A Novel Scheme for NLOS Identification using Energy Detector in 60 GHz Systems

Abstract

The major problem of indoor localization is presence of non-line-of-sight (NLOS) channels. In order to perform NLOS identification, in

Machine learning-based classification method for millimeter wave indoor channel at 28 GHz

“…Current research efforts are mostly concentrated on wireless local area network systems and ultra-wideband systems below 6 GHz with limited studies on channel classification in millimeter-wave systems [3], [4] . The method used in [5], under omnidirectional antenna simulation conditions, utilizes an energy detector approach for NLOS identification in a 60 GHz millimeter-wave system. The method used in [6] is based on the actual measurement of channel impulse response (CIR) using a 28GHz quasi-omnidirectional antenna.…”

Neural-Network-Based NLOS Identification of Angular Clusters at 60 GHz

“…Although many studies report on indoor [52][53][54] and outdoor [55][56][57][58] localization based on mm-waves, only a few works [59][60][61][62][63] are dealing with NLOS identification at mm-wave frequencies. To maintain a low-complexity system, the work in [59,60] uses a simple energy detector to mitigate NLOS components in TOA estimations. However, its robustness to various environments is not validated with measurements.…”

“…Based on 28 GHz measurements with quasi-omnidirectional antennas, it uses similar identification approaches than in UWB. Therefore, as well as all mm-wave identification, studies [59][60][61][62][63] do not consider the directional behavior of the channel.…”