  Future mobile ad hoc networks will share spectrum between many users.
Channels will be assigned on the fly to guarantee signal and interference power
requirements for requested links. Channel losses must be re-estimated between
many pairs of users as they move and as environmental conditions change.
Computational complexity must be low, precluding the use of some accurate but
computationally intensive site-specific channel models. Channel model errors
must be low, precluding the use of standard statistical channel models. We
propose a new channel model, CELF, which uses channel loss measurements from a
deployed network in the area and a Bayesian linear regression method to
estimate a site-specific loss field for the area. The loss field is explainable
as the site's 'shadowing' of the radio propagation across the area of interest,
but it requires no site-specific terrain or building information. Then, for any
arbitrary pair of transmitter and receiver positions, CELF sums the loss field
near the link line to estimate its channel loss. We use extensive measurements
to show that CELF lowers the variance of channel estimates by up to 56%. It
outperforms 3 popular machine learning methods in variance reduction and
training efficiency.
