An Autocorrelation-Based Interference Rejection Signal Detection Scheme

2011 ◽  
Vol 271-273 ◽  
pp. 833-838
Author(s):  
Cheng Ti Huang ◽  
Ruey Wen Liu ◽  
Hou Jun Wang ◽  
Cheng Lin Yang
Keyword(s):  
Signal Detection ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Interference Rejection ◽  
Mean Square ◽  
Detection Scheme ◽  
High Detection ◽  
Mmse Estimator ◽  
Simulation Results ◽  
Detection Quality

This presentation proposed an autocorrelation-based signal detection scheme to get a better resulting. The signal detection scheme is combined by Autocorrelation Filter and an improved linear Minimum Mean Square Error (MMSE) estimator. The Autocorrelation Filter is used to reject the interference, and the improved linear MMSE estimator is used to get the least error. Both two methods are based on the non-overlapping property of the signals in autocorrelation domain. The theory consequence and Simulation results indicate that this signal detection scheme can achieve a high detection quality.

scholarly journals A Novel Signal Detection Algorithm for Underwater MIMO-OFDM Systems Based on Generalized MMSE

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Gaoli Zhao ◽  
Jianping Wang ◽  
Wei Chen ◽  
Junping Song
Keyword(s):  
Computational Complexity ◽  
Signal Detection ◽  
Mean Square Error ◽  
High Performance ◽  
Minimum Mean Square Error ◽  
Multipath Fading ◽  
Detection Algorithm ◽  
Mean Square ◽  
Ofdm System ◽  
Mimo Ofdm

The MIMO-OFDM system fully exploits the advantages of MIMO and OFDM, effectively resisting the channel multipath fading and inter-symbol interference while increasing the data transmission rate. Studies show that it is the principal technical mean for building underwater acoustic networks (UANs) of high performance. As the core, a signal detection algorithm determines the performance and complexity of the MIMO-OFDM system. However, low computational complexity and high performance cannot be achieved simultaneously, especially for UANs with a narrow bandwidth and limited data rate. This paper presents a novel signal detection algorithm based on generalized MMSE. First, we propose a model for the underwater MIMO-OFDM system. Second, we design a signal coding method based on STBC (space-time block coding). Third, we realize the detection algorithm namely GMMSE (generalized minimum mean square error). Finally, we perform a comparison of the algorithm with ZF (Zero Forcing), MMSE (minimum mean square error), and ML (Maximum Likelihood) in terms of the BER (bit error rate) and the CC (computational complexity). The simulation results show that the BER of GMMSE is the lowest one and the CC close to that of ZF, which achieves a tradeoff between the complexity and performance. This work provides essential theoretical and technical support for implementing UANs of high performance.

Efficient Beam-Forming for Maximum Outage of Cognitive Relay Networks

2014 ◽  
Vol 926-930 ◽  
pp. 2321-2324
Author(s):  
Zi Jun Liu ◽  
Zhan Gao ◽  
Guo Xin Li ◽  
Hai Tao Zhang
Keyword(s):  
Mean Square Error ◽  
Multiple Antennas ◽  
Minimum Mean Square Error ◽  
Relay Networks ◽  
Beam Forming ◽  
Mean Square ◽  
Cognitive Relay Networks ◽  
Optimal Beam ◽  
Simulation Results ◽  
Theoretical Results

We consider the scenario of cognitive relay networks, where the cognitive relay is equipped with multiple antennas and the cognitive destinations have only one antenna due to the size and cost limitations. Aiming to maximize the signal-to-interference noise ratio (SINR), we develop the optimal beam-forming scheme for the relay case. The proposed scheme is based on minimum mean square error (MMSE). The theoretical results are validated by simulations. Simulation results show that the proposed scheme has a considerable performance.

scholarly journals Improved Signal Detection Techniques for QOSTBC System in Fast Fading Channel

2020 ◽  
Vol 1 ◽  
pp. 6-12
Author(s):  
Jyoti P. Patra ◽  
Poonam Singh
Keyword(s):  
Signal Detection ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Decision Feedback ◽  
Decision Feedback Equalizer ◽  
Mean Square ◽  
Zero Forcing ◽  
Decision Feedback Equalization ◽  
Interference Cancelation ◽  
Detection Schemes

Most existing quasi-orthogonal space time Block coding (QO-STBC) schemes have been developed relying on the assumption that the channel is at or remains static during the length of the code word symbol periods to achieve an optimal antenna diversity gain. However, in time-selective fading channels, this assumption does not hold and causes intertransmit-antenna-interferences (ITAI). Therefore, the simple pairwise maximum likelihood decoding scheme is not sufficient to recover original transmitted signals at the receiver side. To avoid the interferences, we have analyzed several signal detection schemes, namely zero forcing (ZF), two-step zero forcing (TS-ZF), minimum mean square error (MMSE), zero forcing - interference cancelation - decision feedback equalizer (ZF-IC-DFE) and minimum mean square error - interference cancelation { decision feedback equalizer (MMSE-IC-DFE). We have proposed two efficient iterative signal detection schemes, namely zero forcing - iterative interference cancelation - zero forcing { decision feedback equalization (ZF-IIC-ZF-DFE) and minimum mean square error - parallel interference cancelation - zero forcing – decision feedback equalization (MMSE-IIC-ZF-DFE). The simulation results show that these two proposed detection schemes significantly outperform all conventional methods for QOSTBC system over time selective channel.

scholarly journals An Efficient Indoor Positioning Method with the External Distance Variation for Wireless Networks

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1949
Author(s):  
Ching-Mu Chen ◽  
Yung-Fa Huang ◽  
You-Ting Jheng
Keyword(s):  
Mean Square Error ◽  
Reference Point ◽  
Minimum Mean Square Error ◽  
Indoor Positioning ◽  
Reference Points ◽  
Variation Method ◽  
Average Error ◽  
Small Range ◽  
Mean Square ◽  
Simulation Results

This study strengthens the external distance variation for the indoor positioning performance. With the received signal strength (RSS) of the unknown node, a localization is performed to positioning its coordinates. The mean square error (MSE) of localization is deteriorated by the shadowing effect and the MSE depends on the location of reference nodes. Moreover, the minimum mean square error (MMSE) algorithm is also used with the RSS. The amount of variation in the distance between the reference point and the positioning node will also affect the accuracy. Therefore, this paper considers the distance between the reference point and the positioning node and also the distance variation between the reference points. MSE is used to estimate positioning performance and Monte Carlo is also used to simulate the average error of different shadowing and decay environments. When reference nodes have known distances, the distance is obtained separately and the estimated distances are identified by the MMSE method. In order to reduce the number of reference nodes and calculation cost, this paper uses adaptive reference node selection to improve the accuracy of positioning. Simulation results show that the external distance variation mechanism strengthens the indoor positioning performance. Moreover, this paper investigates the performance of several reference nodes (three, four, five, and six reference nodes) through 3D graphs to estimate the small range area. The differences are more clearly observed with fewer reference nodes and lower MSE. Finally, simulation results show that the MSE value of fixed three reference nodes is almost 100% better with external distance variation method compared to the random selected three reference nodes.

LMMSE Equalization Algorithm Research Based on 0.5chip Level

2014 ◽  
Vol 701-702 ◽  
pp. 974-978
Author(s):  
Shan Ling Liu
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Mean Square ◽  
Spreading Codes ◽  
Equalization Algorithm ◽  
Simulation Results ◽  
Performance Of Algorithm ◽  
Better Than ◽  
Inter Symbol Interference

The LMMSE equalization filter based on minimum mean square error (MMSE) criterion can effectively suppress the inter-symbol interference caused by multi-path and recover the orthogonality among the spreading codes. In this paper, LMMSE equalizer with 0.5chip level algorithm is studied in the downlink of the UTRA HSPA+ system and the simulation results show that the performance of algorithm with 0.5chip level is better than the algorithm with one chip level.

Cancelable Biometric System for face Based on linear minimum mean square error Model.

2019 ◽  
Vol 28 (1) ◽  
pp. 145-152
Author(s):  
Abd El-aziz Ebrahim Hsaneen ◽  
EL-Sayed M. El-Rabaei ◽  
Moawad I. Dessouky ◽  
Ghada El-bamby ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Error Model ◽  
Mean Square ◽  
Biometric System

Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology

2021 ◽  
Vol 11 (9) ◽  
pp. 3763
Author(s):  
Yunlong Zou ◽  
Jinyu Zhao ◽  
Yuanhao Wu ◽  
Bin Wang
Keyword(s):  
Mean Square Error ◽  
Selection Process ◽  
Minimum Mean Square Error ◽  
Correlation Coefficients ◽  
False Alarms ◽  
Mean Square ◽  
Segmentation Method ◽  
Space Objects ◽  
High Earth Orbits ◽  
Star Images

Space object recognition in high Earth orbits (between 2000 km and 36,000 km) is affected by moonlight and clouds, resulting in some bright or saturated image areas and uneven image backgrounds. It is difficult to separate dim objects from complex backgrounds with gray thresholding methods alone. In this paper, we present a segmentation method of star images with complex backgrounds based on correlation between space objects and one-dimensional (1D) Gaussian morphology, and the focus is shifted from gray thresholding to correlation thresholding. We build 1D Gaussian functions with five consecutive column data of an image as a group based on minimum mean square error rules, and the correlation coefficients between the column data and functions are used to extract objects and stars. Then, lateral correlation is repeated around the identified objects and stars to ensure their complete outlines, and false alarms are removed by setting two values, the standard deviation and the ratio of mean square error and variance. We analyze the selection process of each thresholding, and experimental results demonstrate that our proposed correlation segmentation method has obvious advantages in complex backgrounds, which is attractive for object detection and tracking on a cloudy and bright moonlit night.

scholarly journals An Estimator for Weather Radar Doppler Power Spectrum via Minimum Mean Square Error

2021 ◽  
pp. 1-16
Author(s):  
Eiichi Yoshikawa ◽  
Naoya Takizawa ◽  
Hiroshi Kikuchi ◽  
Tomoaki Mega ◽  
Tomoo Ushio
Keyword(s):  
Power Spectrum ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Weather Radar ◽  
Mean Square ◽  
Doppler Power
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