Signal-Power-Based Combination Weight Rule for Distributed Blind Equalization in WSN Model

Chi, Sulin; Shimamura, Tetsuya

doi:10.2299/jsp.27.35

Cited by 2 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, the raised cosine channel model in (28) is used, in which the parameter θ is set to 3.0, 3.4, 3.8, 3.6, and 3.2, for f 1 (n), f 2 (n), ..., f 5 (n), respectively. The common noise variances are set to v 1 (n), v 2 (n), ..., v 5 (n), respectively.…”

Section: Casementioning

confidence: 99%

“…Although a complicated algorithm is sometimes required, resulting in increased computation burden, accurate noise estimation is not guaranteed because such an approach is difficult to use in reality. In [28], a combination weight rule based on the received signal power at each sensor node was proposed to improve the performance of the equalizer where the parameter of interest in a WSN was the transmitted data signal. In [28], the noise variance was not known in advance, but an excellent weight combination was designed so that a better equalization performance than with the Metropolis, relative-degree, and uniform weight combination rules was obtained.…”

Section: Introductionmentioning

confidence: 99%

“…In [28], a combination weight rule based on the received signal power at each sensor node was proposed to improve the performance of the equalizer where the parameter of interest in a WSN was the transmitted data signal. In [28], the noise variance was not known in advance, but an excellent weight combination was designed so that a better equalization performance than with the Metropolis, relative-degree, and uniform weight combination rules was obtained. The authors focus on the performance in a local sensor network.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Blind Equalization Based on the Best Local Network in Networked System

Chi,

Shimamura

2023

Journal of Signal Processing

Self Cite

View full text Add to dashboard Cite

A distributed estimation algorithm is discussed referring to the single-input multiple-output (SIMO) channel model over a wireless sensor network (WSN) to estimate the transmitted data signal blindly. We find a sensor node in the best local sensor network by minimizing the total average deviation from the minimum signal power in the WSN. The signal at the sensor node is used as the input signal for the distributed blind equalizer. We assume two classes of channels in the SIMO channel model, namely, Class 1 of the common channel conditions with different noise variances and Class 2 of the different channel conditions with common noise variances, based on raised cosine channel models. Computer simulations confirm the excellent performance of the proposed method through the evaluation of mean square error and symbol error rate.

show abstract

Section: Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Blind Equalization Based on the Best Local Network in Networked System

Chi,

Shimamura

2023

Journal of Signal Processing

Self Cite

View full text Add to dashboard Cite

show abstract

Network Topology Reconfiguration-Based Blind Equalization over Sensor Network

Sulin,

Tetsuya

2024

Sensors

View full text Add to dashboard Cite

Distributed in-network processing has garnered much attention due to its capability to estimate the unknown parameter of interest from noisy measurements based on a set of cooperating sensor nodes. In previous studies, distributed in-network processing mainly focused on short-distance communication systems, wherein sensor nodes collect certain parameters of interest within their maximum communication distance. In addition, the estimation of certain parameter vectors of interest from noisy measurements, relying heavily on training signals, is achieved with a non-blind distributed estimation algorithm. However, in some applications, acquiring knowledge of training signals beforehand is difficult. Therefore, it is necessary to perform distributed estimation algorithms for receivers without training signals, a concept known as blind distributed estimation. In this paper, the generalized Sato algorithm is used to design the blind equalizer for the signal estimation. In addition, we consider extending the short-distance communication system to a long-distance communication system for an unmanned aerial vehicle (UAV) cooperating with sensor nodes in the wireless sensor network (WSN). In this scenario, the data signal is transmitted from a UAV to the WSN and is received by sensor nodes. However, the performance of the blind equalizer is susceptible to the transmission channel in long-distance communication systems. Here, we present a network topology reconfiguration approach to address the issue of distributed blind equalization. The objective of the proposed method is to discard the influence of ill-channels on the other sensor nodes by detecting ill-channels and redesigning the sensor node weights. Through computer simulation experiments, we evaluated the performance of the blind equalizer using the average mean square error (MSE) and average symbol error rate (SER). In the results of the computer simulation experiments, the blind equalizer using the proposed method outperformed the conventional methods in terms of prediction accuracy and convergence speed.

show abstract

Signal-Power-Based Combination Weight Rule for Distributed Blind Equalization in WSN Model

Cited by 2 publications

References 33 publications

Distributed Blind Equalization Based on the Best Local Network in Networked System

Distributed Blind Equalization Based on the Best Local Network in Networked System

Network Topology Reconfiguration-Based Blind Equalization over Sensor Network

Contact Info

Product

Resources

About