Cnn Autoencoder Architecture Images High Quality Grahic
%20necessitates%20the%20ability%20to%20discern%20Line%20of%20Sight%20(LoS)%20and%20Non-Line%20of%20Sight%20(NLoS)%20scenarios%20across%20diverse%20applications%20and%20environments.%20Unmanned%20Aerial%20Vehicles%20(UAVs)%20exhibit%20significant%20potential%20in%20this%20regard%20due%20to%20their%20rapid%20mobility%2C%20aerial%20capabilities%2C%20and%20payload%20characteristics.%20Particularly%2C%20UAVs%20can%20serve%20as%20vital%20non-terrestrial%20base%20stations%20(NTBS)%20in%20the%20event%20of%20terrestrial%20base%20station%20(TBS)%20failures%20or%20downtime.%20In%20this%20paper%2C%20we%20propose%20CNN%20autoencoder%20resizer%20(CAR)%20as%20a%20framework%20that%20improves%20the%20accuracy%20of%20LoS%2FNLoS%20detection%20without%20demanding%20extra%20power%20consumption.%20Our%20proposed%20method%20increases%20the%20mean%20accuracy%20of%20detecting%20LoS%2FNLoS%20signals%20from%2066%25%20to%2086%25%2C%20while%20maintaining%20consistent%20power%20consumption%20levels.%20In%20addition%2C%20the%20resolution%20provided%20by%20CAR%20shows%20that%20it%20can%20be%20employed%20as%20a%20preprocessing%20tool%20in%20other%20methods%20to%20enhance%20the%20quality%20of%20signals.)
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