Patent ID: 11966587
Assignee: FUZHOU UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 2:
3. The method for optimizing a Polar-RNNA quantizer of MLC NAND flash memory based on deep learning according to claim 1, wherein Step S4 specifically comprises:
defining f0({circumflex over (L)}) and f1({circumflex over (L)}) as probability density functions of LLRs, corresponding to c=0 and c=1 in the memory cells respectively; and reversing all LLR symbols of c=0, wherein a probability density of the LLRs after channel symmetrization is:

fs({circumflex over (L)})=1/2 (f0(−{circumflex over (L)})+f1({circumflex over (L)}))   (3)

defining aN(i) as a probability density function of the LLR of an ith sub-channel;
then, calculating aN(i) of each sub-channel according to formulas a2N(2i-1)=aN(i) ⊙ aN(i), a2N(2i)=aN(i)*aN(i), a1(1)=fs({circumflex over (L)}); calculating a code error probability of each sub-channel after aN(i) of each sub-channel is obtained; and, P
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adding K minimum Pe(i) through a discretized density evolution method to obtain a block error rate formula, P
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