No abstract

No abstract

No abstract

In this letter, we characterize the impact of diffuse scattering in dense urban deployments of millimeter-wave (mmWave) systems by using an advanced ray-launching (RL) simulation tool. Specifically, we first construct an RL-based methodology which is well-suited for the proposed analysis, and then investigate the received power distribution both with and without the contribution resultant from the diffuse scattering of rays for both line-of-sight (LOS) and non-LOS (NLOS) conditions. Simulation results are presented which show that, different from lower frequency bands, diffuse scattering makes a noticeable contribution to the total received power for NLOS links in the mmWave band. Therefore, when considering NLOS mmWave propagation in urban settings, it is critical to properly model and take into account specular attenuation due to surface roughness.Index Terms-Millimeter-wave propagation, ray launching, propagation in urban areas.

In the emerging 5G radio networks, beamformingcapable nodes are able to densely cover narrow areas with a high-quality signal. Such systems require high-level handover management system to proactively react to upcoming changes in signal quality, while restricting common issues such as pingponging or fast-shadowing of the signal. The utilization of deep learning in such a system allows for dynamic optimization of the system policies, based directly on the past behavior of the users and their channel responses. Our approach on handover optimization is purely non-deterministic, proving the idea that a self-learning network is able to efficiently manage user mobility in dense network scenario. The proposed network consists of feature extractors and dense layers. The model is trained in two stages, first serves as an initial weight setting in supervised fashion based on 3GPP model. The second stage is an optimization problem to reduce the number of unnecessary handovers while sustaining a high-quality connection. The model is also trained to predict the user location information as the second output. The presented results show that the number of handovers can be significantly reduced without decreasing the throughput of the system. The predicted location of the user has meter-level accuracy.