  CF-mMIMO systems are a promising solution to enhance the performance in 6G
wireless networks. Its distributed nature of the architecture makes it highly
reliable, provides sufficient coverage and allows higher performance than
cellular networks. EE is an important metric that reduces the operating costs
and also better for the environment. In this work, we optimize the downlink EE
performance with MRT precoding and power allocation. Our aim is to achieve a
less complex, distributed and scalable solution. To achieve this, we apply
unsupervised ML with permutation equivariant architecture and use a non-convex
objective function with multiple local optima. We compare the performance with
the centralized and computationally expensive SCA. The results indicate that
the proposed approach can outperform the baseline with significantly less
computation time.
