Future wireless networks must support heterogeneous services with markedly different requirements, such as enhanced mobile broadband (eMBB), which demand high data rates, and massive machine-type communications (mMTC), which seek long battery lifetimes. Recently, cell-free massive MIMO (CF-mMIMO) has emerged as a promising paradigm for providing uniform service quality and flexible resource allocation in such scenarios.
In this talk, we investigate uplink multiple-access schemes for the coexistence between these two use cases. We consider a non-orthogonal access strategy in which low-rate mMTC transmissions are spread across the time–frequency resources concurrently used by eMBB users, enabling efficient resource reuse while supporting heterogeneous traffic.
Assuming imperfect channel state information, we derive closed-form expressions for the achievable uplink rates of both services, based solely on statistical channel knowledge. For mMTC devices, the analysis explicitly accounts for the finite blocklength (FBL) regime to characterize short-packet transmissions.
To accommodate heterogeneous service requirements, we formulate a power-control problem that maximizes the minimum energy efficiency across mMTC devices while enforcing quality-of-service constraints for eMBB users. The resulting nonconvex fractional optimization is addressed via sequential fractional programming, with formulations that cover both the classical Shannon and FBL regimes.
Numerical results demonstrate that the proposed scheme enables effective multiplexing of eMBB and mMTC traffic while maintaining the desired reliability-efficiency trade-offs. As future work, we plan to investigate learning-based approximations of the proposed optimization framework (e.g., using graph neural networks) to further reduce computational complexity and enable real-time operation.