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Table 9 Power control in cell-free massive MIMO

From: Application of cell-free massive MIMO in 5G and beyond 5G wireless networks: a survey


Focus and coverage

Key findings




Power control techniques to minimize the pilot contamination effect in CF-mMIMO are investigated. The goal is to distribute pilot power to each network user while ensuring accurate channel estimation. The efficacy of the proposed pilot power control is investigated by comparison with the case of full-pilot power.

▪ The pilot contamination effect is generally reduced.

▪ Notably, the 95% likely throughput in both UL and DL transmission is considerably improved.

▪ The case without pilot power control is sub-optimal at all times compared to the case with pilot power control.

▪ Pilot power control is a multi-objective optimization problem and providing an optimal solution that simultaneously optimizes each objective is quite challenging.



The authors present power allocation techniques in the UL of a CF-mMIMO network. The work aims at maximizing the sum rate and the minimum rate of CF massive MIMO systems using the ANN-based power control method.

▪ The effect of pilot contamination on the learning capabilities of deep neural networks (DNN) is highly minimal, as near-optimal performance is obtained.

▪ The shadowing effect degrades the performance of the suggested ANN-based power control considerably.



The DL performance of a partially distributed CF massive MIMO network under the assumption of different power control policies is investigated. These policies are extended to cover the case when the power control is coordinated only within subsets of APs and not across the subsets.

▪ The SCA policy is shown to outperform other power control policies in terms of the SE gains.

▪ Moreover, the gain in SE for SCA is higher for CB precoding compared to ZF precoding.

▪ Computational complexity and loss in sum SE are major bottlenecks to the optimal performance of the SCA policy.



In this correspondence, a DNN-based power allocation for CF massive MIMO is proposed. Particularly, DNNs are trained to perform both centralized and distributed power allocation with reduced computational complexity.

▪ A properly trained framework is shown to improve power allocation significantly.

▪ The gap between the distributed and centralized methods is substantial and requires further improvement.



The UL data transmission of a NOMA-aided CF-mMIMO is investigated. An optimal backhaul combining scheme to enhance the worst SINR among users in the network is proposed. The work is further analyzed using a max-min QoS power control problem which is iteratively solved by a successive inner approximation technique.

▪ A significant performance improvement is obtained with the proposed OBC compared to equal-gain combining and ZF combining.

▪ The SINR degrades dramatically as the number of antennas on each BS grows large.



A novel unsupervised learning-based to address the max-min rate problem in CF massive MIMO is proposed. A DNN is adopted and trained in an unsupervised manner to learn user power allocations. An online learning stage is further introduced to maximize the minimum user rate.

▪ The proposed DNN achieves comparable performance to the optimization-based max-min power control.

▪ The online complexity is relatively low due to a fairly simpler network configuration.

▪ The performance-complexity trade-off is quite modest.

▪ Processing complexity is sacrificed for improved max-min performance.

▪ Complex setups with a large number of APs and users as obtainable in practical configurations were omitted.



The performance of a CF-mMIMO having single-antenna APs and single-antenna users in the presence of imperfect CSI is investigated. An iterative robust minimum mean-squared error (RMMSE) precoder based on generalized loading and optimal power allocation techniques based on the maximization of the minimum SINR is proposed.

▪ The proposed RMMSE precoder outperformed other existing schemes regarding bit error rate, per-user rate, and sum rate.

▪ Although the computational cost is comparable to existing techniques, there is a need for low-cost low-complex optimization techniques.