Search for: All records

Recently emerging WiGig systems experience limited coverage and signal strength fluctuations due to strict line-of-sight (LoS) connectivity requirements. In this paper, we address these shortcomings of WiGig communication by exploiting two emerging technologies in tandem, namely the reconfigurable intelligent surface (RIS) and unmanned aerial vehicles (UAVs). In ultra-dense traffic sites (referred to as hotspots) where WiGig nodes or User Devices (UDs) experience complex propagation and non-line-of-sight (non-LoS) environment, we envision the deployment of a UAV-mounted RIS system to complement the WiGig base station (WGBS) to deliver services to the UDs. However, commercially available UAVs have limited energy (i.e., constrained flight time). Therefore, the trajectory of our considered UAV needs to be locally estimated to enable it to serve multiple hotspots while minimizing its energy consumption within the WGBS coverage boundaries. Since this tradeoff problem is computationally expensive for the resource-constrained UAV, we argue that sequential learning can be a lightweight yet effective solution to locally solve the problem with a low impact on the available energy on the UAV. We formally formulate this problem as a contextual multi-armed bandit (CMAB) game. Then, we develop the linear randomized upper confidence bound (Lin-RUCB) algorithm to solve the problem effectively. We regard the UAV as the bandit learner, which attempts to maximize its attainable rate (i.e., the reward) by serving distinct hotspots in its trajectory that we treat as the arms of the considered bandit. The context is defined as the hotspots’ locations provided using GPS (global positioning system) service and the reward history of each hotspot. Our proposal accounts for the energy expenditure of the UAV in moving from one hotspot to another within its battery charge lifetime. We evaluate the performance of our proposal via extensive simulations that exhibit the superiority of our proposed. 

The emerging Sixth Generation (6G) communication networks promising 100 to 1000 Gb/s rates and ultra-low latency (1 millisecond) are anticipated to have native, embedded Artificial Intelligence (AI) capability to support a myriad of services, such as Holographic Type Communications (HTC), tactile Internet, remote surgery, etc. However, these services require ultra-reliability, which is highly impacted by the dynamically changing environment of 6G heterogeneous tiny cells, whereby static AI solutions fitting all scenarios and devices are impractical. Hence, this article introduces a novel concept called the softwarization of intelligence in 6G networks to select the most ideal, ultra-fast optimal policy based on the highly varying channel conditions, traffic demand, user mobility, and so forth. Our envisioned concept is exemplified in a Multi-Armed Bandit (MAB) framework and evaluated within a use case of two simultaneous scenarios (i.e., Neighbor Discovery and Selection (NDS) in a Device-to-Device (D2D) network and aerial gateway selection in an Unmanned Aerial Vehicle (UAV)-based under-served area network). Furthermore, our concept is evaluated through extensive computer-based simulations that indicate encouraging performance. Finally, related challenges and future directions are highlighted.