Although multiple works have proposed energy-efficient resource allocation schemes for Massive Multiple-Input Multiple-Output (M-MIMO) system, most approaches overlook the potential of optimizing Power Amplifier (PA) transmission power while accounting for non-linear distortion effects. Furthermore, most M-MIMO studies assume narrow-band transmission, neglecting subcarrier intermodulations at the non-linear PA for an Orthogonal Frequency Division Multiplexing (OFDM) system. Therefore, this work investigates the energy-efficient power allocation for a single-user equipment (UE) M-MIMO downlink (DL) system employing OFDM with nonlinear PAs. Unlike prior works, we model wide-band transmission using a soft-limiter PA model and derive a closed-form expression for the signal-to-distortion-and-noise ratio (SNDR) under Rayleigh fading and Maximal Ratio Transmission (MRT) precoding. Next, the Energy Efficiency (EE) function is defined considering two PA architectures and a distorted OFDM signal. We then propose a low complexity root-finding algorithm to maximize EE by transmit power adjustment. Simulation results demonstrate significant EE gains over a fixed PA back-off baseline, with over $100\%$ improvement under both low and high path loss. Our findings reveal how the optimal operating point depends on the antenna count, the PA model, and the propagation conditions.

Real-world wireless transmitter frontends exhibit certain nonlinear behavior, e.g., signal clipping by a Power Amplifier (PA). Although many resource allocation solutions do not consider this for simplicity, it leads to inaccurate results or a reduced number of degrees of freedom, not achieving the global performance. In this work, we propose an optimal PA distortion-aware power allocation strategy in a downlink orthogonal frequency division multiplex (OFDM) based massive multiple-input multiple-output (M-MIMO) system. Assuming a soft-limiter PA model, where the transmission occurs under small-scale independent and identically distributed (i.i.d) Rayleigh fading channel, we derive the wideband signal-to-noise-and-distortion ratio (SNDR) and formulate the power allocation problem. Most interestingly, the distortion introduced by the PA leads to an SNDR-efficient operating point without explicit transmit power constraints. While the optimization problem is non-convex, we decouple it into a non-convex total power allocation problem and a convex power distribution problem among the users (UEs). We propose an alternating optimization algorithm to find the optimum solution. Our simulation results show significant sum-rate gains over existing distortion-neglecting solutions, e.g., a median 4 times increase and a median 50% increase for a 64-antenna and 512-antenna base station serving 60 users, respectively.

Typically, energy-efficient operation of wireless networks is achieved by allocating resources, such as physical resource blocks (PRBs), transmit power, and number of base station (BS) antennas, in an energy-efficient manner. However, the allocation of these resources is tightly coupled with user (UE)-to-BS association, which impacts the performance and efficiency of the network. Therefore, in this work we focus on energy-efficient UE-to-BS association for multi-cell multi-user massive multiple input multiple output (mMIMO) heterogeneous networks (HetNets) and propose two bias-based UE-to-BS association algorithms. Firstly, a range expansion bias (REB) model is employed to increase the effective coverage area of each pico BS (PBS), improving overall network performance and energy efficiency (EE). Secondly, the sum of channel gains as a metric for UE-to-BS association is leveraged, seeking to maximize the total channel gain across UEs and BSs by prioritizing stronger connections. This approach aims to optimize the distribution of UEs among available BSs, thereby enhancing the quality of service provided to UEs. Our results, based on numerical system-level simulations describing a realistic metropolis (Berlin) scenario, demonstrate that the individual REBs (iREBs) per PBS lead to significant improvements in EE, while the channel-gainbased approach effectively boosts overall network throughput and EE, outperforming the greedy maximum channel gain, a baseline REB based, and the iREB based UE-to-BS association methods.

For energy efficient operation of the massive multiple-input multiple-output (MIMO) networks, various aspects of energy efficiency maximization have been addressed, where a careful selection of number of active antennas has shown significant gains. Moreover, switching-off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of spectral PRB allocation and spatial layering in a heterogeneous network has not been completely solved yet. Therefore, we study a power consumption model for multi-cell multi-user massive MIMO 5G network, capturing the joint effects of both dimensions. We characterize the optimal resource allocation under practical constraints, i.e., limited number of available antennas, PRBs, base stations (BSs), and frequency bands. We observe a single spatial layer achieving lowest energy consumption in very low load scenarios, whereas, spatial layering is required in high load scenarios. Finally, we derive novel algorithms for energy efficient user (UE) to BS assignment and propose an adaptive algorithm for PRB assignment and power control. All results are illustrated by numerical system-level simulations, describing a realistic metropolis scenario. The results show that a higher frequency band should be used to support UEs with large rate requirements via spatial multiplexing and assigning each UE maximum available PRBs.

To meet the targets of net-zero green house gas (GHG) emissions, future wireless networks must operate highly energy efficient. To this end, various aspects of energy efficiency (EE) maximization have been addressed. On the one hand, careful selection of active number of antennas in massive multiple-input multiple-output (MIMO) systems has shown significant gains. Whereas, switching off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of both dimensions, the spectral PRB allocation with carrier aggregation (CA) and spatial layering, has not been accounted for. In this paper, we propose a power consumption model that captures the joint effect of CA and spatial layering on the total power consumption of a 5G network. We characterize the optimal resource allocation in spatial and spectral dimensions under practical constraints. Our results show that only in very low load scenarios, a single spatial layer achieves the lowest energy consumption and in most cases with high rate requirements and more users, spatial layering is required with carefully optimized number of active antennas and active PRBs. The gains compared to activating all available antennas and using all available PRB resources are tremendous. Finally, we study the point where switching on another frequency band results in better EE depending on the attenuation model.