5G Ultra-Dense Network Fingerprint Positioning Method Based on Matrix Completion

The problem of high-precision indoor po-sitioning in the 5G era has attracted more and more attention.A fingerprint location method based on ma-trix completion(MC-FPL)is proposed for 5G ultra-dense networks to overcome the high costs of tradi-tional fingerprint database construction and matching algorithms.First,a partial fingerprint database con-structed and the accelerated proximal gradient algo-rithm is used to fill the partial fingerprint database to construct a full fingerprint database.Second,a finger-print database division method based on the strongest received signal strength indicator is proposed,which divides the original fingerprint database into several sub-fingerprint databases.Finally,a classification weighted K-nearest neighbor fingerprint matching al-gorithm is proposed.The estimated coordinates of the point to be located can be obtained by fingerprint matching in a sub-fingerprint database.The simula-tion results show that the MC-FPL algorithm can re-duce the complexity of database construction and fin-gerprint matching and has higher positioning accuracy compared with the traditional fingerprint algorithm.