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.

机器学习方法在通信问题中被越来越广泛地采用，尤其是在下一代无线设置中。通过使用贝叶斯参数调整和时空高斯过程代理建模，我们能够降低现有硬件的能耗并改善网络性能，同时减少下一代混合波束赋形系统的开销，从而大大提高网络的能效和针对高移动用户的目标能力。贝叶斯范式本身对于能源使用也是有帮助的，因为训练一个贝叶斯优化模型所需的计算要比训练深度神经网络要少得多。