Cell-free massive mimo boosts wireless fairness and speeds for weakest users
Unifying Performance and Equity via Percentile Optimization and Cell-Free Massive MIMO
Information TheoryNetworking and Internet Architecture
Summary
Wireless networks often struggle to give slow connections enough speed. The authors study a special network design called user-centric cell-free massive MIMO to improve speeds for the slowest 10% of users. They created a new method to adjust signal power better and proved it works well in simulations, raising fairness and helping weak users much more than current methods. This approach also beats traditional big antenna setups and small cells, especially as network density increases.
What this means in practice
- •For network schedulers: Optimize wireless downlink power to maximize rates of weakest users in cell-free massive MIMO deployments.
- •For wireless infrastructure engineers: Design and deploy user-centric cell-free massive MIMO systems that outperform massive MIMO and small cells on fairness and low-percentile rates.
- •For wireless service providers: Offer more equitable wireless service with improved worst-case user rates to increase customer satisfaction in dense network areas.$Commercial implications: Enables commercially viable cell-free massive MIMO products that deliver better fairness and minimum speeds to customers.
Authors
Ahmad Khan, Hussein A. Ammar, Raviraj Adve
Abstract
Improving the throughput of the weakest users is a central objective of wireless resource allocation, and the objective around which the user-centric cell-free massive multiple-input multiple-output (UC-mMIMO) architecture is built. This paper brings the two together by formulating and solving percentile programs for UC-mMIMO networks. We maximize the sum-least-qth-percentile (SLqP) rate via downlink power control, a utility that targets the user equipments (UEs) in the lowest q percentile of the rate distribution and subsumes max-min-rate and sum-rate maximization as special cases. The problem is non-convex, non-smooth and NP-hard. The cyclic maximization approach of one of the previous percentile optimization series does not carry over: the coherent joint transmission technique introduces cross terms in the powers that destroy the block-concavity on which it relies. We develop a change of variables that restores block-concavity, yielding an algorithm with closed-form auxiliary updates and a concave power update, and we give a self-contained proof of non-decreasing convergence to a directional stationary point. Simulations establish two facts: first, percentile optimization retains its value in an architecture with no cell edges; relative to the weighted minimum mean-square error benchmark, it raises the average rate of the lowest-10th-percentile UEs by 56--72% and Jain's fairness index from below 0.92 to above 0.99. Second, compared directly with massive MIMO and small cells under the same SLqP utility, UC-mMIMO delivers the highest lower-percentile rates at every access point density tested, by up to 62% and 2.7$\times$ respectively, at the same fairness, and its advantage widens with densification.