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In millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and increase spectral efficiency significantly. To reduce the hardware complexity and energy consumption of massive MIMO systems, hybrid architecture has attracted extensive attention. Channel estimation and beamformer design based on the hybrid architecture are two critical techniques. Motivated by the advantages of sparse recovery, we propose an algorithm based on compressive sensing (CS) for channel estimation to estimate the dominant paths in the sparse mmWave channel. Moreover, benefited by the limited components of multipath in the angle-domain, an overhead-efficient method for hybrid beamformer design without singular value decompose (SVD) for obtaining the optimal hybrid precoder is proposed followed by the mmWave channel estimation, and only the indices of orthogonal array response vectors of the estimated dominated paths need to be transmitted as the feedback information for hybrid precoder design. Numerical experiments demonstrate that the proposed algorithm for channel estimation can recovery the channel matrix by estimating the parameters of dominant paths. Through the theoretical analysis and numerical experiments, we prove that the proposed algorithm for hybrid beamformer design can perform near-optimal spectral efficiency. Finally, we analyze the feedback overhead cost by the proposed algorithm and prove that the proposed algorithm can significantly reduce the feedback overhead.

Millimeter wave (mmWave) communications are considered as a key technology for 5G due to the huge spectrum resources which can't be provided in traditional frequency band. However, traditional full digital precoding methods are difficult to implement in massive multiple-input-multiple-output (MIMO) systems due to high cost and high power consumption. Nevertheless, mmWave hybrid precoding architecture achieves a good balance between performance and cost. Inspired by the matching pursuit (MP) algorithm, we propose a near-optimal hybrid precoding algorithm for fully-connected hybrid architecture. Instead of searching the analog vectors from the set of the array response vectors, we successively construct the appropriate analog precoding vectors by the alternating iteration method to constitute the analog precoding matrix, and the baseband precoding matrix is derived by regular least squares. In addition, the proposed algorithm takes no assumptions or approximations. Numerical simulations show that the proposed algorithm outperforms previous methods in performance with lower computational complexity.

In multi-user millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and spectral efficiency significantly. To reduce the hardware complexity and energy consumption of massive MIMO systems, hybrid precoding as a crucial technique has attracted extensive attention. Most previous works for hybrid precoding developed algorithms based on optimization or exhaustive search approaches that either lead to sub-optimal performance or have high computational complexity. Motivated by the thought of cross-fertilization between Data-driven and Model-driven approaches, we consider applying deep learning approach and introduce the Hybrid Precoding Network(HPNet), which is a compressed deep neural network exploiting the feature extracting (thanks to convolutional kernels) and generalization ability of neural networks and the natural sparsity of mmWave channels. The HPNet takes imperfect channel state information (CSI) as the input and predicts the analog precoder and baseband precoder for multi-user massive MIMO systems. Moreover, in order to make the approach more practical in real scenarios, we further introduce a model compression algorithm, using network pruning, to greatly reduce the computational complexity and memory usage of the neural network while almost retaining the model performance and then assess the influence of pruned parameters in the network. Numerical experiments demonstrate that HPNet outperforms state-of-the-art hybrid precoding schemes with higher performance and stronger robustness. Finally, we analyze and compare the computational complexity of different schemes.

In multi-user millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and spectral efficiency significantly. In this paper, we focus on the maximization of the weighted sum rate and the global optimization of the max-min fairness problem, which are not only important but also very hard to find the global optimal solutions due to their nonconvexity property. Some previous works on the power allocation for maximization of the weighted sum rate usually proposed methods that either have high complexity or achieve sub-optimal performance. And there are few previously developed algorithms for finding the global optimal solution of the max-min fairness optimization problem. Based on a quadratic transform (QT) technique, which is a novel fractional programming (FP) approach, we introduce efficient iterative algorithms with low complexity to find optimal solutions of the two typical and difficult power allocation problems in wireless communications. Simulation evaluations demonstrate that our proposed algorithms are able to provide the optimal solutions and outperform previous methods in both performance and computational complexity.

In this paper, we propose an end to end model for keyword spotting(KWS) with densely connected convolutional network(DenseNet) and the integration of Short Time Fourier Transform(STFT) which aims to extract utterances from raw waveform directly. Furthermore, we investigate the efficiency of adaptive and trainable window function in the task of keywords spotting. Using the recently-released Google Speech Commands Dataset as our benchmark. Our DenseNet implementation significantly outperforms previous neural networks based KWS system in terms of accuracy.