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As an example, the interference cancellation method described in [11] depends on the block-size K and as a consequence its complexity increases with the block-size.
Table 1 compares the computational complexity of our proposed self-SIC method to that of the interference cancellation method proposed in [11].
In this paper, we implement the noise cancellation method in FPGA as hardware to perform real-time speech enhancement.
Subsequently, the interference cancellation method is performed by block processing.
In [4], the interference cancellation method is further extended to a turbo Volterra architecture designed for an arbitrary number of carriers.
In this section, we extend our non-idealities model to include RF non-linearities, providing some clues as to why the joint cancellation method starts failing at high transmit powers.
In order to evaluate the impact of the ICI cancellation methods on the final user quality of experience (QoE), throughput estimations have been carried out using the information conveyed in the bursts.
The proposed method and the impulsive noise cancellation method in [30] are similar as they have two stages: the noise detection and the signal estimation stages.
In this paper, the performance of the adaptive noise cancellation method is evaluated on several possible dual microphone system (DMS) configurations.
In simulation analysis, the adaptive noise cancellation method can increase the SNRs (Signal to Noise Ratios) of leak signals as high as 16 dB.
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