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In multiple-input multiple-output (MIMO), millimeter wave (mmWave) is considered as a promising technology for advanced communication over wireless networks due to its rich frequency spectral resources. However, recognizing the mmWave in MIMO remains a complex task that faces the issues like increased propagation loss. Therefore, this paper proposes a new optimization-assisted estimation algorithm to estimate the mmWave channel parameters. The channel estimation and hybrid precoding performance on mmWave massive MIMO system are proposed by adopting optimization process in the codebook design principles. In fact, the existing works have performed uniform distribution of azimuth angles in the codebook design, whereas the proposed work evaluates it as a single objective optimization problem without excluding the angle characteristics. In order to solve the mentioned optimization problem, dragonfly-evaluated gray wolf optimization (DA-GWO) model is introduced that hybridizes the concepts of dragonfly algorithm and GWO, respectively. Finally, the performance of proposed work is compared and validated over other state-of-the-art models with respect to channel state information and error measures. Accordingly, from the analysis, the proposed DA-GWO model concerning (64, 64) combination for 400th channel bandwidth is 80% and 95.53% superior to adaptive channel estimation and projected gradient factorization algorithms.

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Supply chain management is a crucial task of managing large organizations. In a decentralized supply chain each member focuses on maximizing his own profit. As a result of it, the conflict between the manufacturer and the retailers will arise. To avoid this sort of situations, coordination model strike a balancing between the profit of manufacturers and retailers. This paper investigates a two echelon supply chain system which consisting of one manufacturer and multiple retailers. Using the mathematical modeling a coordination model which maximizes the total profit is developed and analyzed for deteriorating items. The optimal pricing and ordering policies of the model are also derived. A sensitivity analysis with respect to the parameters and costs is also presented. This model lower down the total cost of supply chain and increases the general profit. It also improves cooperation for both manufacturer and retailer.