Having over 18 projects in Wireless Communication
Wireless Routing Protocol Algorithm
Distance Vector is term used to describe routing protocols which are used by routers to forward packets between networks. The purpose of any routing protocol is to dynamically communicate information about all network paths used to reach a destination and to select the from those paths, the best path to reach a destination network. The terms distance vector is used to group routing protocols into two broad categories based on whether the routing protocol selects the best routing path based on a distance metric (the distance), finding the path that has the lowest total metric to reach the destination.
LEACH protocol designing
LEACH (Low Energy Adaptive Clustering Hierarchy) is a hierarchical-based routing protocol which uses random rotation of the nodes required to be the cluster-heads to evenly distribute energy consumption in the network. In this project leach protocol will be implemented to analyze the working of whole procedure. The steps we will follow are as follow 1. First of all we will have coverage area from user 2. After that we will give initial parameters for the network in which parameters such as nodes, energy of transmitter and receivers etc 3. After that clusters will be made and then energy basis nodes will be declare as half dead node dead node and alive nodes will be find 4. Then on basis of dead nodes we will analyze lifetime of system
Stable Election Protocol in EEP
Due to small power batteries in WSNs, efficient utilization of battery power is an important factor. Clustering is an efficient technique to extend life time of sensor networks by reducing the energy consumption. Many clustering techniques were introduced to find cluster heads in a cluster. One of them which is Stable election protocol. SEP protocol is for clustered heterogeneous wireless sensor networks. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. In this project, implementation of SEP is done. This project also concludes by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. The analysis show that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.
Clustering Approach Development in WSN
Sensor node is a tiny autonomous device which is used for the monitoring, tracking and surveillance. A number of sensor nodes together form a Wireless Sensor Network. Wireless Sensor Networks are used for a number of applications like monitoring of human body, under water surveillance, military purposes and traffic control etc. The consumption of the Wireless Sensor Networks is increasing day by day as sensor nodes are becoming cheaper. Inefficient or manual placement of sensor nodes leads to the failure of sensor networks. By placing the nodes at a pre-determined optimized location, sensing range can be minimized. Sensing range minimization will lead to increased lifetime because of the less energy consumed during monitoring of targets. Here is implementation of algorithm for efficient clustering and deployment of sensor In this project we will have some initial parameters from user in which the parameters are as • Location points of nodes • Number of sensors So that we can find optimized location of sensor on basis of k-mean clustering Process follow will be 1. Get initial parameters of nodes and sensor 2. Find Euclidean distance between nodes and sensor 3. Do k-mean clustering 4. And on basis of clustering find optimized location for sensor that is optimized location for sensor deployment
Wireless System Design in Simulink
In any communication system, there must be an information source (transmitter), a destination (receiver) and a medium to transmit information between the transmitter and the receiver. Message source originates message such as human voice, a television picture a teletype message or data. The message can be electrical and non-electrical. If it is not electrical, the source transducer will convert it into electrical signal. The transmitter may be consists of analog to digital converter, data compressor, source encoder, channel encoder a modulator or any other complicated subsystems. The receiver may be consists of demodulator, channel and source decoders data expender, digital to analog converter or others. Receiver transducer converts the electrical signal to its original form- the message. Message destination is the actual unit to which the message it sent. The channel is the information transmission medium. This medium can be of different types such as wire, a waveguide, an optical fiber or a wireless link. As the channel act as a filter, during the transmission of the signal (message) through the channel, the signal can be distorted due to the attenuation and phase shift suffered by different frequency component of the signal. Noise will also be added with the transmitted signal during the transmission of the signal through the channel. In this project WSN communication module is implemented and performance is analyzed over the AWGN channel. The analyzing parameters are BER, SNR etc. Signal is generated firstly then is passed through the procedure of transmission then passed through AWGN channel and then at receiver algorithm is implemented and at end the transmitted signal and receiver signal is compared on basis of parameters
PAPR reduction Using PTS algorithm
Communication is one of the important aspects of life. Signals were initially sent in the analog domain, are being sent more and more in the digital domain. For better transmission, even single carrier waves are being replaced by multi carriers. Multi carrier systems like CDMA and OFDM are now a day’s being implemented commonly. In the OFDM system, orthogonally placed sub carriers are used to carry the data from the transmitter end to the receiver end. Presence of guard band in this system deals with the problem of ISI. But the large Peak to Average Power Ratio (PAPR) of these signal have some effects on the communication systems. The major drawback of orthogonal frequency-division multiplexing (OFDM) is its high peak-to-average power ratio (PAPR), which gets even more substantial if a transmitter with multiple antennas is considered. To overcome this problem, in this project, the partial transmit sequences (PTS) method well known for PAPR reduction in single antenna systems is studied for multi-antenna OFDM. Finally in this project after reduction of PAPR using PTS algorithm performance is checked on basis of number of errors in signal or by calculating PAPR in signal.
PAPR reduction Using SLM algorithm
In this project thee performance of peak-to-average power ratio (PAPR) reduction scheme i.e. selected mapping (SLM) scheme is investigated. In the presence of nonlinearity, we analyze the impact of the selected mapping (SLM) technique on bit-error-rate (BER) performance of orthogonal frequency division multiplexing (OFDM) systems in an additive white Gaussian noise channel The SLM technique was first described by Bauml etal. Selective mapping scheme is a technique in which multiple phase rotations are applied to the constellation points, and the one that minimizes the time signal peak is used. Selective mapping involves generating a large set of data vectors all representing the same information. The data vector with the lowest resulting PAPR is selected. Information about the selected and transmitted data vectors is coded and these codes are by an additional sub carriers.
Simulink Modal Design For Wireless System
In our project we are going to implement a Simulink model for wireless sensor network standard having OFDM concept. This system will be implemented in Simulink toolbox of mat lab and which will have transmitter and receiver with between them standard methodology used for data transmission and at receiver end there will be a analyzer block which will help us to analyze the performance of system on basis of parameters like BER, number of errors etc. The main goal of this Project work is to learn and understand the features of the wireless transmitter and receiver performance, BER is calculated after model designing by error rate calculation in model to check the accuracy of sensor network standard model for transmitter and receiver synchronization, for the implementation of model Mat lab-Simulink is used.
Signal Equalization in OFDM systems
In modern digital communications, it is well known that channel equalization plays an important role in compensating channel distortion. Unfortunately, most channels have time varying characteristic and their transfer functions change with time. Also, time-varying multipath interference and multiuser interference are two major limitations for high speed digital communications. Usually, adaptive equalizers are applied in order to overcome these issues. For adaptive channel equalization, we need a suitable filter structure and proper adaptive algorithms. High-speed digital transmissions mostly suffer from inter-symbol interference (ISI) and additive noise. The adaptive equalization algorithms recursively determine the filter coefficients in order to eliminate the effects of noise and ISI. Several algorithms like Least Mean Square (LMS), Recursive Least Mean Square (RLMS), Normalized Least Mean Square (NLMS) etc., has been proposed to perform this operation of equalization. In this project, we study the adaptive equalization technique with the use of normalized least mean Square algorithm.
Wireless System Protocol Design using MATLAB
Implementation of a simulink model for 802.11 standard of wireless. This system will be implemented in simulink tool of Matlab and which will have transmitter and receiver with between them standard methodology used for data transmission and at receiver end there will be a analyzer block which will help us to analyze the performance of system on basis of parameters like ber, number of errors etc. The main goal of this Project work is to learn and understand the features of the IEEE standard 802.11a and afterwards, once familiarized with this standard, to develop an OFDM 802.11a PHY layer baseband implementation with the characteristics showed in such standard. Matlab/Simulink is used for designing the model.
Antenna Designing with Directivity Analysis
The three decade of growth and development of satellite communication has provided the world with international and long distance fixed and mobile satellite services (FSS) that have helped to change the world to what we know today as Global village. The global communication satellite market has been expanded rapidly into personal communication services, mobile communication services, navigational satellite services and broadband satellite services. What makes satellite communication such an attractive market can be summarized in wide area coverage, distance insensitivity, flexibility, multiple access, destination capability and economical reasons. An antenna array is a set of N spatially separated antennas. The number of antennas in an array can be as small as 2, or as large as several thousand (as in the AN/FPS-85 Phased Array Radar Facility operated by U. S. Air Force). In general, the performance of an antenna array (for whatever application it is being used) increases with the number of antennas (elements) in the array; the drawback of course is the increased cost, size, and complexity. In this project there is an implementation of linear –polar antenna array radiation patterns and finally do comparison between them on basis of radiation pattern.
Route Optimization Using ACO optimization
Now a day’s in Wireless communication data is transfer by wireless mediums in that admin generally give source and destination of data many algorithms have been developed for finding best route for selection of next neighbor node for data to reach destination. In this project Ant colony optimization (ACO) is used to solve the best node for next data transfer. First of all we will have input from user that is coverage area in which the nodes lies. Secondly we will have number of nodes from user. After that on basis of Euclidean distance we will calculate the initial population of our problem which is to solve by ACO. ACO is generally an optimization technique which will find best results for an objective which in this is next transfer node. So here our fitness function is on basis of distance. To get maximum fitness value we will optimize the initial population using ant colony optimization and will get best route with objective of minimum distance and reach the destination node from source.
Digital Video Broadcasting Simulink
The demand of wireless communication is growing exponentially and next generation of wireless broadband multimedia communication systems will integrate various function and application in same system which supports large data rates with su?cient robustness to radio channel impairments, requires careful choosing of modulation technique. The suitable choice is orthogonal frequency division multiplexing (OFDM) which is special case of multi-carrier communication system, where single data stream is transmitted over number of lower sub-carrier. This altogether has brought to the conclusion that one radio frequency channel can be used to transmit more than one TV program. Digital video broadcasting (DVB-T) means broadcasting a multiplex, a package of various services. We had implemented DVBT system with addition to an effective scheme called as Orthogonal Frequency Division Multiplexing (OFDM) with which the high bit rate over the frequency selective channel is guaranteed to some extent.
Wireless Updated Routing Protocol Design
This project is an advancement over the distance algorithm for data transfer and have an objective of delay reduction and fast data transfer. Main objective in this project is to provide maximum throughput or bandwidth to transfer data from Source to destination the explanation of algorithm is:- According to this module a table is created on basis ok acknowledgement. Hence the transmission is carried out on the basis of this content. Table consists of information regarding node distance, neighboring node, bandwidth. This will help us to select the shortest path with accuracy. As communication starts and data is send. The node which has receive the data, now for further communication will act as source and will transmit the data onwards. Now the table is reviewed. If the last node is busy or massively congested then the choice of nodes will be made on the bandwidth bases which mean the node with highest bandwidth will be selected. In case if both of the nodes with high bandwidth are loaded then the choice is made on the distance bases that is least distant node is selected. This technique is more efficient than previous one on basis of delay reduction fast data transfer and maximum channel availability for data transfer.
Simulink modal for BER analysis OFDM systems
OFDM is one of the applications of a parallel-data-transmission scheme, which reduces the influence of multipath fading and makes complex equalizers unnecessary increase dramatically future wireless communications. OFDM is a particular form of Multi-carrier transmission and is suited for frequency selective channels and high data rates In this project we are going to implement a simulink model design for wireless sensor network standard having OFDM concept. This system will be implemented in simulink toolbox of mat lab and which will have transmitter and receiver with between them standard methodology used for data transmission and at receiver end there will be a analyzer block which will help us to analyze the performance of system on basis of parameters like BER, number of errors etc.
Fading Channel performance analysis
Fading is the term used to describe the rapid fluctuations in the amplitude of the received radio signal over a short period of time. Fading is a common phenomenon in Mobile Communication Channels, where it is caused due to the interference between two or more versions of the transmitted signals which arrive at the receiver at slightly different times. The resultant received signal can vary widely in amplitude and phase, depending on various factors such as the intensity, relative propagation time of the waves, bandwidth of the transmitted signal etc. In this project we have implemented the simulink model for communication data transmitter and receiver and finally Rayleigh fading channel in introduced to check its performance in communication and analysis over BER etc
Multiuser detection in CDMA systems
Multi user detection techniques exploit the structure of MAI to achieve interference suppression and provide substantial performance gains over conventional single user detection techniques. This results in interference between multiple direct-sequence users and is referred as MAI. In our project we study two multi-rate access methods of multi-carrier CDMA system. Decorelator detector is implemented in multi user detection to remove MAI from signal this detector is advanced to Matched filter At the receiving end, it is required to detect the signal of the desired user in the presence of MAI. If some interfering transmitters are located closer to the base station as compared to the desired user, the receiver of the intended user receives more interference in comparison to that it would have received without near far effect. The MAI and near-far problem are the two issues, which need considerable attention for reliable detection of the signal of the desired user. The conventional detector considers MAI as external noise and is referred to as single user detection technique
multiuser detection Comparative analysis
The challenge to enhance the capacity of a CDMA system therefore lies in interference management. Many techniques that control and/or suppress interference in CDMA systems by transmit and/or receiver side processing .In this project we will implement two techniques for multiuser detection these two techniques are for 2 users in CDMA system and will analyze on basis of SNR The two techniques are • Least mean square algorithm • Blind mud algorithm First of all we will generate m sequences for our system. After that we will generate data which is to be send. Then we will follow the step of encoding of data using both algorithms for user detection. After that we will analyze the results on basis of • Mean square error • Signal to noise ratio