Ozan Alp Topal

I have been working on different aspects of mmWave network deployment in indoor dense spaces (IDSs). The related publications on different directions are given as below:

1. Channel modeling for IDS: Under IDS environment, we model the propagation channel at 28 GHz under different geometry, material, and denseness cases to illustrate the effects of these factors on the signal propagation. We conduct extensive RT simulations by taking into account material characteristics from the literature.
   1.  "mmWave Communications for Indoor Dense Spaces: Ray-Tracing Based Channel Characterization and Performance Comparison," ICC 2022
   2. Millimeter Wave Channel Modeling and Coverage Analysis for Indoor Dense Spaces, IEE Transactions on Vehicular Technology, under review.
2. Network deployment in IDS: In an IDS, minimizing the number of APs deployed is essential, since it reduces overall hardware cost, efforts in wiring APs during installation, and weight. Based on the limited user mobility and the static nature of the IDS environment, we can jointly solve AP deployment and resource allocation. One can benefit by considering the available radio resources such as bandwidth, time,  access point cooperation, power, and antenna limits of the access points as part of the AP deployment problem to further reduce the number of deployed APs. We cast and solve mixed integer binary optimization problems to find the minimum number of APs for different levels of AP cooperation.
   1. "Optimal Joint Access Point Placement and Resource Allocation for Indoor mmWave Communications," ICC 2023.
3. Coverage extension with RIS in IDS: To further the cost reduction, we work on deploying reflective intelligent surfaces (RISs) in the IDS, so that we can extend the coverage from a single AP and deploy less number of APs as a result.
   1. "mmWave Coverage Extension Using Reconfigurable Intelligent Surfaces in Indoor Dense Spaces," ICC 2023.