  One of the primary goals in spectrum occupancy mapping is to create a system
that is robust to assumptions about the number of sensors, occupancy threshold
(in dBm), sensor noise, number of emitters and the propagation environment. We
show that such a system may be designed with neural networks using a process of
aggregation to allow a variable number of sensors during training and testing.
This process transforms the variable number of measurements into approximate
log-likelihood ratios (LLRs), which are fed as a fixed-resolution image into a
neural network. The use of LLR's provides robustness to the effects of noise
and occupancy threshold. In other words, a system may be trained for a nominal
number of sensors, threshold and noise levels, and still operate well at
various other levels without retraining. Our system operates without knowledge
of the number of emitters and does not explicitly attempt to estimate their
number or power. Receiver operating curves with realistic propagation
environments using topographic maps with commercial network design tools show
how performance of the neural network varies with the environment. The use of
very low-resolution sensors in this system can still yield good performance.
