To ensure link reliability and signal receiving quality, robust adaptive beamforming (RAB) is vital important in mobile communications. In this paper, we propose a new RAB algorithm based on interference-plus-noise covariance (INC) matrix reconstruction and steering vector (SV) estimation. In this method, the INC matrix is reconstructed by estimating all interferences SVs and powers, as well as the noise power. The interference SVs are estimated by using the Capon spatial spectrum together with robust Capon beamforming principle, subsequently the interference powers are estimated based on the orthogonality between different signal SVs. On the other hand, the desired signal SV is estimated via maximizing the beamformer output power by solving a quadratic convex optimization problem. The proposed algorithm only needs to know in advance the array geometry and angular sector in which the desired signal lies. Simulation results indicate that the proposed algorithm outperforms the existing RAB techniques in terms of the overall performance in cases of various mismatches.

In massive multiple-input multiple-output (MIMO) systems, accurate direction-of-arrival (DOA) estimation is critical for the transmitters to conduct downlink precoding. In this paper, we focus on the problem of two-dimensional DOA estimation of incoherently distributed sources in massive MIMO systems using uniform rectangular arrays (URAs). First, with the generalized array manifold, we obtain the beamspace array manifold by performing beamspace transformation on the observed vector of the URA, and establish one beamspace shift invariance structure via appropriate beamforming matrix design and array manifold selection. Second, from the beamspace array manifold, we extract two array manifolds, and derive the other beamspace shift invariance structure. Final, the total least squares approach is used to estimate the nominal azimuth and elevation DOAs. With the DOA estimates, the corresponding angular spreads are given in closed-form solutions. Our approach avoids spectral search and reduces the dimensions in matrix operations, thus, it is more computationally attractive than the existing techniques. Numerical results show that the proposed algorithm achieves performance comparable to that of the conventional estimators in massive MIMO systems.

We propose a novel low complexity two-stage frequency offset estimation (FOE) scheme based on modified Zoom-FFT for square 16/64-quadrature amplitude modulation (QAM) coherent optical systems. The first stage roughly estimates the frequency offset through a small size FFT. After frequency shifting, the modified Zoom-FFT can select and reconstruct the spectrum of interest by time domain average. Since the average provides a good estimate of the amplitudes of the frequency components, we can accurately find the peak after the second-stage FFT. We conduct simulations and compare the normalized frequency variance of the proposed scheme with different algorithms, e.g., the single-stage FFT and the Chirp-Z transform (CZT). The simulation results show that the proposed FOE scheme has the similar normalized frequency variance as other algorithms of $4.0\times 10^{-9}$ and $1.6\times 10^{-9}$ for 16QAM and 64QAM, respectively. The numerical simulations and experimental results demonstrate that the proposed scheme can achieve comparable performance, while the complexity is reduced by 86.6% (81.2%) and 60.9% (61.2%) for 16QAM (64QAM) compared with single-stage FFT and FFT+CZT, respectively.

Double patterning lithography (DPL) technologies have become a must for today's sub-32 nm technology nodes. Currently, there are two leading DPL technologies: self-aligned double patterning (SADP) and litho-etch-litho-etch (LELE). Among them, SADP has the significant advantage over LELE in its ability to avoid overlay, making it the likely DPL candidate for the next technology node of 14 nm. In any DPL technology, layout decomposition is the key problem. While the layout decomposition problem for LELE has been well studied in the literature, only a few attempts have been made to address the SADP layout decomposition problem. In this paper, we present a polynomial time exact (optimal) algorithm to determine if a given layout has SADP decompositions that do not have any overlay at specified critical edges. The previous approaches tried to minimize the total overlay of a given layout, which may be a problematic objective. Furthermore, all previous exact algorithms were computationally expensive exponential time algorithms based on SAT or ILP. Other previous algorithms for the problem were heuristics without having any guarantee that an overlay-free solution can be found even if one exists.

In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder combining a deep neural network and the Adaboost algorithm. Experimental results show that 48 Gb/s with 7% hard-decision forward error correction (HD-FEC) threshold under 65 km standard single mode fiber (SSMF) is achieved with proposed scheme. Moreover, we further experimentally study the Tree depth and the number of Decision Tree, which are the two main factors affecting the bit error rate performance. Experimental research afterwards showed that the effect from the number of Decision Tree as 30 on bit error rate (BER) flattens out under 48 Gb/s for the fiber range from 25 km and 75 km SSMF, and the influence of Tree depth on BER appears to be a gentle point when Tree Depth is 5, which is defined as the optimal depth point for aforementioned fiber range. Compared to the autoencoder based on a Fully-Connected Neural Network, our algorithm uses addition operations instead of multiplication operations, which can reduce computational complexity from 10⁸ to 10⁷ in multiplication and 10⁶ to 10⁸ in addition on the training phase.