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What are the most common techniques for estimating wireless channel parameters?

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Wireless channel parameters are essential for designing and optimizing mobile communications systems. They describe how the signals propagate, fade, and interfere in different environments and scenarios. Estimating wireless channel parameters accurately and efficiently can improve the performance, reliability, and capacity of wireless networks. In this article, you will learn about some of the most common techniques for estimating wireless channel parameters and their advantages and limitations.

本文章的要点总结

Pilot-based estimation:

This method uses known signals, or pilots, to measure the wireless channel response. It's like having guideposts along the way to ensure the data travels accurately, though it does take up a bit of your bandwidth.
Semi-blind estimation:

It's a mix-and-match approach using fewer guideposts and some smart guessing to refine data travel paths. It’s efficient and still pretty accurate, making it a solid middle ground when resources are tight.

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hossein seilany

M.S. in Computer Science,founder of…
Pekka Ollikainen

Sustainable Digitalization

1 Pilot-based estimation

Pilot-based estimation is a technique that uses predefined symbols or sequences, known as pilots, to estimate the channel parameters at the receiver. The pilots are inserted periodically or randomly into the transmitted signal and are used as references to measure the channel response. Pilot-based estimation can achieve high accuracy and low complexity, but it also introduces some drawbacks. First, it reduces the data rate and the spectral efficiency, since some of the bandwidth is allocated to the pilots. Second, it may suffer from pilot contamination, which occurs when the pilots from different transmitters interfere with each other.

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hossein seilany

M.S. in Computer Science,founder of Emperor-OS, Little-Psycho, Predator-OS,Hubuntu Linux. A few of my experiences and Certificates:Linux/Win Sysadmin, UX/UI, Front-End, SEO, Graphic Designer, React Js
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hi dear. the best technique that shows the following parameters: Frequency measured in Hertz by using the iw and wavemon commands.

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Pekka Ollikainen

Sustainable Digitalization
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Fixed training is used in GSM at frame level. It is the best available to mid bandwidth system, relatively low drop of spectral efficiency. OFDM modulation as low data need not use such as received signal level is the estimate. It requires a bit more complicated modulation/demodulation microchipsbut is efficient and widely used solution in varying channels with other normal mitigation techniques.

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2 Blind estimation

Blind estimation is a technique that does not rely on any pilots or prior knowledge of the channel or the signal. Instead, it exploits some inherent properties or statistics of the signal, such as sparsity, cyclostationarity, or constant modulus, to estimate the channel parameters. Blind estimation can improve the data rate and the spectral efficiency, since it does not waste any bandwidth on the pilots. However, it also faces some challenges. First, it requires more computational complexity and processing time, since it involves solving optimization problems or iterative algorithms. Second, it may be sensitive to noise, interference, or signal variations, which can degrade the estimation accuracy.

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Pekka Ollikainen

Sustainable Digitalization
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Use Blind estimation is not really a problem since computation is relatively easy and power efficient solution in varying channels. Fixed channels do not need to have more than signal level estimate, or adjustable phase corrections, in addition to normal mitigation techniques against radom noise and interference

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3 Semi-blind estimation

Semi-blind estimation is a technique that combines both pilot-based and blind estimation methods. It uses a small number of pilots to provide some initial information or constraints for the channel estimation, and then applies blind estimation techniques to refine the results. Semi-blind estimation can balance the trade-off between accuracy and efficiency, since it uses fewer pilots than pilot-based estimation and less complexity than blind estimation. However, it also depends on the quality and the placement of the pilots, as well as the choice and the convergence of the blind estimation algorithms.

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4 Machine learning-based estimation

Machine learning-based estimation is a technique that uses data-driven and learning-based approaches to estimate the channel parameters. It can leverage various types of machine learning models, such as artificial neural networks, support vector machines, or decision trees, to learn the mapping between the input signal and the output channel parameters. Machine learning-based estimation can adapt to different channel conditions and scenarios, since it can learn from historical or real-time data. It can also handle nonlinear and high-dimensional channel models, which may be difficult for conventional estimation methods. However, it also requires a large amount of training data and computational resources, which may not be available or feasible in some cases.

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Pekka Ollikainen

Sustainable Digitalization
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Machine learning-based estimation should be connected to station locations for better estimates from past channel evaluations, added with time-dependent vector estimates to Machine learning system. Once the essential parameters are found the machine learning may be concanated into a simple regression type linearization estimate even on non-linear effects; and lead to an easy implementation. Studies are still ongoing..

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5 Compressive sensing-based estimation

Compressive sensing-based estimation is a technique that uses the concept of compressive sensing to estimate the channel parameters. Compressive sensing is a theory that states that a sparse signal can be recovered from a small number of linear measurements, as long as the measurement matrix satisfies certain conditions. Compressive sensing-based estimation can exploit the sparsity of the channel, which means that only a few components or paths are dominant in the channel response. It can reduce the number of measurements and the complexity of the estimation, while maintaining a high accuracy. However, it also requires the knowledge or the estimation of the sparsity level and the support of the channel, which may not be easy or accurate in some cases.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Pekka Ollikainen

Sustainable Digitalization
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All the channel estimation success depends on data bandwidth related to carrier signal frequency, and environment around the transmitter and receiver; and the type of modulation applied with those channel coding, error correction etc mitigation techniques; and finally on the communication quality requirements. Changing one parameter changes the channel performance. Different estimation techniques are efficient for different types of channels. Optical ( radio) communication is still another story.. but follows mainly the same principles

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What are the most common techniques for estimating wireless channel parameters?

1

2

3

4

5

6

1 Pilot-based estimation

2 Blind estimation

3 Semi-blind estimation

4 Machine learning-based estimation

5 Compressive sensing-based estimation

6 Here’s what else to consider

Mobile Communications

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What are the most common techniques for estimating wireless channel parameters?

1

2

3

4

5

6

1 Pilot-based estimation

2 Blind estimation

3 Semi-blind estimation

4 Machine learning-based estimation

5 Compressive sensing-based estimation

6 Here’s what else to consider

Mobile Communications

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