Device Selection and Beamforming Optimization in Large-Scale mmWave IoT Networks

The joint provision of higher data rates and massive Internet of Things (IoT) connectivity has been identified as one of the key milestones toward beyond 5G (B5G). To this end, we investigate the issue of device selection and beamforming (BF) optimization assuming a large-scale IoT network using mmWaves. We formulate the considered problem as a network sum-rate maximization problem under Access Points’ load constraints, and where the BF parameters belong to discrete sets, as in practical cases. First, we mathematically prove the submodularity of the objective function, under specific yet reasonable assumptions. Based on the identified features of the problem at hand, we propose three different approaches to tackle this intricate optimization problem: 1) a Branch-and-Bound-based; 2) a Lagrangian Relaxation-based; and 3) a Greedy-based approach inspired by the submodular objective. The numerical results validate the three approaches, as they achieve a near-optimal sum rate in small network cases, and largely outperform benchmark schemes in terms of sum rate and individual rates. Among them, the proposed Greedy-based approach achieves the best sum rate with very low complexity, thereby providing excellent scalability.