To Study Channel Estimation Techniques In Mimo Ofdm System

Sharma, Deepika

doi:10.9790/2834-0234649

Cited by 2 publications

(6 citation statements)

References 1 publication

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“…ISI can also be eliminated if guard interval is added, but it leads us to power loss and increased bandwidth of the system [10]. A drawback of OFDM is that N modulators and filters at transmiter and N demodulators and filters at receiver end make the system very complex, this complexity can be overcome by using FFT and IFT at receiver and transmitter section [9]. Generally OFDM is a combination of N modulated signals, in few cases some OFDM signals are in phase with each other and hence they add up and provides a large output, whereas in some cases OFDM signal components are out of phase and hence get cancelled producing zero output.…”

Section: Ofdm Systemmentioning

confidence: 99%

Comparision of BER’s of QAM and PSK Modulation Techniques for Channel Estimation by using LS Estimator in MIMO-OFDM System

Chauhan¹,

Kanwar²

2017

International Journal of Advanced Research in Computer and Comm

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This paper gives the comparison of the Bit error rates of modulation techniques like QAM and PSK for their different order of modulations (M) and for different values of subcarriers (N) by using LS channelestimator. Comparison of bit error rates of various modulation schemes is done to find out the best suited modulation technique for transmitting N no. of subcarriers. LS estimator is a technique used for channel estimation in MIMO-OFDM system. Channel estimation is done at the receiver for compensating the effect of distortions which are created by the channel. LS channel estimator comes under the block type channel estimation technique.

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Section: Ofdm Systemmentioning

confidence: 99%

Comparision of BER’s of QAM and PSK Modulation Techniques for Channel Estimation by using LS Estimator in MIMO-OFDM System

Chauhan¹,

Kanwar²

2017

International Journal of Advanced Research in Computer and Comm

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“…The baseband OFDM system based on pilot channel estimation is given in Figure (1).On the transmitter side, binary information is grouped and mapped according to chosen modulation. After serial/parallel (S/P) conversion pilots are inserted either to all subcarriers with a specific period or uniformly between the information data.…”

Section: Ofdm System Modelmentioning

confidence: 99%

“…let𝑦 𝑓 (𝑛) = y(received signal), then 𝐼 1 is the real part of y and 𝐼 2 is the imaginary part of y. The input layer plays the role of distributing the input to all neurons in the first processing layer, where, first hidden layer (layer (1)) contains ten neurons and every input in the input layer is connected to every neuron inlayer (1). The output of layer ( 1) is computed as…”

Section: Proposed Design Of Ofdm With Annmentioning

confidence: 99%

“…𝑂 Represents the output of layer (1), W{1} represents the weights that connect the input layer with layer (1). B {1} represents the bias values of layer (1) and 𝑡𝑎𝑛𝑠𝑖𝑔 represents the activation function as mentioned in equation ( 12) for layer (1). 𝑊{1}, 𝐵{1}and 𝑂 can be expressed as…”

Section: Proposed Design Of Ofdm With Annmentioning

confidence: 99%

“…Now OFDM is widely used for high-speed communications over frequency selective channels. OFDM divides the high data rate stream into parallel lower data rate and hence prolongs the symbol duration, thus helping to eliminate Inter Symbol Interference (ISI) [1].…”

Section: Introductionmentioning

confidence: 99%

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OFDM Channel Estimation Based on Intelligent Systems

Jaber¹,

Akkar²,

Hatem³

2014

ETJ

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This work is dedicated to the study of reducing Bit Error Rate (BER) when transferring data in the system Orthogonal Frequency Division Multiplexing (OFDM) by estimating the carrier channel in different ways. The proposal design for Artificial Neural Network (ANN) is considered as a tool to improve performance BER and compared with the traditional method based on the use of the Least Square estimation algorithm (LS) to estimate the impulse response of frequency selective Rayleigh fading channel. A MATLAB 7.14 program is used in simulation.The proposed method which integrates algorithm LS with ANN includes the following: 1. Training the neural network by Back-Propagation (BP) and using the trained neural network with algorithm (LS) to estimate the channel in different paths. 2. Using Resilient Back propagation algorithm (RProp)in the training of the neural network. 3. UsingLevenberg-Marquardt algorithm (LM) in the training of the neural network. 4.The comparison of results between the traditional method and the proposed method when taking BER = 0.001 at various tracks (one path, two path and three path) and showed that there profit of (1.5dB, 2dB, 2dB) between using the traditional method and the proposed method using RProp algorithm and a profit of (2dB,3dB, 2dB) using an algorithm LM. There is also comparison between the performance ofRProp algorithm and LMalgorithm and the results showed that the LM algorithm better thanRProp algorithm.

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To Study Channel Estimation Techniques In Mimo Ofdm System

Cited by 2 publications

References 1 publication

Comparision of BER’s of QAM and PSK Modulation Techniques for Channel Estimation by using LS Estimator in MIMO-OFDM System

Comparision of BER’s of QAM and PSK Modulation Techniques for Channel Estimation by using LS Estimator in MIMO-OFDM System

OFDM Channel Estimation Based on Intelligent Systems

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