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Each point in the figure corresponds to a network parameter plan.
However, this is not the case of network planning, where computations required to measure the quality of a new parameter plan at every iteration may jeopardize method scalability when large scenarios are considered.
To assess the value of a parameter plan, two performance indicators are used: a) as a measure of network capacity, overall average user throughput, (overline {{TH}_{textit {avg}}(i)}), computed by averaging T H avg (i) across cells in the scenario, and b) as a measure of coverage, the ratio of cells fulfilling T H ce (i >T H c e,m i n, denoted as (R_{{TH}_{ce,min}}phantom {dot {i}!}).
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In contrast, any of the self-planning methods proposed for constructing cell-based parameter plans took only 300 s in average (i.e., 0.43 s per cell).
The chapter also explains soft handover parameter planning, hierarchical cell structures, and code requirements.
This chapter discusses the issues associated with network planning, and describes control channel power planning, radio frequency characterization, soft handover (parameter) planning, and inter-frequency handover planning.
Study on the hot deformation behavior of modified CNS-II F/M steel is of great importance for processing parameter planning and microstructure controlling during hot deformation.
For comparison purposes, results of uniform parameter plans are superimposed.
This makes ULPC an ideal candidate for automated parameter planning.
The video system planning model is for network QoS parameter planning and video coding parameter planning, given a target video quality.
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.
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