Access Point Placement for Hybrid UAV-Terrestrial Small-Cell Networks

We address the problem of access point (AP) placement in small-cell networks with partial infrastructure flexibility, i.e., a novel class of problem in Beyond 5G, resultant from the utilization of unmanned aerial vehicles (UAVs) with AP functionalities (UAV-APs), to aid fixed wireless networks in coping with momentary peak-capacity requirements. We use the signal-to-generated-interference-plus-noise ratio (SGINR) metric as an alternative to the traditional signal-to-interference-plus-noise ratio (SINR) to quantify the effects of inter-cell interference (ICI) on the per-user capacity. From average SGINR, we derive the ICI-aware distortion measure leading to the Inter-AP Lloyd algorithm to obtain throughput-optimal AP placement for a fully flexible infrastructure. We then impose a hybridity constraint to the AP placement problem which turns a fraction of the network into a fixed infrastructure composed of terrestrial APs (T-APs) while the remainder is constituted by UAV-APs with flexibility in position. This newly formulated AP placement problem is solved by the proposed Lloyd-type algorithm called Hybrid AP Placement Algorithm (HAPPA). Furthermore, we present an initialization method for the Lloyd and Lloyd-type algorithms for Gaussian mixture models (GMMs) that offers an AP allocation leading to a higher rate compared to the k-means++ initialization. Finally, computer simulations show that the Inter-AP Lloyd algorithm can improve the performance of the worst users by up to 42.75% in achievable rate, assuming a fully flexible network. By using HAPPA on hybrid networks, we achieve improvements of up to 71.92% in sum rate over the fixed network and close the performance gap with fully flexible networks down to 2.02%, when an equal number of UAV-APs and T-APs is used. Further, our proposed initialization scheme always results in a balanced AP allocation, which means a more even distribution of users per AP, whereas the k-means++ scheme results in unbalanced allocations at least 30% of the time, resulting in a worse minimum rate.