|Ref.||Focus and coverage||Key findings||Limitations||Year|
|||The work aims at quantifying the improvements in EE of CF massive MIMO over cellular massive MIMO. The authors opine that service providers have ignored the EE for SE over time, which is presumed to be a prerequisite for practical energy-efficient technologies.||
▪ The SE and EE for cellular systems in an urban scenario with equal power and max-min power control strategies are comparable.|
▪ However, CF systems with max-min power control significantly outperform those with equal power control (EPC) regarding the radiated EE.
▪ In the suburban and rural scenarios, CF-mMIMO with max-min power control improves the radiated EE considerably and optimizes the 95% likely per-user throughput alongside.
|▪ The complexity and time delay in determining max-min power control for CF systems largely limits the EE.||2018|
|||An efficient energy management strategy for CF massive MIMO systems is examined. The authors posit that APs powered jointly by energy harvested from renewable sources and grid sources offer better performance improvement. To this end, the work aims at compensating for the intermittent and random nature of the harvested energy while ensuring optimized total grid power consumption.||
▪ Compared to traditional non-cooperative techniques, results obtained indicate a significant reduction in total grid power consumption.|
▪ Specifically, as the SINR increases, the performance gap between the proposed scheme and the traditional systems without cooperation becomes noticeable.
|▪ CF systems enabled with energy exchange capabilities generally suffer from power lows.||2019|
|||Presents the EE of limited backhaul links, which connects the APs to a CPU for coordination and data processing in CF massive MIMO. The authors opine that the backhaul links from the APs to the CPU are somewhat limited in capacity, presenting a major challenge. In order to address this issue, an MRC technique is proposed, where the only quantized weighted signal is forwarded to the CPU.||
▪ The proposed model is shown to satisfy the optimization constraints effectively.|
▪ Also, the EE is considerably improved compared to equal power allocation.
▪ Moreover, an optimal number of quantization bits enhances the backhaul links’ capacity and simultaneously maximizes the EE of the system.
|▪ An optimal trade-off between the total number of APs and antennas per APs is required to maximize the EE.||2019|
|||EE maximization techniques for CF massive MIMO communication systems with fpZF precoding strategy are investigated. The work aims at mitigating inter-cell interference while enabling an optimized EE. Contrary to conventional precoding schemes, no instantaneous CSI exchange occurs among the APs.||▪ The DL EE is considerably improved compared to the case with full power transmission.||▪ It is mainly sub-optimal in real-time applications.||2019|
|||This survey considers power optimization techniques to maximize the total EE in CF-mMIMO. The authors remark that energy consumption in communication systems has expanded rapidly owing to increasing growth in information. In order to boost the EE, a pilot-contaminated UL CF system assuming a ZF receiver is analyzed.||
▪ The proposed algorithm converges quickly, thereby validating its effectiveness.|
▪ The total EE is considerably improved compared to the EPC scheme.
|▪ Although the proposed novel path-following algorithm provides low complexity and a modest performance gain, it is not fully optimized.||2019|
|||Strategies to maximize the overall power consumed in CF systems without compromising the SE are investigated. The authors consider the possibility of turning off inactive APs after SE requirements have been fully satisfied, thereby optimizing EE.||
▪ Significant power reduction is achieved by turning off inactive APs after users’ SE requirements have been fully satisfied.|
▪ The developed sparsity-based method minimizes the total power consumption in CF massive MIMO network.
|▪ The proposed scheme is primarily limited by high complexity.||2020|
|||The work presents a CF massive MIMO system enabled with energy exchange capabilities and powered by independent micro-grids. The work aims at offsetting power consumption costs while ensuring maximized EE. A minimization problem of total grid power consumption is developed with reference to users’ QoS constraints and energy exchange constraints.||▪ The EE is considerably improved compared to the case of traditional non-cooperative techniques||▪ A closer look indicates that there is a performance gap between the proposed algorithm and the optimal AP selection, and user scheduling is quite significant and requires further optimization.||2020|
|||This research considered integrating the new revolutionary technology called RIS in a CF system to maximize the network EE, considering the impact of limited backhaul capacity. An alternating descent algorithm based on the inner approximation (IA) framework is proposed to tackle the computationally intractable nonconvex optimization problem.||
▪ The alternating descent algorithm converges to a locally optimal solution.|
▪ The authors proposed the RIS-CF network, which is shown to improve the EE greatly compared to the schemes considered.
|▪ The trade-off between the total sum rate and power consumption is not fully optimized.||2021|
|||This survey considered the EE of an mMIMO-NOMA network with WPT. Novel joint transmits power, antenna selection WPT, time and subcarrier resource allocation schemes are proposed to solve the EE maximization problem. A distributed ADMM-based resource allocation algorithm is also adapted to provide an optimal solution to the problem.||▪ Compared to alternative schemes, the EE is substantially improved with the proposed distributed ADMM-based algorithm.||
▪ Channel estimation error greatly impacts the system’s performance.|
▪ Practical and implementable scenarios were omitted.