to timing error. As the data rate in UWB systems is high, there is heavy requirement of accuracy in timing synchronization constraints. Moreover the high dispersive nature of UWB channels is an extra challenge to acquire timing synchronization. In general, synchronization can be done based on auto correlation and cross correlation methods. Joint timing and channel estimation (JTCE) can also be done but the computational complexity while correlating will be more compared to correlation methods. But, for UWB systems the timing schemes cannot perform well as multi path UWB channels are denser and longer than wideband channels. Due to trade off’s and computational complexity the implementation of large size Fast Fourier transform (FFT) and Inverse FFT (IFFT) can’t be employed. In this paper, we discuss the design and implementation of UWB systems with timing synchronization by avoiding all the above discussed PAPR, CFO, ISI, ICI and timing synchronization in UWB channel problems and results are compared by transmitting signal through three different channels (dispersive, fading and addictive white Gaussian noise (AWGN) channels).

 Keywords – CFO, ISI, ICI, JTCE, OFDM, PAPR, SC-FDE, UWB

Conclusion

From the obtained simulation results, we would like to conclude that our OFDM design works efficiently in ultra wide band by avoiding the drawbacks in OFDM, including with timing control. As the number inputs are applied randomly continuously the bit error rate is continuously varied. When dispersive and fading type channels are used, the results varies continuously, the scattering plot of data and pilots shows interference of points. As it happens, there will be loss in signal but in AWGN channel the systems shows the stable results, which shows the efficiency of system‟s design and reliability to the channel. So, our proposed design is best suited for UWB communications in AWGN channel. The future work can be extended if we could work with system design and implementation for combination of channels with further advancements like SC-FDE including with timing control unit to avoid the problems in OFDM.

Few more project titles are below:

1. BER Analysis of MIMO OFDM System using M-QAM over Rayleigh Fading Channel
2. Modeling and Performance Analysis of QAM OFDM System with AWGN Channel
3. BER Comparison of DCT and FFT Based OFDM Systems in AWGN and RAYLEIGH Fading Channels With Different Modulation Scheme.
4. A Comparative Performance Analysis of OFDM using MATLAB Simulation with M-PSK and M-QAM Mapping.
5. Implementation of ANT Colony algorithm.
6. Implementation of Binary Genetic algorithm.
7. Implementation of Particle Swarm algorithm.
8.  Implementation and Analysis of Convolutional Codes Using MATLAB.

CONTACT: engineeringtechhub@gmail.com 

Phone number: 9490389019

Note: Complete project costs 5000/- only. (conditions apply)

Some more projects will be added shortly.