Two key challenges in underlay dynamic spectrum access (DSA) are how to
establish an interference limit from the primary network (PN) and how cognitive
radios (CRs) in the secondary network (SN) become aware of the interference
they create on the PN, especially when there is no exchange of information
between the two networks. These challenges are addressed in this paper by
presenting a fully autonomous and distributed underlay DSA scheme where each CR
operates based on predicting its transmission effect on the PN. The scheme is
based on a cognitive engine with an artificial neural network that predicts,
without exchanging information between the networks, the adaptive modulation
and coding configuration for the primary link nearest to a transmitting CR. By
managing the effect of the SN on the PN, the presented technique maintains the
relative average throughput change in the PN within a prescribed maximum value,
while also finding transmit settings for the CRs that result in throughput as
large as allowed by the PN interference limit. Simulation results show that the
ability of the cognitive engine in estimating the effect of a CR transmission
on the full adaptive modulation and coding (AMC) mode leads to a much more fine
underlay transmit power control. This ability also provides higher transmission
opportunities for the CRs, compared to a scheme that can only estimate the
modulation scheme used at the PN link.

本文介绍了利用人工神经网络进行自主分布式的次级网络的控制策略，并通过预测次级网络对主网络的干扰来解决干扰限制和认知无线电之间的信息交流等问题，以实现更精细的下层传输功率控制和更高的传输机会。