This paper investigates direct and indirect learning methods to develop deep learning digital predistortion (DL-DPD) models and apply the models to improve the linearity of a power amplifier (PA). The two methods are applied to class-AB and class-$\mathrm{F}^{-1}$ PAs designed with gallium nitride (GaN) on silicon carbide (SiC) high electron mobility transistors (HEMTs). The simulation results show that both direct and indirect DL-DPD methods improve the linearity of the class-AB PA by about 12dB and the class-$\mathrm{F}^{-1}$ PA by 11 dB, while the indirect method offers marginally better performance. The paper shows the direct learning method leads to significant improvement of the DL-DPD method based over the memory polynomial. It also presents the advantages of a BiLSTM based on the neural network architecture to design direct/indirect DPDs. Finally, it demonstrates that the DL-DPD can improve the linearity of class-AB and class-$F^{-1}$ PAs without architectural changes.

Existing power analysis techniques rely on strong adversary models with prior knowledge of the leakage or training data. We introduce side-channel analysis with unsupervised learning (SCAUL) that can recover the secret key without requiring prior knowledge or profiling (training). We employ an LSTM auto-encoder to extract features from power traces with high mutual information with the data-dependent samples of the measurements. We demonstrate that by replacing the raw measurements with the auto-encoder features in a classical DPA attack, the efficiency, in terms of required number of measurements for key recovery, improves by 10X. Further, we employ these features to identify a leakage model with sensitivity analysis and multi-layer perceptron (MLP) networks. SCAUL uses the auto-encoder features and the leakage model, obtained in an unsupervised approach, to find the correct key. On a lightweight implementation of AES on Artix-7 FPGA, we show that SCAUL is able to recover the correct key with 3,700 power measurements with random plaintexts, while a DPA attack requires at least 17,400 measurements. Using misaligned traces, with an uncertainty equal to 20 percent of the hardware clock cycle, SCAUL is able to recover the secret key with 12,300 measurements while the DPA attack fails to detect the key.

Exact spread-spectrum code tracking in the presence of multipath is an important issue in distance and time measurement systems. Different tracking algorithms based on feedback, feedforward or a combination of them reduce multipath effects, however, the accuracy of the tracking in presence of short-delay multipath signals is still a problem. In this paper, we consider feedback delay lock loops (DLL) and gating function schemes that have shown better performance with lower complexity under multipath. We develop a shot-noise process model for the control signal of a DLL in the presence of multipath signals and determine the range of tracking errors they cause. Then, we propose a method based on computation of parallel forward and backward correlations with the incoming signal in order to reduce tracking errors due to multipath. As will be shown, the multipath signals do not change the zero-crossing of the forward correlation output which is used to estimate the exact phase of the received signal. The new structure alleviates the problem of short-delay multipath while adding not much complexity to the system.