LAUSR

In this letter, we are committed to obtaining load-balanced user association and optimal beamforming in an energy-saving downlink distributed Millimeter-Wave (mmWave) network, with per-user quality of service (QoS) requirements, user association constraints and remote radio unit (RRU) transmission power limits. A mixed discrete nonlinear programming (MINLP) problem is formulated, which is NP-hard. The traditional sparse beamforming method (SBM) can jointly optimize the user association and the beamforming vector by inducing the beamformer sparsity via the reweighted $\ell _{1}$ norm technique. However, such existing scheme may cause unbalanced load of RRU, thus degrading the mmWave network performance. For better load-balanced user association, we introduce the Fuzzy inference System (FIS) to pursue further advancement of SBM. In addition, the good scalability of FIS framework can help itself to be extended to more indicators in the next generation of communication systems. Simulation results show that the improved algorithm can obtain a load-balanced user association, meanwhile the transmit power consumption of mmWave networks can be significantly reduced.