3D NAND flash memory has attacked widespread attention due to its fast speed, high endurance, and strong reliability. However, its reliability decreases as program and erase time increases. To tackle this problem, current researches mostly employ machine learning models to predict flash memory failure, but there lacks the consideration of using the inter-layer difference and page type difference characteristics in flash memory chips to help failure prediction. Based on the internal characteristic of inter-layer difference and page type difference, two failure prediction algorithms are proposed in the paper, corresponding to the Standard1 and Standard2. For Standard1, an attention focused failure prediction (AFFP) algorithm is proposed. To predict the failure of the entire block, the proposed AFFP algorithm only focuses on the layer which is the most prone to failure and further predicts eight pages of the most likely failure pages within this layer. For Standard2, a low predict-frequency failure prediction (LPFFP) algorithm is proposed, which can reduce the frequency of failure prediction significantly and thus reduce the prediction overhead as much as possible. The experimental results show that, for Standard1, the AFFP algorithm can predict the block of failure accurately, and its data extraction and prediction overheads are reduced by 99.8% compared to the original algorithm, and meanwhile the F1-score exceeds 0.96. For Standard2, the LPFFP algorithm can predict the page of failure within a flash block accurately, and its F1-score exceeds 0.91 with a significant reduction in prediction overhead.

In 3-D NAND flash memory, the channel is always offset due to the complicated interference from the program/erase (PE), including the data retention and layer interference, so that the channel estimation is desired. However, due to the physical structure of 3-D flash memory, there exists significant variation in channel parameters and inconsistent channel offsets among different wordlines. As a result, the process of estimating channel parameters for each individual wordline typically requires a considerable amount of computational resources, resulting in the high latency of system, which becomes a new challenge. To tackle this problem, two unsupervised learning algorithms are proposed to estimate the channel parameters, based on analyzing the error distribution in 3-D flash memory. To address the longer read latency introduced by the unsupervised learning algorithm for the channel estimation, we further propose a low-latency detection algorithm, which first detects whether the current channel needs to be updated. In the event that an update is required, the algorithm only periodically estimates the channel parameters during system idle times, resulting in a more efficient and streamlined process. Compared to the existing methods, the proposed algorithms can efficiently estimate channel parameters with lower-computational complexity. Moreover, combining with the search algorithm, a correction scheme is proposed to minimize the error between the estimated read reference voltage (RRV) and the optimal RRV in the actual device. Theoretical analysis and simulation results demonstrate that the proposed method can improve the lifetime of flash memory and reduces the number of read retries.

Sharing a multiple-input multiple-output (MIMO) radar, the single platform can achieve dual functions of radar and communication (DFRC) within the same frequency spectrum, via the same transmit waveforms. In this paper, a space-time coding scheme is developed for transmit beamforming of DFRC and embedding communication information, without their cross-interference. For transmit beampattern design of DFRC, the shape approximation and integrated power approximation criteria are adopted respectively for waveforms optimization with the constant-envelope constraints of transmit waveforms and the equivalent signal in the communication direction. Based on the space-time coding scheme, the direct constellation mapping (DCM) and phase-rotation constellation mapping (PRCM) methods are proposed to embed information symbols. It turns out that the proposed space-time coding scheme for information constellation mapping can prevent missing information symbols and have better performance in bit-error rate (BER), compared to the existing information-embedding techniques. Moreover, the scheme can reduce the dependence of the communication data rate on radar pulse repetition frequency (PRF). Simulation results are presented to demonstrate the effectiveness of the proposed methods.