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and a memory length of M+L ′.
Several adaptation procedures have been discussed by changing the memory length of signal [7 13].
Therefore, the memory length of the complete Volterra model can be extremely high (M=τ f S >106).
The new memory length of the received signal model is also increased from M to M+L.
However, the performance of existing receive beamforming methods is highly sensitive to the memory length of time domain equalizer.
The multiplying constant α defines the memory length of the algorithm; obviously, for α<1, the recursion (13) is asymptotically stable.
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Note that as the channel memory lengths of the underlying channel links increase, the complexity of MMSE-ZP-OFDM, M-ZP-OFDM, and the LC-ZP-OFDM receiver increases.
Using the Hopfield neural network (HNN) [15] as foundation, this equalizer has complexity quadratic in the data block length and approximately independent of the channel memory length for practical systems.
The choice of memory length, L, of the kernel, sets the resolution of the density function.
Throughout this paper, we follow a realistic assumption that, where is the memory-length of the Markov process.. Every state,, of this model can transit to only two other states: either to state (even state) or to state (odd state).
Given a partial trajectory S, it is required to ptrdict the next road segment σ. PGST here serves as Markov model of memory length equal to height (h) of the PGST trained over edges of road network.
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