  While machine-type communication (MTC) devices generate massive data, they
often cannot process this data due to limited energy and computation power. To
this end, edge intelligence has been proposed, which collects distributed data
and performs machine learning at the edge. However, this paradigm needs to
maximize the learning performance instead of the communication throughput, for
which the celebrated water-filling and max-min fairness algorithms become
inefficient since they allocate resources merely according to the quality of
wireless channels. This paper proposes a learning centric power allocation
(LCPA) method, which allocates radio resources based on an empirical
classification error model. To get insights into LCPA, an asymptotic optimal
solution is derived. The solution shows that the transmit powers are inversely
proportional to the channel gain, and scale exponentially with the learning
parameters. Experimental results show that the proposed LCPA algorithm
significantly outperforms other power allocation algorithms.
