In this paper, the minimum mean-square error (MMSE) technique has been used to equalize the recording channel in order to facilitate the application of the Viterbi detector. The resulting performance has been compared with that of the optimal equalization system which yields the minimum probability of error at the output of the Viterbi detector. The results indicate that depending on the constraint used in the MMSE design, the amount of noise correlation varies significantly at the equalizer output, which in turn makes a large difference in the performance of the Viterbi detector. In particular, in the jitter-dominant channel where unconditioned channel noise samples are highly correlated, the monic constraint on the equalizer target response tends to whiten the noise samples at the equalizer output. This results in a significant performance improvement of the monic constraint upon the fixed-energy constraint as well as the popular partial response targets of the form (1-D)(1+D)/sup n/. >

Equalization For Maximum Likelihood Detectors

Physical layers of communication systems using Filter Bank Multicarrier (FBMC) as a modulation scheme provide low out-of-band leakage but suffer from the large Peak-to-Average Power Ratio (PAPR) of the transmitted signal. Two special FBMC schemes are investigated in this paper: the Orthogonal Frequency Division Multiplexing (OFDM) and the Staggered Multitone (SMT). To reduce the PAPR of the signal, time domain clipping is applied in both schemes. If the clipping is not compensated, the system performance is severely affected. To avoid this degradation, an iterative noise cancelation technique, Bussgang Noise Cancelation (BNC), is applied in the receiver. It is shown that clipping can be a good means for reducing the PAPR, especially for the SMT scheme. A novel modified BNC receiver is presented for SMT. It is shown how this technique can be implemented in real-life applications where special requirements must be met regarding the spectral characteristics of the transmitted signal.

https://downloads.hindawi.com/journals/jcnc/2012/382736.pdf

PAPR Reduction of FBMC by Clipping and Its Iterative Compensation

An adaptive antenna testbed for mobile communication applications is briefly described and results from field trials presented. The goal is to provide an experimental demonstration of both transmit and receive digital beamforming supporting SDMA user access. Trials are presented for a typical urban environment with different combinations of user positions and the ability of the employed adaptive algorithm to establish the link and track the channels is investigated alongside the link BER performance. The tracking performance of the adaptive algorithm used for SDMA is also tested for an indoor environment against maximum ratio combining and a fixed grid of beams.

Space division multiple access (SDMA) field trials - Parts I & II : Tracking and BER performance

In the modern wireless Communication system high data rate must require. Orthogonal Frequency Division Multiplexing (OFDM) is well known technique for transmitting large data over radio waves. In the OFDM system, orthogonally placed sub - carriers are used to carry the data from the transmitter end to the receiver end. More bandwidth is required for transmitting data at higher rate. Orthogonal frequency division multiplexing is a special form of multicarrier modulation which is particularly suited for transmission over a dispersive channel. OFDM has gained a tremendous interest in recent years because of its robustness in the presence of severe multipath channel conditions with simple equalization, robustness against Inter-symbol Interference (ISI); multipath fading in addition to its high spectral efficiency. OFDM system used in microwave communication due to high data rate. But main drawback of OFDM system is PAPR (Peak to average power ratio) nothing but several sinusoidal leads. It restricts the efficiency of the transmitter. Numbers of techniques have been proposed in the literature for reducing the PAPR in OFDM systems. All techniques has been discussed in this thesis report and from all techniques two techniques name Tone Reservation & Clipping has been discussed and simulated in this thesis for reducing PAPR in OFDM system.

Different techniques to reduce the papr in ofdm system

Keeping in view the variety of the applications, image denoising still remains the unexplored territory for the researchers. There are many pros and cons in existing denoising algorithms. The two prime cons of image denoising algorithms are (i) Over and under detection of noisy pixels (ii) Low performance at high noise levels. So, in order to overcome these existing issues, a spatially adaptive image denoising via enhanced noise detection method (SAID-END) is proposed for grayscale and color images. The denoising is achieved using a two-stage sequential algorithm, the first stage ensures accurate noise estimation by eliminating over and under detection of noisy pixels. The second stage performs image restoration by considering non-noisy pixels in estimation of the original pixel value. To enhance the accuracy while denoising high-density impulse noise and artifacts, both noise estimation and restoration stages are using a spatially adaptive window (window expands to spatially connected area), the size of the window depends upon the noise level in the vicinity of the reference noisy pixel. The two stages of the proposed method are referred to as (i) Enhanced adaptive noise detection (ii) Non-corrupted pixel sensitive adaptive image restoration. The proposed method is evaluated by two test steps to ensure its versatility and robustness. In the first step, the proposed method is tested on a wide standard data set of color and grayscale images affected by impulse noise and artifacts. The results of proposed method are compared with well-known methods compatible for denoising impulse noise and artifacts. In the second step, the results of proposed method are compared with the recent state of the art algorithms for traditional test images. The result shows that the proposed method outperforms the existing denoising methods when applied to grayscale and color images.

/pdf/spatially-adaptive-image-denoising-via-enhanced-noise-5ebcsu96ux.pdf

Spatially Adaptive Image Denoising via Enhanced Noise Detection Method for Grayscale and Color Images

In a communication system, operation of the transmitter power amplifier is limited to linear range. Input signal with an amplitude more than the transmitter power amplifier linear range results in signal distortion. Hence, the input signal to the transmitter should be with low peak to average power ratio. This paper presents a new method of reducing the peak to average power ratio in OFDM system. The proposed method is based on DCT aided successive addition and subtraction of OFDM symbols inside the single OFDM frame. Performance of the proposed method is evaluated and found to be superior to PTS, SLM techniques.

/pdf/papr-reduction-in-ofdm-systems-4f0aon76mr.pdf

PAPR reduction in OFDM systems

In this work we have applied Adapive Boosting (AdaBoost) for channel estimation of Orthogonal Frequency Division Multiplexing (OFDM) systems. The result of the AdaBoost algorithm was compared with other algorithms such Least Square (LS), Best Linear Unbiased Estimator (BLUE) and Minimum Mean Square Error (MMSE).

/pdf/channel-estimation-algorithms-for-ofdm-systems-2dkyhborhy.pdf

Channel estimation algorithms for OFDM systems

Absolute difference with line detectors for impulse noise identification.Edge preserving context model is used for impulse noise correction.Gaussian kernel is used for edge preserving of the processing pixel. In this article, a new edge preserving contextual model based image restoration technique is proposed for images affected by impulse noise. The proposed restoration technique consists of two stages: noisy pixel identification and restoration. Center sliding window is considered as current processing pixel for both noisy pixel identification and restoration. In the first stage of the proposed technique, we follow an absolute directional difference of the neighborhood pixels to identify the pixels those are affected by impulse noise. We propose an edge preserving contextual model to restore the noisy pixels. The noise correction stage of the proposed scheme depends on the context model of the noise-free pixels in the selected window. The parameters of the contextual model are obtained using a Gaussian kernel. The proposed algorithm is tested on nine benchmark test images. The evaluation of the proposed algorithm is carried out by comparing it against nine competitive state-of-the-art algorithms for impulse noise removal. The proposed algorithm is evaluated using Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM), Non-shifted Edge Ratio (NSER) and Correlation Factor (CF) performance measures. Experimental results corroborate that the proposed algorithm provides better performance than the existing state-of-art impulse denoising methods.

Context model based edge preservation filter for impulse noise removal

Road traffic crashes are the major risk factor in everyone’s day-to-day life due to the fast increase in the number of vehicles. It is now a more challenging task to deal with existing traffic systems with massive traffic. In the current scenario, the Internet of things (IoT) based solutions will make the transport system more intelligent. This solution will help to reduce congestion and increase the safety of everyday people. Fast reporting of driving abnormalities1 will help in preventing the life of the person involved in traffic crashes. Traffic crashes can be addressed using sensors, audio, and video-based analysis. The work presented here is a fast information sharing system with the date and time of the event, the detailed geographical location with Google map URL, speed, and the path traced using global positioning system (GPS) data. The fast reporting system uses various sensors to collect the vehicle data and notifies the driving abnormality, whenever any abnormal situation occurs. It will help the rescue team, insurance people, or relatives for easy navigation to the desired location. The limitations of communication bandwidth requirement and the powerful central processor may reduce detection performance; however, fast reporting could save many precious lives from danger.1 In this article, the abnormality is the behavior of the vehicle when it meets an accident while on the move.

http://dspace.nitrkl.ac.in:8080/dspace/bitstream/2080/3544/1/SahooG_Hydcon2020.pdf

An IoT-Based Intimation and Path Tracing of a Vehicle Involved in Road Traffic Crashes

In this paper, an iterative adaptive unsymmetric trimmed shock filter based on partial differential equations (PDE) is proposed to remove high-density salt-and-pepper noise by preserving the edge details in the images. This algorithm consists of two steps: identification of pixels affected by the salt-and-pepper noise and recovery of these noisy pixels using adaptive unsymmetric trimmed shock filter. Shock filter has been used in image processing literature for image restoration and enhancement task. However, its use was limited to unwanted noise removal, enhancement, despeckling, etc. In the proposed work, a modified form of shock filter termed as adaptive unsymmetric trimmed shock filter is designed to remove the salt-and-pepper noise. The proposed algorithm is tested with different test images. Three performance evaluation measures: PSNR, CR and MSSIM are used to validate the proposed scheme. The performance of the proposed algorithm claims better results than the other considered eight existing state-of-the-art techniques.

Prasanta Kumar Pradhan

Papers

PAPR reduction in OFDM systems

Channel estimation algorithms for OFDM systems

Context model based edge preservation filter for impulse noise removal

An IoT-Based Intimation and Path Tracing of a Vehicle Involved in Road Traffic Crashes

Iterative Adaptive Unsymmetric Trimmed Shock Filter for High-Density Salt-and-Pepper Noise Removal