Pathloss Estimation Techniques for Incomplete Channel Measurement Data

摘要: The pathloss exponent and the variance of large-scale fading are two parameters that great importance when modeling or characterizing wireless propagation channels. is typically modeled using a single-slope log-distance power law model, whereas log-normal distribution. In practice, received signal affected by noise it might also be corrupted interference from other active transmitters transmitting in same frequency band. Estimating large scale without considering effects interference, can lead to erroneous results. this paper, we show path loss estimates improved if samples located below floor taken into account estimation step. When number such known, then standard deviation iteratively computed maximum likelihood incomplete data via expectation maximization (EM) algorithm. Alternatively, unknown, estimated based on expression for truncated normal (Less)