Performance Analysis of Adaptive Algorithms in Future Cellular Networks

Abstract

For requirements of high data coverage and rate challenging throughput of future wireless cellular networks, the concept of relaying is found to be a promising solution.

It can be explained in this thesis that the high data coverage rate as well as throughput can be efficiently improved in such networks by making use of digital fixed relays rather working with simple protocols, which do not sustain any penalty of capacity; this explanation is the most significant contribution of thesis. Particularly, the downlink network of non-CDMA is considered in which six digital fixed relates are located simultaneously in every cell in hexagonal arrangement.

Equipment of user is selected to obtain the conveyed signal either through one of the relays or directly from the BD (base station). Three algorithms such as SINR, distance and pathloss are studied for the process of relay selection algorithm. Benefits of diversity are also studied, when the signal is obtained in two-hops.

The proposed algorithms performance can be investigated for several variables of system comprising of cell sizes, rely locations, transmission bandwidth, propagation parameters, transmit power levels, and number of user equipments through simulations of Monte-Carlo.

It can always be shown that the outage is reduced and throughput is enhanced, without having any capacity penalty for practical range of parameters values is investigated. On the whole, it can be concluded that greater potential is included by the digital fixed relaying in offering envisioned high data coverage rate in future wireless cellular networks.

Project Results:

Performance analysis of Qam Modulaltion
Performance analysis of Qam Modulaltion
Relay Tramitted Power
Relay Tramitted Power
Number of users per Cell
Number of users per Cell

 Introduction

Wireless network will go through the point to point or classical cellular networks paradigms. The approaching 4G standard LTE-Advances is the first   mobile interaction standard for cellular networks which permit for the usage cooperative transmission models. The base station  operation is predictable  to increase rates of data  an coverage importantly this is compared to existing technologies of 3G.

Cooperation communication is proposed to face Fading of multipath  permitting users to spread one another ‘s message to the target. Thus every message is transmitted across multipath spread paths, spatial diversity are achieved   without have the need of multipath antennas on every communication channel.

Adaptive filters includes the transformation of parameter of filters the is coefficient across time ,to adapt to transforming characteristics of signal From the 10 decades processors f digital signal had made higher advances in enhancing speed  and complexity  and decreases consumption of power.

The algorithms of real time adaptive filtering are swiftly become practical as well as important fro the future communications. These will be both wireless as well as wired.

The signal in the adaptive filter continues with the coefficient of adaptive filter, this will adjust themselves to desire results like recognizing of an unknown filter or canceling noise in the signal of input. There have a various types of adaptive filters but the algorithms of Recursive Least square (RLS)  as well as  Least Mean Squares is having higher importance.

The two main considerations are that this frames the decision of usage of algorithms of filters. The basic concern of usage of adaptive filter is competitive methods to solve that needs of the filtering. In general so many areas resolve the adaptive filter suitability this includes the four areas, they are Filter Performance, DSP requirements, Filter Consistency and Tools.

Project description

Purpose:

The main objective of the project is to examine the Adaptive filter’s performance and transmit a signal from source to target by using relay. The relay is used to increase the network coverage area.

The below figure is project overview and it is model of entire system. The main aim is to compare the performance of the Gaussian signals, the signal passed directly to the target and transmit signal by using the relays of the filters, and in this it mainly prefers single detector and single user.

Scope:

The project mainly explains about the adaptive algorithms effect at Hop or relay and this compare the Recursive Least Square algorithm and Least Mean Square algorithm. Developing coding and algorithms this implements the code in software of MATLAB and examines the performance.

Problem definition:

Performance of future cellular networks depends on the adaptive algorithms implemented across them. The forthcoming 4G standard LTE- Advanced is the first mobile communication standard for cellular networks that allows for the use of cooperative transmission schemes.

The relaying and base station cooperation is expected to enhance data rates and coverage significantly as compared to existing 3G technologies. Adaptive Filtering involves the changing of the filter Parameters i.e. coefficient over time, to adapt to changing signal characteristics.

Over the past three decades, digital signal processors have made great advances in increasing speed and complexity, and reducing power consumption. As a result, real- time adaptive filtering algorithms are quickly becoming practical and essential for the future of communications, both wired and wireless communications.

There are different types of Adaptive filters but Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms are of major Importance. The two main considerations frame the decision to use the filter and the filter algorithm to use. The primary concern of using an adaptive filter is a cost – competitive approach to solving your filtering needs.

Project Code:

%%% simulation of the first algorithm for N=4;
clear all;clc
R=2;
Ps=10; %% 10 watt
Pn=1.3;
N=4;
prel=[10^-5 0.1 0.3 1];
%%%%%%%%%%% Distance based algorithm %%%%%%%%%%%%%%%%%
for i=1:length(prel)
PI1=6*prel(i)*1/R;
S(i)=Ps/(Pn+PI1);
end
figure,
plot(prel,N./S,'rx-');hold on;
ylim([0 4]);grid on;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% path loss algorithm %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=1:length(prel)
B(i)=10*log10(Ps/prel(i));
PI1=(N)*prel(i)*1/R
if abs(Ps-B(i))~=0
P(i)=min([PI1 abs(Ps-B(i))]);
else
P(i)=PI1;
end
end
plot(prel,P,'bx-');hold on;
ylim([0 4]);grid on;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=1:length(prel)
PI1=6*prel(i)*1/R;
S(i)=Ps/(Pn+PI1);
bs=Ps*1/R;
Sr(i)=max(S(i),bs);
end
plot(prel,N./Sr,'kx-');hold on;
ylim([0 4]);grid on;
title('Avereage speactral effciency at N=4');
xlabel('Relay tramitted power')
ylabel('Average spectral effciency per user');
legend('distance','pathloss','SINR')

Aims & Objectives 

Aims:  To analyze the performance of future cellular networks in terms of Adaptive algorithms using MATLAB simulation.

Objectives:  Following are the research objectives of the project

  • To analyse the concept of Future cellular networks and the role of Adaptive algorithms.
  • To prepare the literature review on the existing performance evaluation techniques across cellular networks and their limitations.
  • To design Adaptive algorithm that can be used to analyse the performance of future cellular networks.
  • To compare the performance of a Gaussian signal i.e. the signal transmitted directly to the destination and the signal transmitted using the Adaptive Filters at the relay

To simulate the proposed system in MATLAB and document the observations

Control of Photovoltaic System with A DC-DC Boost Converter Fed DSTATCOM Using ICOS Ø Algorithm

Control of Photovoltaic System with A DC-DC Boost Converter Fed DSTATCOM Using Icosø Algorithm project is latest matlab project related to power systems. Main objective of this project is to take a effective survey of DSTAT COM  related strategies which are facing in present system. This project will analyze and give detailed report of DSATCOM control methods for students and researches who want to research work onk harmonic suppression related issues. Scientists and researches have worked on this topic to develop many active filters. 

Control of Photovoltaic System with A DC-DC Boost Converter Fed DSTATCOM Using ICOS Ø Algorithm

Proposed Work : 

DSTATCOM consist of VSC  ( voltage source converter ) . This VSC consists of DC BUS capacitor. For maintaining DC Link voltage for used dc bus capacitor to maintaining load PV Array or boost converter which works on battery is used in this system.

Issues in System:

With the increase in usage of electricity in every part of our work,  we need a effective power quality systems which can be used by power co operations to give quality power.  Major problem in power quality is non liner loads caused by power electronic devices in distributed systems. 

Proposed System Development: 

This application will be developed in MATLAB /SIMULINK software and the results which are shown from matlab gives effective and best results based in performance characteristics. 

Project Purchase: 

This is latest MATLAB project which is part of M.Tech EEE Power systems. For detailed explanation about project download project abstract .

Spreading the transmitted symbols in OFDM across the STC-MIMO in Frequency Selective Rayleigh Fading Channels

Abstract:

Here it expand this spreading idea through symbols  in the scheme of OFDM by spreading matrices of unitary which depends on the rotated DFT (discrete Fourier transform) or matrices of rotated Hadmund  stated in  literature  to use STC-MIMO-OFDM (Space-Time Coded Multiple-Input Multiple-Output OFDM ) schemes.

It refers the resulting schemes to STC-MIMO-BOFDM (STC-MIMO Block spread OFDM) system’s multidimensional diversity which includes space, frequency; time as well as modulation diversities utilized which results fine bit error implementation in a selective frequency. Additive white gausian channels fading channels this is compared to the traditional OFDM systems including or without STC’s recreation passed out with the Alamouti code confirm the benefit of the stated STC-MIMO-BOFDM systems.

Introduction:

OFDM (Orthogonal Frequency Division Multiplexing) had been intensively thought in literature with passing signals across fading channels of frequency selective. The main benefit of this method which is evaluated to one carrier modulation to facilitate high data rates issue by relevant less complexity receiver needs only FFT (Fourier transform) processor tracked by one tap equalizes on single carrier system because  sub carriers orthogonal excludes multipath from being collaborated over the passage at the sign level.

The use of spreading matrices to mix the passed symbols linearly over the sub channels it is considered for one wireless communications of antenna system in the literature the main benefit of utilizing spreading machines that it permits to gain diversity across selective channels of frequency fading.

Unitary spreading machines this is based on the turned Discrete Fourier transform or rotated Hadamund matrices to develop one antenna OFDM performance schemes was initially established by Bury and remaining was analyzed by MC cloud.

The utilization of receive antennas and multiple transmit will improve the ability of wireless communication systems. These schemes are known MIMO (Multiple Input Multiple Output) schemes. STCS (Apace Time Codes) are the codes planned for the usage of MIMO systems. Space-Time Trellis Codes, Bell Lab Layered Space-Time (BLAST), STBCs (Space-Time Block Codes) are different STCs kinds.

The cooperation of MIMO systems by STCS with OFDM techniques this accepted a attention fair amount in literature .Moreover this combinations mostly uses STCS by block spread OFMS method this is not considered as the combination is mainly used to increase the scheme performance with the vaguely more cost complex receiver and transmitter configuration.

It is mainly focus on the expansion of idea of Block spread OFDM which was already mentioning in above, use to the scheme of MIMOOFDM by STCs. It calls resultant method STC-MIMO-BOFDM to differentiate the standard MIMO-OFDM without block spreading. It is signified that the provision “spreading “ this is inherited through the above to communicate the expansion of modulation rater then the bandwidth expansion .

In this it mainly discuss about the  novel contributions 1) the comparison of error performance between the traditional OFDM and STC-MIMO-BOFDM 2) Here it uses the   Alamouti code  for the more detailed  derivation Simple maximum Likelihood (SMl) of decoding  technique  for stated TCMIMO-BOFDM system. The block spreading application method to traditional STC-MIMO-OFDM system recover the error recital of the STC-MIMO_OFDM systems, Systems (, BOFDM systems and without block spreading (STC-MIMO-OFDM systems).

Here it exploits the spatial dimension by applying several antennas on the side of the interaction this is a great result to highly improved efficiency of bandwidth. The information research theoretical this is revealed the multipath channel of wireless is  capable of huge capacities offered that the multipath scattering is very rich, Scattering of multipath I s properly exploited by the use of the suitable processing architecture.

The diagonally layered space-time architecture is stated in called as Diagonal blast in this approaching below figure .The diagonal approach‘s detector is moreover is very complicated    and it is tough to implement. So BLAST simplified version, this I known as vertical Blast or else V-blast.

Source Code:

[cpp]

clc
clear all
close all
m=16;
k = sqrt(m);
r = 2*(0:k-1) – k + 1;
[xi, yi] = meshgrid(r);
c = xi + i*flipud(yi);
Qmm=c(:)’;
Es=10;
index=1;
step=4;
for SNR=4:step:20
count=0;count1=0;
N=10^3;
if SNR==20 N=2*10^3; end;
for it=1:N
A=round(rand(1,8));
s1=Qmm(bi2de(A(1:4),’left-msb’)+1);
s2=Qmm(bi2de(A(5:8),’left-msb’)+1);
C1=[s1 s2;-s2 s1];
C=[C1; conj(C1)];
F=eye(2);
K=1/sqrt(2)*(randn(2,1)+i*randn(2,1));
N0=(2*Es/10^(SNR/10));
Z=sqrt(N0/2)*(randn(4,1)+i*randn(4,1));
R=K’*F*C’+Z’;
H1=[K(1) K(2); K(2) -K(1)];
H=[H1 conj(H1)];
R=[R(1:2)’; conj(R(3:4)’)];
S=R.’*H’/(2*sum(abs(K).^2));
R1=[ abs(Qmm-S(1)); abs(Qmm-S(2))];
[D,I]=min(R1,[],2);
Rec_bits(1,1:4)=de2bi(I(1)-1,4,’left-msb’);
Rec_bits(1,5:8)=de2bi(I(2)-1,4,’left-msb’);
count=count+sum(abs(A(1:8)-Rec_bits));
end;
Berr(index)=count/(N*2);
index=index+1;

end;
figure,semilogy(4:step:20, Berr,’rx-‘);
grid on;title(‘Optimal Performance of MIMO system with ML decoding’);
xlabel(‘—SNR’);ylabel(‘—BER’);

[/cpp]

The methods which are based on multiplexing transmit signals across several antennas across space which can be occupied under the more general term Space division Multiplexing (SDM) or Space Division Multiple Access (SDMA).SDMA methods search the spatial dimension by using several antennas to pass. Fundamentally these methods simultaneously pass different signal son various antennas at the same carrier frequency.

Thee parallel streams f data are combined with the air but it is recovered at the recover by using sophisticated processing algorithms, this mainly needs multiple antennas to receive those guarantees the adequate error performance. The difference between SDMA and SDM is that it permits various users to pass simultaneously on single antenna in other hand in SDM here single user pass simultaneously on several antennas .Here it also explains about Hybrid schemes.

It is a fact that in these SDMA methods will differ from conventional several access techniques. Most of the differences are pointing out at first the entire channel bandwidth used by an SDMA system is higher than other symbol rate this is identical to bandwidth which is needed by a traditional single carrier transmission method like  Amplitude modulation.

Secondly Frequency division multiple access here every transmitted signal captures the total system bandwidth. Ultimately the total system bandwidth is utilized simultaneously by all transmitters at all the time. These variations together are explained about the efficiencies of using different algorithms.

Time division multiple access (TDMS) the total system bandwidth is utilized by all the transmitters. These deviation together are accordingly this give SDM the potential to realize greater bandwidth efficiencies than the other different multiple access methods.

Maximum-Likelihood Carrier Frequency Offset Estimation for OFDM Systems

Aims & Objectives

Aims:

To estimate the carrier frequency in maximum likelihood conditions across OFDM systems using MATLAB simulation.

Objectives:  

Following are the project objectives

  • To understand the concept of OFDM systems and frequency offset estimation methods
  • To prepare literature review on existing carrier frequency offset estimation techniques and their corresponding limitations
  • To design maximum-likelihood carrier frequency offset estimator across OFDM systems
  • To simulate the proposed technique in MATLAB
  • To document the results and observations.

Results and Explanations:

Simulation results

By the following system parameters such as normalized CFO (sub carrier spacing/ frequency deviation) fo = 0.01, number of sub carriers N = 512, and cyclic prefix size Ng = 52, simulation experiments were performed. In order to simulate the multipath channel under fading and static scenarios with L=12 principal components, the Jakes model was used.

For 400 symbols of OFDM, the variation of estimation error of CFO is calculated, and for all SNR values the results are common on hundred monte-carlo models. The proposed estimator’s performance is compared by the frequency domain of weighted least squares (WLS) approach and MLE scheme which is proposed by Moose. For various SNRs, the variance of CFO estimation error in time-invariant channel is illustrated in a given figure.

In case of static channel, all the four methods are identically performed, expect in low region of SNR, where the suggested TD-MLE is found to perform much better. Also, in this case observe that the suggested method of FDMLE minimizes to MLR method proposed by Moose, as a result of this they both possess the same implementation. In fading channels case, the solutions are displayed in both the figures represented below through 0.005 normalized Doppler frequency of fast fading and 0.005 (slow fading) normalized Doppler frequency respectively.

slow fading channel
slow fading channel

Fig: Comparison of performance of various estimation schemes of CFO in case of slow fading channel, with normalized Doppler frequency of 0.005.

The figure represents that both the suggested schemes such as TD-MLE and FD-MLE produce comparable results, and provide enhanced performance when compared to WLS approach and Moose MLE under each SNR, mostly in case of higher SNR regions.

fast fading channel
fast fading channel

Fig:  Comparison of performance of various estimation schemes of CFO in case of fast fading channel, with normalized Doppler frequency of 0.005. 

The figure represents that each and every scheme viewed to possess the implementation floor in high and SNR moderate regions, and a lower error floor is contained by the suggested MLEs when compared to WLS scheme and Moose MLE. The reason for enhancement of performance is that the fading is considered by the suggested estimators.          

Conclusion:

For the estimation of CFO, the two estimators of maximum likelihood are proposed in the conditions of the fading channel. In the design of this estimator, the considering factor is coefficient of fading. In this there is a special case about the static channel and in this the coefficient of fading is a=1. Under the conditions of the static channel, scheme of WLS and moose MLE these are comparable with the performance of the proposed schemes, these are shown by the simulations.

Under the conditions of fading, comparing to the conventional schemes this proposed one is better. The reason for this is considering the fading factor of the proposed schemes. For the great accuracy of the estimation, the complexity reduction is the great advantage of the scheme TD-MLE. In this estimation of the CFO, use the dominant multi paths. So, the CFO is used for the fading channels by the proposed estimators. In OFDM systems reduced the ICI; by this the performance of the system is increased.

Performance analysis of Wideband CDMA systems Matlab Project

Introduction:

CDMA means Code Division Multiple Access; this system uses a technique called the multiple accesses. For various technologies such as radio communication technologies this system is useful. This system has the spread spectrum technique; in this the data bandwidth is spreader by the code uniformly. In such time the transmitted power is same. For all the codes of the user, the codes which are spreading are orthogonal.

This spreading code has its own specifications. Here the user’s code is generated by Gold codes, Walsh codes, and generation of the PN sequence. For modeling of this CDMA systems use the different approximations types such as Improved Gaussian Approximation (IGA), Gaussian Approximation (GA), and Simplified Expression for Improved Gaussian Approximation (SEIGA).

In this, by using the Gaussian Approximation (GA) CDMA is modeled. When ever using the CDMA with GA in that situations keep the approximations of interferences and the channel noises are at zero because, the random variables of the Gaussian with certain variance of Gaussian. For describing the following, fading is used. Those are the amplitude’s rapid fluctuations, the radio signals multipath delaying or phases or distance of travel. This is done because at various times, the interferences are living in between the transmitted signal of two or more versions.

At receiver side the result of this is varying of the same signal amplitude and the phase. The shadowing result is slow fading, this is done because of the mountains, buildings and differ other objects. The occurrence of the shadowing is done when at the power of received signal the mobile experience the reduction. This is possible when the mobiles moved to the behind of obstruction.

The Lognormal Fading Channel is considered for the proposing system. According to the distribution of Lognormal, randomly this channel fades the transmitted signal amplitude. The channel coherence time is large when compare it with the channel delay constraints in that situations the fading channel of Lognormal is considered as slow fading.

Motivation:

The various services are supported by the wireless multimedia system. The supporting of these services is done with various performances of delay and the error and also the transmission rates at a wide range. The structure of the network has the following such as terminals, mobile handsets, broadband network, base stations and control entities. In this the terminals means data, low rate video, image and voice. The broad band network is used for the base stations connections. This broadband network is possible either with the wired connection or wireless connection. The following is the figure shows the connection of broadband network in both the point of view such as in wired and in wireless.

At the subnet of wireless, at the mobile/fixed user initiating or terminating the communication link from base stations to the broadband network. At different times the transmission id takes place. First the transmission is taking place in between the mobiles and the base station, this is known as uplinks. And next transmission is taking place in between the base station to the mobiles, this is known as the downlinks. This is possible either in between the same radio channels or different radio channels. If this is in various or different radio channel then it is called as “Frequency Division Duplex FDD”, and if this is in same radio channel then it is known as “Time Division Duplex TDD”.

The following is the table shows for different multi media services requiring bandwidth in the downlink and uplink of the wireless network.

Multimedia Services Downlink Bandwidth Uplink Bandwidth
Broadcast VideoBroadcast TVEnhanced pay per View 1.5 – 6 Mbps/ channel None
Interactive VideoVideo on DemandInteractive TVInteractive GamesInformation Retrieval Services 64 kbps – 6 Mbps 9.6 – 64 kbps
Internet (www, ftp, telnet)Voice 14.4 kbps – > 10 Mbps 14.4 – 128 kbps
Symmetric dataDesktop multimediaWork-at-homeVideo ConferencingVideo TelephonyFax 9.6 kbps – 2 Mbps 9.6 kbps – 2 Mbps
Small Business/HomeInternet Home pageInternet Information Server 9.6 – 384 kbps 64 kbps – 1.5 Mbps

Table: the transmission rates of the uplink and downlink in the multimedia services.

Over the mobile when the signal is under the transmission, such signal has more attention because may occur the shadowing and the path loss. These are might be occur because of the uncertain such as buildings, terrain, and any other. These are very much larger when compare this to the radio channel frequency’s wave length, the result of this is faded of multiple paths and the channel with time variant.

That means the signal which is received has the variations in the power and randomly it will be fluctuated. If the power level which is of same, at this if all the mobiles wants to transmit in that times the receiver mobile which is near of this signal, at strong level this is received. If the receiver which is far away of this signal, at weak level this will be receive.

This type of effect is known as “near-far effect”. If the power levels of the received signal has the differences in the range of the 80 ~ 100 dB then the result of this is causing the cochannel interference with more amount and saturate the receiver’s weaker signal.

At very low rate the utilization of the frequency spectrum of current mobile system is there. According to the estimations of the optimistic, the fall of the highest achievable capacity of mobile systems of second generation 20 percent under the capacity of the Shan-non channel. In order to close the capacity of the channel in real system, the more diversity paths are used by the wideband mobile system capacity.

To all the users started the radio channel of the common wideband, because of this the CDMA wireless system will became the potential candidate for the mobile communications which are having the high capacity. The standard bodies of wireless communication systems in Europe, Asia, and North America proposed that CDMA for the wireless systems of third generation is as “major multiple access technique”.

In order to lighten the near-far problem, develop the algorithms for the power controlling. This is done at the receiver of base station compensate the fluctuations and variations of signal power of a mobile. The adjustment made to the power will leads to the increment of the system capacity. This increment is done in terms of same number of calls. In CDMA, the limiting factor is level of total interference for the capacity of the system; in this the frequency and the time of the radio channels are not separated.

If the single user uses more power then it will automatically reduce the communication capacity of the other users. This reduction is done in significant manner. So, here in CDMA the most essential and important are management of resource and the power control. In so many investigations, the uplink power control of the CDMA is treated as main focus because the signal of the user cell experiences the characteristics of various channel by the following of the propagation path of various.

On other hand, the downlink path desired signals and the interference of the intra-cell are under doing the impairments of the same channel and preserve the relative power levels. If, on the basis of the single cell, if the noise of the background is negligible at that time the power control of the downlink is no need. Where as in case of the multi-cell, in this should employee power control of the downlink. But in this the power control of the uplink is more complicated.

Besides the hostility of the near-far problem, in order to accommodate the various Qualities of Services (QoS) the power control is engaged in terms of BER (Bit Error Rate). For maintaining the BER which is targeted, processing gain increment, or decreasing the equivalent transmission rate maximum power fails available if the channel condition id poor. This is improved further in terms of average “signal-to-interference ratio” and the BER.

In other hand it was proposed in this research here the rate of data will be regulated in the way that requirement of delay performance of a multimedia CDMA system is controlled as a task of the resource (rate and power) budgets. It is highly advantageous to mange the resources of network efficiently so that the QoS needs for user is satisfied and it uses network resources maximally.

A significant factor in assigning power as well as data rate to mobile is the assignments of base station it resolve the access point of the mobile to network of wireless. In other words allocated resources may differ with various assignments. Traditionally a mobile user is linked to the closest base station or to the one where broadcasted signal of pilot is sensed as the strongest that implies that the route from the mobile to this base station is the greatest with respect to all additional base stations. The latte and performer assignment is one of the least signal attenuation (LSA) assignments. In the absence of fading and shadowing where NBS assignment is valid.

If the traffic is evenly distributed across the total network consequently every base station sees the same entire interference at its getting antenna. LSA assignment offers maJcimum SIR, therefore the best performance .Moreover a base station  is having  high local t r a c ,in spite of being the Choice, may take  a mobile signal  with a Iowa   level  a than  a near  base station  with  a lighter local traffic. So an equipment decision based on the global traffic at least in a dust of near base station outperforms the MA.

It is interested in exliming the performance of globally decided assignment which is compared to the LSA; here it combines the assignment with the allocation of resource. Optimization methods are employed commode in the literature for cellular management system and power control.

The algorithms of power control HMS are stated for mobile system s of narrow band to minimize the entire transmitted power and this enhances the system capacity. Many of the algorithm are planed for traditional voice communication as well as do not tackle various rates and quality of service for various users, as for the system of CDMA, relevant architectures are focused on transmission rate and BER requirements of real time services.

Moreover non real time forces with different   requirement of delay had not been tackled. However stated models are restricted to single-cd environments where as roaming problems as well as linked challenges like base station is disregarded.

Results:

distance in the power control algorithm
distance in the power control algorithm

This figure shows about the distance in the power control algorithm in the outage probability when the system is in the CDMA.

Comparison of performance of Gaussian Approximated simulation of CDMA
Comparison of performance of Gaussian Approximated simulation of CDMA

Figure 2: Comparison of performance of Gaussian Approximated simulation of CDMA 

The above figure represents SNR versus Signal to Noise ratio in dB for performance of the Gaussian approximation values of CDMA without and with awgn fading. In clear, the system without awgn fading when compared to the system with awgnl fading performs better for SNR’s highest values.

Comparison of performance of Gaussian approximated CDMA simulation with awgn fading
Comparison of performance of Gaussian approximated CDMA simulation with awgn fading

Figure 3: Comparison of performance of Gaussian approximated CDMA simulation with awgn fading.

The above figure represents SNR (dB) versus BER for an A-CDMA system for different values of memory of channel with awgn fading. As the memory of channel enhances, in fading the rate of change reduces, and thus the BER reduces.

Performance comparison of Gaussian approximated CDMA
Performance comparison of Gaussian approximated CDMA

Figure 4: Performance comparison of Gaussian approximated CDMA simulation for the case of awgn fading with different user 0 powers.

The below figure represents the SNR (dB) for Gaussian approximated CDMA simulation   with awgn fading for various values of user 0 power. At present, the level of power of interferers is proportional directly to the 0 user power, so that CLT (Central limit theorem) can be integrated to estimate the interference contribution with a non-unity Gaussian variance as zero mean Gaussian distribution. Thus, the interferer powers enahnce as user 0 power enhances.

Conclusion:

The performance of Gaussian approximated CDMA simulation system is compared with awgn fading in this paper. Obviously, the case of awgn fading performs poorer when compared to no fading case. In addition the performance of Gaussian approximated CDMA simulation system is related for various cases of memory of channel. Faster is the rate of change of fading, if lower is the memory of channel, and thus the rate of BER also improves.

The interferer power is higher if the user 0 power is higher, and therefore the rate of BER also improves. This research’s most significant accomplishment is to attain a simulated BER of the order of 10 seconds or less for Gaussian approximated CDMA simulation system by making use of MATLAB.

Secret Communication through Audio for Defense Applications

Secret Communication through Audio for Defense Applications is MATLAB Project. Main objective of this project is to develop a security related application which is useful for sending secured information encrypting in audio file.

Secret Communication through Audio for Defense Applications

Code Explanation:

Select Wav file (cover signal) for Embedding: 

  1. The above set of code would be executed whenever the “Select a .wav file button” is pressed.
  2. Next when the “Hide Text button” is selected and the “Select a .wav file button” is pressed then the code would guide us through path and then file name to select the .wav file.
  3. As the above sets of steps are executed it would make the “Hide the Text button” go enable.
  4. Once the “Hide the Text button” is enabled it reads the selected .wav file and stores first 40 bytes in the variable named “header”.
  5. The next 41st to 44th bytes are clubbed together to make a single element and is stored in the variable named “data_size”.
  6. Then close the file, only wav data samples are sufficient for extracting the text.

Encryption:

  1. Enter text in the edit box.
  2. Get text message from edit box.
  3. Then convert message to binary.
  4. Reshape the message binary in a column.
  5. Calculate length of message binary.
  6. Convert the length to binary.
  7. Hide identity in first 8 .wav data samples.
  8. Hide binary length of message from 9th to 28th sample
  9. Hide the message binary starting from 29th position of wave data samples.
  10. Open a new .wav file in write mode
  11. Copy the header of original wave file
  12. Copy the wav data samples with hidden text in new file
  13. Your text is hidden in  new ‘ randname ‘.wav file.

Decryption Process:

. Extract the length of text from first 9th to 28th wav data samples

2. Convert the length to decimal

3. Extract the LSB from wave data sample

4. Convert it to binary

5. Convert to character (ASCII)

6. If message is empty then display File has no hidden text.

Matlab Simulation Projects

Our Matlab simulation can support two forms of data source- data that are produced randomly and the image file. In case of random data it is the best option to check the impact of the channel on the performance of the BER as well as the constellation of the signal. Moreover, image file gives us an impression as well as a comparison of various other channels.

The pilot data is inserted into the head of the source data after the production and it is used to know the shift of the channel that is fading away. It enables to train the decision thus helps in adjusting the signal that is received with a recover phase. Any percentage of the data length of the pilot data can be set by the users to the total length of the data. This means the source data in addition to the pilot data is used in our proposed model. According to our study we set the stimulation of the pilot data to 8% of the entire length of the data. 

The user can use whether he wants to use the stimulation or the gray coding. If he prefers the gray coding the mapped data from the binary to the complex along with the datum represent a single point in the diagram of the constellation. The phase shifting key is used in our model to modulate the source of data and the an arbitrary Mary PSK.

We also test the QPSK modulation in our simulation. Three different channels are simulated in our simulation AWGN channel, and Rayleigh frequency selective slow fading channel and Rayleigh flat slow fading channel. AWGN channel is straight forward channel and just by adding a Gaussian noise into the network can meet the SNR specified. Flat fading and selective frequency channels by the Clarke and Gans fading model is used in the simulation.

Graphical Password Authentication Using Cued Point Technology MATLAB Project Abstract

Introduction to Graphical Password Authentication Using Cued Point Technology MATLAB Project:

Graphical password authentication is use to support users in choosing higher passwords, so rising safety by increasing the effective parole house.

If we choose poor passwords result in the appearance of hot spots – parts of the image wherever users are additional probably to pick click-points, permitting attackers to mount additional sure-fire lexicon attacks.

Usable security has distinctive usability challenges as a result of the requirement for security usually implies that commonplace human-computer-interaction approaches cannot be directly applied.

PROPOSED WORK:

Our projected system permits user selection whereas trying to influence users to pick stronger passwords.

It additionally makes the task of choosing a weak parole (easy for attackers to predict) a lot of tedious, so as to discourage users from creating such decisions.

Many types of graphical passwords are projected. In effect, our theme makes selecting a safer parole the “path-of-least resistance”.

During this password are created by positioning a “template” over a background image in order that the user’s secret areas fall at intervals the cut-out parts of the guide.

We tend to focus totally on click-based graphical passwords. In Pass Points passwords contains a sequence of 5 click points on a given image.

Users could choose any pixels within the image as click-points for his or her watchword. They found that users had problem memory the position of their example and selected similar areas of the photographs.

It absolutely was found that though comparatively usable, security issues stay. to cut back the safety impact of hot spots and more improve usability, we tend to projected an alternate click-based graphical watchword theme referred to as Cued Click-Points.

Graphical passwords provide an alternate to text-based passwords that’s supposed to be a lot of unforgettable and usable as a result of graphical passwords admit our ability to a lot of accurately keep in mind pictures than text.

To log in, they repeat the sequence of clicks within the correct order.

Download  Graphical Password Authentication Using Cued Point Technology MATLAB Project Abstract.

Optical Character Recognition Project Report

Introduction to Optical Character Recognition Project:

The project is about Optical Character Recognition. It is a process of classifying optical patterns with respect to alphanumeric or other characters. Optical character recognition process includes segmentation, feature extraction and classification. 

Text capture converts Analog text based resources to digital text resources. And then these converted resources can be used in several ways like searchable text in indexes so as to identify documents or images. 

As the first stage of text capture a scanned image of a page is taken. And this scanned copy will form basis for all other stages. The very next stage involves implementation of technology Optical Character Recognition for converting text content into machine understandable or readable format. 

OCR analysis takes the input as digital image which is printed or hand written and converts it to machine readable digital text format. Then OCR processes the digital image into small components for analysis of finding text or word or character blocks. And again the character blocks are further broken into components and are compared with dictionary of characters. 

Matlab is an environment where problems and solutions can be denoted in terms of mathematical notations. A use of Matlab includes analysis, algorithm development, computation and much more. Matlab is a system where elements are placed in an array but are not required any dimensionless. It helps us to solve our problem in no time and provides an easy solution. 

The OCR text is written into a pure text file that is then imported again to a search engine. The text is used as index searching of the information. Accuracy rates are measured in several ways and the ways they are measured impact the accuracy rate.

Download  Optical Character Recognition Project Report.

Using modelling techniques to compare digital modulation systems

MSc Project Objectives Form

Programme of Study: M Sc Telecommunications Engineering

Project Title: Using modelling techniques to compare digital modulation systems

Project Objectives:

List measurable outcomes that you expect to achieve

1. Proficiency with MATLAB to carry out modelling of modulation, demodulation and transmission effects

2. Modelling FSK over a variety of system parameters

3. Modelling transmission impairments such as noise, multi-path effects, fading effects

4. Investigation of effects on synchronisation, eye-height and similar effects

5. Extending the analysis to more complex systems such as OFDM

6. Evaluation of MATLAB as a modelling tool