We consider the downlink of a cache-aided cell-free (CF) massive multi-input multi-output (mMIMO) system, wherein user equipments (UEs) exploit cached data to cancel multi-user interference (MUI) in the downlink. This work shows the importance of downlink pilots transmitted by the APs, which are used by the UEs to first estimate the instantaneous downlink channel state information (CSI), and then perform cache-aided MUI cancellation. We derive a closed-form SE expression for this system by considering practical spatially-correlated Rician fading channels with phase shifts, pilot contamination, and hardware impairments, both at the APs and UEs. We also propose a joint cache placement and power control optimization to maximize the global energy efficiency (GEE) metric. The cache placement problem is solved via clustering, while the power is optimized by designing a novel low-complexity parallel block minorization-maximization optimization. We numerically validate the benefits of downlink CSI for cache-aided CF mMIMO systems, and also show that degradation in CSI quality dramatically negates the UEs cache-aided MUI cancellation capability. We also show that the proposed optimization yields the same GEE as existing state-of-the-art optimizations, but with a much lower complexity.

We consider the downlink of a cell-free (CF) massive multi-input multi-output (mMIMO) system, wherein multiple access points serve clustered users by employing non-orthogonal multiple access (NOMA) technology. This work shows the importance of transmitting downlink pilots for a NOMA CF mMIMO system, and derives a closed-form spectral efficiency (SE) expression with spatially-correlated Rician channels by addressing the complexity due to i) dynamic-resolution analog to digital converters; and ii) channel estimation errors. We also design a low-complexity parallel block minorization-maximization (MM) algorithm to optimize the non-convex global energy efficiency (GEE) metric. The MM algorithm requires a surrogate function for the GEE metric, which we construct in this work. We show that the NOMA CF mMIMO system, wherein channels estimated using downlink pilots are used for performing successive interference cancellation (SIC), significantly outperforms its counterpart which uses channel statistics to perform SIC. We also show that our parallel block MM optimization yields the same GEE as an existing optimization, but with a much lower complexity.