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Aiming at the problem of computational complexity of channel estimation, this paper proposes a low-complexity block matching pursuit (BMP) algorithm based on antenna grouping and block sparsity for frequency division duplex (FDD) massive Multiple-input Multiple-output orthogonal frequency division multiplexing (OFDM) systems. The system coherence time may be exceeded as a result of time consumption when adopting an orthogonal pilot symbol in the time domain. To solve this problem, an antenna grouping transmission scheme is proposed to reduce the total channel estimation time by sacrificing the observed data length. The simulation results show that the proposed BMP algorithm has good anti-noise performance, and it can accurately determine the non-zero position of the sparse vector and adaptively determine the sparsity of the channel, which effectively translates to improved channel estimation performance and better overall system performance than the existing algorithms.
Waleed Shahjehan; Syed Waqar Shah; Jaime Lloret; Antonio Leon. A Low Rank Channel Estimation Scheme in Massive Multiple-Input Multiple-Output. Symmetry 2018, 10, 507 .
AMA StyleWaleed Shahjehan, Syed Waqar Shah, Jaime Lloret, Antonio Leon. A Low Rank Channel Estimation Scheme in Massive Multiple-Input Multiple-Output. Symmetry. 2018; 10 (10):507.
Chicago/Turabian StyleWaleed Shahjehan; Syed Waqar Shah; Jaime Lloret; Antonio Leon. 2018. "A Low Rank Channel Estimation Scheme in Massive Multiple-Input Multiple-Output." Symmetry 10, no. 10: 507.
Traditional Minimum Mean Square Error (MMSE) detection is widely used in wireless communications, however, it introduces matrix inversion and has a higher computational complexity. For massive Multiple-input Multiple-output (MIMO) systems, this detection complexity is very high due to its huge channel matrix dimension. Therefore, low-complexity detection technology has become a hot topic in the industry. Aiming at the problem of high computational complexity of the massive MIMO channel estimation, this paper presents a low-complexity algorithm for efficient channel estimation. The proposed algorithm is based on joint Singular Value Decomposition (SVD) and Iterative Least Square with Projection (SVD-ILSP) which overcomes the drawback of finite sample data assumption of the covariance matrix in the existing SVD-based semi-blind channel estimation scheme. Simulation results show that the proposed scheme can effectively reduce the deviation, improve the channel estimation accuracy, mitigate the impact of pilot contamination and obtain accurate CSI with low overhead and computational complexity.
Kifayatullah Bangash; Imran Khan; Jaime Lloret; Antonio Leon. A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems. Electronics 2018, 7, 218 .
AMA StyleKifayatullah Bangash, Imran Khan, Jaime Lloret, Antonio Leon. A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems. Electronics. 2018; 7 (10):218.
Chicago/Turabian StyleKifayatullah Bangash; Imran Khan; Jaime Lloret; Antonio Leon. 2018. "A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems." Electronics 7, no. 10: 218.
In this paper, a closed-loop supply chain composed of dual-channel retailers and manufacturers, a dynamic game model under the direct recovery, and an entrusted third-party recycling mode of the manufacturer is constructed. The impact of horizontal fairness concern behavior is introduced on the pricing strategies and utility of decision makers under different recycling models. The equilibrium strategy at fair neutrality is used as a reference to compare offline retails sales. Research shows that in the closed-loop supply chain of dual-channel sales, whether in the case of fair neutrality or horizontal fairness concerns, the manufacturer’s direct recycling model is superior to the entrusted third-party recycling, and the third-party recycling model is transferred by the manufacturer. In the direct recycling model, the horizontal fairness concern of offline retailers makes two retailers in the positive supply chain compete to lower the retail price in order to increase market share. Manufacturers will lower the wholesale price to encourage competition, and the price will be the horizontal fairness concern coefficient, which is negatively correlated. In the reverse supply chain, manufacturers increase the recycling rate of used products. This pricing strategy increases the utility of manufacturers and the entire supply chain system compared to fair neutral conditions, while two retailers receive diminished returns. Manufacturers, as channel managers to encourage retailers to compete for price cuts, can be coordinated through a three-way revenue sharing contract to achieve Pareto optimality.
Muhammad Arshad; Qazi Salman Khalid; Jaime Lloret; Antonio Leon. An Efficient Approach for Coordination of Dual-Channel Closed-Loop Supply Chain Management. Sustainability 2018, 10, 3433 .
AMA StyleMuhammad Arshad, Qazi Salman Khalid, Jaime Lloret, Antonio Leon. An Efficient Approach for Coordination of Dual-Channel Closed-Loop Supply Chain Management. Sustainability. 2018; 10 (10):3433.
Chicago/Turabian StyleMuhammad Arshad; Qazi Salman Khalid; Jaime Lloret; Antonio Leon. 2018. "An Efficient Approach for Coordination of Dual-Channel Closed-Loop Supply Chain Management." Sustainability 10, no. 10: 3433.