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Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem that how users choose access points in cell-free massive multiple-input multiple-output (CF-mMIMO) network, a prioritized access strategy for poorer users based on channel coefficient ranking was proposed.First, users were evaluated and ranked for their channel quality and stability after channel sensing, and suitable access points were selected in sequence according to the order of the channel state information.Second, considering issues such as users' energy consumption and data security, a federal learning framework was used to enhance user's data privacy and security.Meanwhile, an alternating optimization variables algorithm based on energy consumption optimization was proposed to optimize the multi-dimensional variables, for the purpose of minimizing the total energy consumption of the system.Simulation results show that compared with the traditional user-centric in massive MIMO, the proposed access strategy can improve the average uplink reachable rate of users by 20%, and the uplink rate of users with poor channels can be double improved; in terms of energy consumption optimization, the total energy consumption can be reduced by much more than 50% after optimization.

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To alleviate the pilot interference for cell-free massive multi-input multi-output (MIMO) system and improve system’s performance, a comprehensive approach including the pilot reuse, access point (AP) selection and power allocation was proposed.Firstly, to alleviate the pilot contamination, the pilot assignment based on the order of user distance was proposed, in which the pilot sharing was only allowed when the distance between two users is large enough to reduce the impact of pilot reuse.Then, by AP selection, only the APs which have main impact on the macro diversity gain were selected as the user’s serving APs.Finally, a modified max-min power control algorithm was proposed to improve the system’s average throughput.Simulation results show that the pilot interference mitigation strategy proposed improves the performance of the system significantly.

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For problem of low localization accuracy of quantum particle swarm optimization-sequence based localization (QPSO-SBL) method, a wireless access point(AP) selection algorithm based on information gain was used to optimize QPSO-SBL method, and a coal mine underground localization method based on wireless AP selection was proposed. In the method, all APs are ranked in descending order according to comprehensive identifiability of AP, and the first k APs are chosen to form an available AP set. The available AP set is considered as input of QPSO-SBL method, and localization results are obtained by QPSO-SBL method. The test results show that compared with QPSO-SBL method, the average localization error of the coal mine underground localization method based on wireless AP selection is reduced by 10.2% with stronger stability and better localization effect.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to non-line-of-sight propagation and other effects, the accuracy of indoor positioning based on location fingerprint is not high. Aiming at this problem, this paper proposed a novel indoor positioning algorithm, which based on the reliable AP selection and deep belief network ( DBN) . Firstly, the algorithm used the improved K-means clustering algorithm to divide the locating area into several sub-regions in the off-line phase. Then according to the Fisher criterion and the AP absent frequency, it selected the strongly distinguishable and reliable AP node as the training node of the sub-region. And finally it used the DBN model to train the data of each sub-region. In the on-line phase, the improved algorithm determined the cluster according to the received signal strength, and estimated the location of test point by the trained DBN model. The experimental results show that, compared with WKNN, M-WKNN and PSO-ANN algorithm, the proposed algorithm can effectively improve the accuracy and stability of positioning. [ABSTRACT FROM AUTHOR]