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This paper presents a new MPC algorithm based on multiplexed MPC that can take advantage of the full potential of an existing FPGA design by utilizing the provided 'free' parallel computational channels arising from such pipelining.
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This is the computational opponent-channel model used by Briley et al. (2012) to model electroencephalographic (EEG) responses from human auditory cortex to abrupt shifts in sound-source location and to predict psychophysical measures of spatial acuity.
Kumpf, R. A. & Dougherty, D. A. A mechanism for ion selectivity in potassium channels: computational studies of cation-pi interactions.
In addition, since the channel and equalizer coefficients are assumed known, the computational efforts for channel estimation and equalizer design are not included.
This greatly increases the complexity of the receiver structure and the computational burden of channel estimation [2, 3].
In addition, it definitely increases the gap value as shown in Table 4, which leads to increase the computational complexity of channel encoding and decoding.
For the sake of simplicity, these evaluations ignore the computational complexity of channel interpolation and standard QAM demodulation in (18) because they are common to all considered methods.
One of these estimates error rates to reduce the number of feedback iterations and the computational complexity of channel decoding [4 6].
Therefore, to compare the complexity of MP-SA and MA-SA, we need only compare their computational complexity in channel sparsity detection.
It turns out that the channel estimation requires at each EM iteration the a posteriori probabilities (APPs) of these data symbols, resulting in a high computational complexity when channel coding is present.
Intuitively, when channel conditions are not good, a larger g should be selected for guaranteeing an acceptable MSE, whereas a smaller g should be selected for saving the computational complexity when channel conditions are good enough.
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