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.

We study the impact of channel aging on the uplink of a cell-free massive multiple-input multiple-output system with hardware impairments. We consider a dynamic analog-to-digital converter architecture at the access points (APs), and low-resolution digital-to-analog converters at the user equipments (UEs). We derive a closed-form spectral efficiency expression by considering i) practical spatially-correlated Rician channels; ii) hardware impairments at the APs and the UEs; iii) channel aging; and iv) large-scale fading decoding (LSFD). We show that LSFD can effectively mitigate the detrimental effects of i) channel aging for both low and high UE velocities; and ii) inter-user interference for low-velocity UEs but not for high-velocity UEs.

We consider a hardware-impaired multi-cell Rician-faded massive multi-input multi-output (mMIMO) system with two-layer pilot decontamination precoding, also known as large-scale fading precoding (LSFP). We derive a closed-form spectral efficiency (SE) expression by assuming a flexible dynamic analog-to-digital converter (ADC)/digital-to-analog converter (DAC) architecture, and hardware-impaired radio frequency chains at the base stations (BSs) and user equipments. The dynamic ADC/DAC architecture enables us to vary the resolution of ADC/DAC connected to each BS antenna, and suitably choose them to maximize SE. We design a distortion-aware minimum mean squared error (DA-MMSE) precoder, and investigate its usage by combining it with the two-layer LSFP, and conventional single-layer precoding (SLP). We show that the DA-MMSE precoder with SLP outperforms its distortion-unaware counterpart, which is used with LSFP. We analytically show that for pure LoS channels, the LSFP reduces to SLP, and its implementation can thus be avoided. It is shown that the LSFP can tolerate high hardware impairments at the BS, but is extremely sensitive to the ADC resolution of the user. We also optimize the global energy efficiency by using a minorization-maximization based algorithm, and show its improved performance over the conventional SE optimization techniques.

We study the impact of channel aging on the uplink of a cell-free (CF) massive multiple-input multiple-output (mMIMO) system by considering i) spatially-correlated Rician-faded channels; ii) hardware impairments at the access points and user equipments (UEs); and iii) two-layer large-scale fading decoding (LSFD). We first derive a closed-form spectral efficiency (SE) expression for this system, and later propose two novel optimization techniques to optimize the non-convex SE metric by exploiting the minorization-maximization (MM) method. The first one requires a numerical optimization solver, and has a high computation complexity. The second one with closed-form transmit power updates, has a trivial computation complexity. We numerically show that i) the two-layer LSFD scheme effectively mitigates the interference due to channel aging for both low- and high-velocity UEs; and ii) increasing the number of AP antennas does not mitigate the SE deterioration due to channel aging. We numerically characterize the optimal pilot length required to maximize the SE for various UE speeds. We also numerically show that the proposed closed-form MM optimization yields the same SE as that of the first technique, which requires numerical solver, and that too with a much reduced time-complexity.

We consider a hardware-impaired multi-cell Rician- faded massive multi-input multi-output (mMIMO) system with two-layer pilot decontamination precoding, also known as large-scale fading precoding (LSFP). Each BS is equipped with a flexible dynamic analog-to-digital converter (ADC)/digital-to-analog converter (DAC) architecture and the user equipments (UEs) have low-resolution ADCs. Further, both BS and UEs have hardware-impaired radio frequency chains. The dynamic ADC/DAC architecture allows us to vary the resolution of ADC/DAC connected to each BS antenna, and suitably choose them to maximize the SE. We propose a distortion-aware minimum mean squared error (DA-MMSE) precoder and investigate its usage with two-layer LSFP and conventional single-layer precoding (SLP) for hardware-impaired mMIMO systems. We discuss the use cases of LSFP and SLP with DA-MMSE and distortion-unaware MMSE (DU-MMSE) precoders, which will provide critical insights to the system designer regarding their usage in practical systems.

We consider a cell-free (CF) massive multi-input multi-output (mMIMO) system, where multi-antenna access points (APs) serve single-antenna unmanned aerial vehicles (UAVs) and ground users (GUEs). We assume, unlike the existing CF mMIMO literature, hardware-impaired UAVs and GUEs, which observe a mixture of spatially-correlated Rician- and Rayleigh-faded channels while communicating with hardware-impaired APs. We derive a closed-form downlink spectral efficiency (SE) expression by using practical models for the channel mixture, and by considering channel estimation errors. We propose a novel block quadratic transformation (block-QT) technique to optimize non-convex network-centric global energy efficiency (GEE) by appropriately modeling circuit, UAV propulsion and fronthaul powers. The novel block-QT approach combines block optimization and quadratic transformation technique to decompose GEE optimization into simpler convex sub-problems. We numerically show that i) it is better to operate a UAV at a larger height when it has severe hardware impairments; and ii) when UAVs operate at a lower height, they do not significantly affect the SE of GUEs.

We consider an unmanned aerial vehicle (UAV) enabled cell-free (CF) massive multi-input multi-output (mMIMO) system, where multi-antenna access points (APs) assist various single-antenna UAVs and ground users (GUEs). Unlike, existing CF mMIMO works, we assume non-ideal transceiver hardware at the UAVs, GUEs and APs, and derive a closed-form downlink spectral efficiency (SE) expression by considering spatially-correlated Rician channels and channel estimation errors. We also propose a novel block quadratic transformation technique to optimize the system global energy-efficiency (GEE) by incorporating practical backhaul power, circuit and UAV propulsion power. We numerically show that severely hardware-impaired UAVs will have a higher SE when they operate at a larger height.