Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as a potential candidate for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method

Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments

Hybrid massive MIMO structures with reduced hardware complexity and power consumption have been widely studied as a potential candidate for millimeter wave (mmWave) communications. Channel estimators that require knowledge of the array response, such as those using compressive sensing (CS) methods, may suffer from performance degradation when array-inherent impairments bring unknown phase errors and gain errors to the antenna elements. In this paper, we design matrix completion (MC)-based channel estimation schemes which are robust against the array-inherent impairments. We first design an open-loop training scheme that can sample entries from the effective channel matrix randomly and is compatible with the phase shifter-based hybrid system. Leveraging the low-rank property of the effective channel matrix, we then design a channel estimator based on the generalized conditional gradient (GCG) framework and the alternating minimization (AltMin) approach. The resulting estimator is immune to array-inherent impairments and can be implemented to systems with any array shapes for its independence of the array response. In addition, we extend our design to sample a transformed channel matrix following the concept of inductive matrix completion (IMC), which can be solved efficiently using our proposed estimator and achieve similar performance with a lower requirement of the dynamic range of the transmission power per antenna. Numerical results demonstrate the advantages of our proposed MC-based channel estimators in terms of estimation performance, computational complexity and robustness against array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS channel estimator.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

The Matthew Effect of a Fault Classification Mechanism and Its Application

When using the classification algorithm to classify a single sample, the classification accuracy often cannot achieve an ideal effect. To solve this problem, the following two aspects of research work are carried out and presented in this paper. On the one hand, according to the memory characteristics of mechanical faults, a voting classification mechanism for the sample sequence to be classified is proposed. It is found that the classification mechanism of the sample sequence to be classified with memory has the Matthew effect of accumulated advantage. Using this effect, one can improve the accuracy of fault classification. On the other hand, because the length of the sample sequence to be classified increases, the delay of the classification results increases. To solve this problem, the classification algorithm is optimized to minimize the delay on the assumption that the classification accuracy meets the expected requirements

Three-Level Supply Chain Coordination under Disruptions Based on Revenue-Sharing Contract with Price Dependent Demand

Considering the market demand is stochastic and dependent on price, this paper shows that the revenue-sharing contract could coordinate a three-level supply chain consisting of one manufacturer, one distributor, and one retailer under normal environment. However, the original revenue-sharing contract cannot coordinate the supply chain under disruptions in circumstances of certain incidents leading to significant changes in market demand and causing additional deviation costs. To solve the problem, this essay introduces two improved forms of revenue-sharing contract: a mixed contract form based on a quantity discount policy and a pure form, which are characterized by antidisruption ability. The model of improved revenue-sharing contract is optimized when the market demand is in the additive form or in the multiplicative form with price dependent demand. Formulas are given to calculate the optimal contract parameters. Finally, this essay demonstrates the accuracy of the model of improved revenue-sharing contract with the help of numerical examples