User Rate Optimization for Fronthaul-Constrained Cell-Free Massive MIMO-OFDM Systems: A Quadratic Transform-Based Approach

As a promising candidate for the future generations of mobile communication networks, the cell-free (CF) massive multiple-input multiple-output (mMIMO) networks have been shown to provide high spectral efficiency (SE) and more uniform signal coverage than cellular mMIMO networks, enabling smart cities more diverse application services. The fronthaul link in such networks, defined as the transmission link between the central processing unit and the access point, requires a high capacity, but is often constrained. Hence, the optimization of the user rate under the limited capacity of fronthaul is the key problem to be solved in a CF networks. In this paper, we consider the downlink transmission of CF mMIMO orthogonal frequency division multiplexing (OFDM) systems with constrained transmit power and fronthaul capacity. Based on the min-max and sum-max principles, quadratic transform-min max (QT-MM) and quadratic transform-sum max (QT-SM) optimization algorithms are proposed, respectively. Simulation results show that compared with the weighted minimum mean square error (WMMSE) algorithm, both of the proposed algorithms can effectively prevent very low user rates when the fronthaul capacity is limited. When the fronthaul link capacity constraint is high, the QT-SM algorithm has better rate performance than the distributed WMMSE-d algorithm and the centralized WMMSE-c algorithm.

Automated summary

Cell-free massive MIMO networks provide high spectral efficiency and uniform signal coverage for smart city applications, but the limited capacity of the fronthaul link constrains the optimization of user rate. This paper proposes QT-MM and QT-SM optimization algorithms for CF mMIMO OFDM systems, which effectively prevent very low user rates when the fronthaul capacity is limited.