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Generally, if the separation between transmitting and receiving antennas is not sufficient, the oscillation of repeater and the interference may occur due to the feedback of the original transmitted signal. Therefore, the interference cancellation system (ICS) should be implemented as the important part of the repeater system for the wireless communication systems in order to eliminate unwanted signals from the corrupted signals in the receiver.
It is necessary to have sufficient isolation between uplinks and downlinks. Otherwise the downlink signals from the high-power amplifier could be input of the uplink, bringing damage to the low-noise amplifier (LNA) parts of the uplinks. Hence, in the case of a wireless repeater that uses the same frequency band for transmitting and receiving signals, the feedback interference signal which originally comes from the service antenna that directly comes into the donor antenna, may cause the problem of feedback oscillation, synchronization error and damage to the devices of the wireless repeater. Thus interference cancellation techniques are imperative and have already been applied as a method to resolve those problems in using the wireless repeater.
1. ICS technologies
ICS (Interference Cancellation System) Repeater is based on ANC (Adaptive Noise Canceller), which uses the output of interference source to optimally estimate the interference value by the coordination of a digital filter and adaptive algorithm. Thus it deducts the estimated interference value from the input mixed with interference, realizing perfect separation of interference and signal.
Adaptive filtering principle
Adaptive filter is based on Least Mean Square Error as a criterion adjusting the filter coefficients by the adaptive algorithm to achieve optimal filtering. In the design of adaptive filter, without knowing the autocorrelation function of the signal and noise in the filtering process, the filter can automatically adapt and adjust to meet the requirements of Least Mean Square Error even when the autocorrelation function of noise and signal changes slowly.
The adaptive filter is proposed against the fixed filter which is a classic filter that the filter frequency band is fixed. But the adaptive filter filtering frequency automatically adapt to the input signal changes. So its applying scope is much broader. The adaptive filter use the parameters of the filter collected at previous moment to adjust the present when there is no prior knowledge of signal and noise. Thus it can adapt to the statistics of unknown signal and noise or random variation characteristics, then achieve the optimal filtering.
LMS algorithm
The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff is an adaptive algorithm, which uses a gradient-based method of steepest descent. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error.
LMS algorithm is relatively simple compared to other algorithms. It does neither require correlation function calculation nor does it require matrix inversions.
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