Table 12 Spectral efficiency of cell-free massive MIMO
From: Application of cell-free massive MIMO in 5G and beyond 5G wireless networks: a survey
Ref. | Focus and coverage | Key findings | Limitations | Year |
|---|---|---|---|---|
[55] | The work presents the effect of multi-antenna users on CF massive MIMO networks. The work aims at optimizing the multiplexing gain, as well as improving the SE. In order to achieve this, a simple CB scheme is considered. By using the max-min data power control and mutually orthogonal pilot sequence, the effect of the proposed model is analyzed. | â–ª In terms of per-user net throughput, the multiple antennas at both APs and users outperform a single antenna. â–ª Also, the array gain is improved while the inter-user interference is minimized. | â–ª A decrease in the per-user net throughput value is imminent due to an increase in antennas per user. | 2018 |
[104] | A comprehensive survey of CF massive MIMO under different cooperation levels is presented. Specifically, four different levels of implementation, from fully centralized to fully distributed with arbitrary linear processing and spatially correlated fading, are examined. In addition, the achievable SE expression for the different CF implementations is presented. | â–ª The proposed scheme significantly outperforms cellular massive MIMO and SC systems, however, with MMSE processing. | â–ª For centralized implementation with MMSE, the SE is generally minimized in the regime of fewer APs equipped with multiple antennas. â–ª Notably, the distributed implementation is marginalized and requires further improvement. | 2019 |
[80] | This survey characterized the UL performance of CF massive MIMO with multi-antennas at both APs and users. Motivated by the gaps in CF systems with multi-antenna users, the work aims at maximizing the UL SE using ZF combining at the APs and data power control at users. | â–ª The UL SE of CF systems with ZF combining is substantially improved. â–ª Integration of CF systems with maximum-ratio (MR) combining is sub-optimal compared to its ZF combining counterpart. â–ª Notably, additional antennas at users provide significant performance gain is minimal active users in the system. | â–ª The results indicate that the interference increases when the number of active users grows large, and the SINR of each antenna generally decreases, which is undesirable in practice. | 2019 |
[85] | This work considers the impact of spatially correlated fading channels on the UL performance of CF architecture. The LS estimator is used, and a rigorous closed-form expression is designed to analyze the effects of the system’s parameters-number of users. The number of APs and the fronthaul on the SE and EE is developed, considering the spatial correlation matrices and the number of APs antennas. | ▪ The low-complexity LS estimator optimizes the SE and EE significantly as opposed to collocated massive MIMO. ▪ A compromise between the number of APs and the number of users is essential to maximize the performance of SE and EE. ▪ In addition, the performance of SE and EE in correlated Rayleigh fading channels is sub-optimal compared to the uncorrelated Rayleigh fading channel. | ▪ The system is significantly impacted by spatial correlation. | 2019 |
[131] | The authors considered limited-fronthaul CF massive MIMO system in the presence of quantization errors, imperfect channel acquisition, and pilot contamination. By exploiting the max algorithm and Bussgang decomposition, an optimal uniform quantization model is presented under the assumption of estimate and quantize, quantize and estimate, and decentralized scheme. Moreover, analytical expressions for the maximal SE and the EE of the system with three different linear receivers—MRC, ZF, and MMSE—are presented. | ▪ Only a few bits for quantization are sufficient for the limited-fronthaul CF-mMIMO to support the system’s performance with perfect fronthaul. ▪ The power consumption and the SE have been shown to increase with the number of quantization bits. ▪ The exact number of bits quantifying the estimated channel at the APs and quantifying the received signal is enough to achieve optimal performance. | ▪ The performance of the decentralized scheme is considerably enhanced with the proposed AP assignment algorithm, however, only in the case of a large number of APs. | 2020 |
[163] | The work considers improving the spectral/energy efficiency of wireless communication systems using intelligent multi-objective optimization techniques. Multi-objective genetic algorithm, multi-objective particle swarm optimization, and multi-objective differential evolution algorithms are proposed and applied at different circuit power levels. | â–ª The SE-EE trade-off is generally maximized with the proposed intelligent optimization scheme compared to some other schemes investigated. | â–ª The SE-EE trade-off is not optimized with respect to the different number of UEs served in the network. | 2021 |
[164] | The authors quantitatively examined the achievable sum SE of a frequency-selective CF massive MIMO system considering the effect of imperfect CSI and phase noise (PN). Two low-complexity receivers, namely time-reversal maximum-ratio combining (TR-MRC) and time-reversal large-scale fading decoding (TR-LSFD), are employed at the CPU for data detection. Furthermore, the corresponding lower bound on the UL achievable SE of both receivers is presented. | â–ª Numerical results show that the TR-LSFD receiver outperforms the TR-MRC receiver in attaining higher SE. â–ª Moreover, the performance gain of the TR-LSFD receiver is mainly dependent on the number of APs and the propagation losses between APs and users. | â–ª The impact of PN on the achievable sum SE and duration of data transmissions is quite severe. | 2021 |