Reducing the Environmental Impact of Wireless Communication via
  Probabilistic Machine Learning
A. Ryo Koblitz Lorenzo Maggi Matthew Andrews
Machine learning methods are increasingly adopted in communications problems,
particularly those arising in next generation wireless settings. Though seen as
a key climate mitigation and societal adaptation enabler, communications
related energy consumption is high and is expected to grow in future networks
in spite of anticipated efficiency gains in 6G due to exponential
communications traffic growth. To make meaningful climate mitigation impact in
the communications sector, a mindset shift away from maximizing throughput at
all cost and towards prioritizing energy efficiency is needed. Moreover, this
must be adopted in both existing (without incurring further embodied carbon
costs through equipment replacement) and future network infrastructure, given
the long development time of mobile generations. To that end, we present
summaries of two such problems, from both current and next generation network
specifications, where probabilistic inference methods were used to great
effect: using Bayesian parameter tuning we are able to safely reduce the energy
consumption of existing hardware on a live communications network by $11\%$
whilst maintaining operator specified performance envelopes; through
spatiotemporal Gaussian process surrogate modeling we reduce the overhead in a
next generation hybrid beamforming system by over $60\%$, greatly improving the
networks' ability to target highly mobile users such as autonomous vehicles.
The Bayesian paradigm is itself helpful in terms of energy usage, since
training a Bayesian optimization model can require much less computation than,
say, training a deep neural network.