Search Results for « Hiera »

This paper presents a comparison of different metaheuristic approaches applied to the pilot sequence allocation problem in Massive Multiple-Input Multiple-Output (MIMO) systems. A modified version of the Genetic Algorithm (GA) as well as different versions of the Particle Swarm Optimization (PSO) Algorithm are used to maximize the system spectral efficiency under an inter-cell interference regime. The metaheuristic parameters were optimized and computational simulations under different scenarios parameters were conducted to verify the system performance impact in terms of system spectral efficiency, minimum and maximum spectral efficiency per user and the cumulative distribution function (CDF) of the users spectral efficiencies. The main contributions of this work are: the creation of a public available dataset; heuristic parameters tuning; findings related to the impact of sub-optimal pilot sequence allocation to the users in terms of maximal and minimal achievable user spectral efficiency and the robustness of some algorithms in scenarios with different system loadings.

Urban drone delivery increasingly requires autonomous landing in courtyards, curbside bays and rooftop pads, where perception, communication and local micro-weather can change within a few seconds of descent. This paper presents Hiera-Seg, a hierarchical segmentation and edge-adaptive learning model for landing-risk assessment in unmanned aerial vehicle delivery. The model decomposes the landing scene into traversability, obstacle, surface-stability and exposure layers, and then fuses them through a calibrated risk head updated by lightweight edge clients. A controlled evaluation was constructed from 18,420 annotated image-depth frames, 612 descent sequences and five representative landing environments, including rooftop, sidewalk, courtyard, service-lane and campus-pad scenes. Against a non-adaptive DeepLab-style baseline, Hiera-Seg improved mean intersection over union from 78.6% to 84.9%, reduced expected calibration error from 0.116 to 0.052 and lowered high-risk false clearance from 9.8% to 4.1%. The edge adaptation module reached 27.4 ms median inference latency on an embedded GPU and retained 96.2% of centralized accuracy after communication was limited to 1.6 MB per update round. These results show that landing risk is estimated more reliably when scene hierarchy and edge-specific operating conditions are learned together rather than handled as separate perception and decision stages.