Uplink power control has a strong impact on the performance of mobile communication networks. In this work, an automatic parameter planning algorithm for the standardized power control scheme in the physical uplink shared channel (PUSCH) of long term evolution LTE is proposed. The method is conceived for the network design stage, when network measurements are still not available. The proposed heuristic algorithm can handle irregular scenarios at a low computational complexity. For this purpose, the parameter planning problem in a cell is formulated analytically through the combination of multiple regular scenarios built on a per-adjacency basis. Method assessment is carried out over a static system-level simulator implementing a real scenario. Results show that the proposed method can improve average user throughput and cell-edge user throughput when compared to current vendor approaches, which provide network-wide uniform parameter settings.

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Torque Teno virus (TTV), a non-enveloped, circular single-stranded negative-sense DNA genome virus, is a prototypical member of the Alphatorquevirus genus belonging to the Anelloviridae family, 1,2 which comprises a wide variety of genetically related species that exhibit wide cell tropism, are ubiquitous and, to our knowledge, apathogenic in humans. 3 Anelloviruses, which represent about 70%

Uplink Power Control (ULPC) is a key feature of mobile networks. Particularly, in LTE, Physical Uplink Shared Channel (PUSCH) performance strongly depends on Uplink Power Control configuration. In this work, a methodology for the self-planning of uplink Fractional Power Control (FPC) settings is presented. Values for nominal power and channel path-loss compensation factor are proposed. The method is designed for the planning and operational (replanning) stages. A very fast solution for FPC setting can be achieved by the combination of several simple solutions obtained by assuming some simplifications. First, the FPC planning problem is formulated analytically on a cell basis through the combination of multiple regular scenarios built on a per-adjacency basis from a live scenario. Secondly, detailed inspection of the FPC parameter values aims to identify the most important variables in the scenario impacting optimal FPC settings. Finally, regression equations can be built based on those key variables for a simple FPC parameter calculation, so computational costs are extremely reduced. Results show that network performance with the proposed FPC parameter settings is good when compared with typical FPC configurations from operators.