ed here: Clipping is the most intuitive way to implement CFR. If the signal is above a given reference (clipping) level, it is clipped to that value. In mathematical form: Xclipped[n]=Clipfactor[n]X[n) where Clipfactor[n] = with A being the desired clipping level. This clipping is typically followed by a filtering stage that smooths out the regions around the clipped area to reduce unwanted out-of-band components in the signal. Clipping and filtering works well with any signal type but is most efficient with con- tiguous signals composed of similar equal power carriers (e.g., 5 X LTE 20 MHz in the same band). Complexity depends on max allowed number of local peaks to cancel per pass. It is appropriate for medium-large instantaneous bandwidth (IBW) where performance can be 80 Open Radio Access Network (O-RAN) Systems Architecture and Design traded off against complexity. Expect to need to tune parameters per composite signal configura- tion. Higher complexity versions of this algorithm can use time-to-frequency domain conversion (FFT) to implement complex filtering in the frequency domain. Peak windowing scales the peaks in the signal together with the surrounding areas using a weighted window function. After detection of the peak, define a set of samples around it as the "peak region" and scale this peak region in a weighted manner with the maximum reduction being enough to ensure that the reduced peak is within the desired amplitude range. The sigmoid method uses a modified sigmoid transfer to smooth out detected peaks, in mathematical form: Xsigmoia[n]=Sigmoid[n|X[n] where Sigmoid[n] = .\||||||||| K is a shaping factor to control the smoothness of the transfer function and A is the shap- ing threshold above which the sigmoid function starts to be applied. Peak cancellation reduces the peaks in the signal by building a secondary "cancellation pulse" signal for each detected peak that is subtracted from the original signal. The cancellation pulse is shaped by a low-pass filter to ensure that it has few out-of-ban