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The Fantastic Journey of Channel Estimation: How to Optimize Transmission Signals?
In wireless communications, channel state information (CSI) refers to the known channel characteristics of the communication link. It describes how a signal propagates from a transmitter to a receiver and represents the combined effects of scattering, fading, and attenuation over distance. Channel estimation is this method. The acquisition of channel status information makes it possible for transmissions to adapt to current channel conditions, which is crucial for achieving reliable communications at high data rates in multi-antenna systems.
CSI needs to be estimated at the receiver, usually quantized and fed back to the transmitter.
Therefore, the transmitter and receiver may have different channel status information. The CSI of the transmitter and the CSI of the receiver are called CSIT and CSIR respectively.
Different types of channel status information
Channel status information is basically divided into two levels: instantaneous CSI and statistical CSI. Instantaneous CSI (or short-term CSI) means that the current channel conditions are known, which can be thought of as knowing the impulse response of the digital filter. This provides the opportunity to adapt the transmitted signal so that the received signal is optimized for spatial multiplexing or low bit error rates.
Statistical CSI (or long-term CSI) means the statistical characteristics of the known channel. This description may include, for example, fading distribution type, average channel gain, direct-view component, and spatial correlation. Like instantaneous CSI, this information can also be used for transmission optimization. In fact, the acquisition of CSI depends on the speed at which channel conditions change. In fast fading systems, channel conditions change rapidly during the transmission of a single information symbol, so only statistical CSI is reasonable.
In contrast, in slow fading systems, instantaneous CSI can be estimated with reasonable accuracy and used for transmission adaptation for a period of time before becoming obsolete.
In real systems, existing CSI usually falls between these two levels; instantaneous CSI has some estimation/quantization error and is combined with statistical information.
CSI estimate
Due to constantly changing channel conditions, instantaneous CSI needs to be estimated in the short term. A popular approach is the so-called training sequence (or pilot sequence), in which a known signal is sent and the combined knowledge of the transmitted and received signals is used to estimate the channel matrix.
By combining the received training signals, the channel matrix can be estimated.
In signal processing, there are multiple strategies to estimate CSI, the most common of which are least squares estimation (LS estimation) and minimum mean square error estimation (MMSE estimation). In addition, with the advancement of deep learning technology, the estimation of channel state information using neural networks has begun to show good performance and can reduce the number of pilot signals required.
Data assistance and blind estimation
In data-assisted methods, channel estimation is based on some known data, such as training sequences or pilot data, which is shared between the transmitter and receiver. While in the blind method, the estimation is based only on the received data without any known transmission sequence.
Data-assisted methods require more bandwidth, but can achieve better channel estimation accuracy than blind estimation.
Therefore, there is an accuracy versus overhead trade-off between data-assisted methods and blind estimation.
Conclusion
In summary, the estimation of channel status information is crucial to the performance of wireless communication systems. Whether through traditional methods or deep learning technology, future research will likely continue to optimize these estimation technologies to cope with the growing number of Data transfer requirements. In the future of wireless communications, how will channel status information affect our quality of life?
