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Toward Green 6G: Communication Versus Computation Energy Trade-Offs
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AI
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Y
Q
张
J
DOI：10.1109/tgcn.2026.3713105.png)
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Abstract
En 中文
As 6G green networking research increasingly emphasizes energy efficiency alongside performance enhancement, understanding the balance between communication and computation energy consumption has become crucial. While unsupervised learning methods have been widely applied to achieve energy-aware optimization, most existing studies focus solely on communication energy consumption, overlooking the computation cost of the learning algorithms. This paper establishes a comprehensive analytical framework to explore the computation and communication energy consumption trade-off in CF-mMIMO systems employing the ML-based MPCC method. We introduce two new metrics, ECR and MPG, to quantify the interaction between two energy consumptions. Our analysis reveals that similar system performance can be achieved through fundamentally different energy strategies, either computation-centric (frequent MPCC updates with moderate power) or communication-centric (infrequent updates with high power). Overall, the results show that Pareto-like trade-offs emerge across a range of update intervals rather than at a single operating point, with the equilibrium shifting depending on network conditions. Specifically, in the considered MPCC-aided CF-mMIMO setting, increasing computation energy through more frequent chart updates yields larger performance gains than increasing transmit power. For example, increasing the MPCC update frequency from UI4 to UI3 enables a reduction in transmit power from 1.2W to 0.5W (58.3%) while maintaining nearly identical sum rate performance. These findings reveal a partially compensatory relationship between resources in the considered MPCC aided CF-mMIMO setting, suggesting a potentially useful design perspective for future 6G systems.
Keywords:
MPCC
CF-mMIMO
communication energy consumption
computation energy consumption
energy trade-off
ML
6G green networking
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