MSc. Emre Güres

In this paper, we focus on a cache-enabled multihop network, where the unmanned aerial vehicles (UAVs), the user equipment (UEs), or both can serve as relays to deliver contents to individual users from a ground base station (GBS). We formulate a power optimization problem with the objective to minimize the sum content delivery delay. We show the optimization problem is non-convex, thus, we propose a novel heuristic algorithm to allocate the power to individual contents at individual hops in multi-hop scenario. The proposed heuristic algorithm iteratively re-allocates the transmission power among the contents at the same transmitting node. To reduce the number of iteration, the proposed algorithm enables parallel power re-allocation for multiple content pairs and dynamic adaptation of power re-allocation steps, thereby enabling faster convergence of the algorithm. We demonstrate that our proposal reduces the average content delivery duration by up to 31.8% compared to state-of the-art works. At the same time, proposal is suitable for real systems due to a very fast convergence.

Handover management plays a vital role in load balancing by strategically transferring users from overloaded base stations to less congested stations, ultimately optimizing network performance. This paper proposes a novel handover management solution that leverages a two-layer cascaded fuzzy logic controller (FLC) for enhanced load balancing efficiency. The first layer focuses on signal quality evaluation for both the serving and target base stations. It employs separate fuzzy inference systems that consider reference signal received power (RSRP) and signal-to-interference-plus-noise ratio (SINR) to assess overall signal quality. This information is then fed into the second layer. Here, the FLC analyzes four key inputs: load levels of both the serving and target base stations, alongside the signal quality for each (obtained from the first layer's output). By employing a hierarchical architecture, the cascaded FLC significantly reduces the number of fuzzy rules required for decision-making, leading to faster processing and improved system performance. Simulations indicate the proposed FLC solution efficiently associates 80% of users with less congested stations (below 50% load level), ultimately increasing network capacity by up to 51.39% compared to competitive algorithms.

The caching paradigm has been introduced to alleviate backhaul traffic load and to reduce latencies due to massive never ending increase in data traffic. To fully exploit the benefits offered by caching, unmanned aerial vehicles (UAVs) and device-to-device (D2D) communication can be further utilized. In contrast to prior works, that strictly limits the content delivery routes up to two hops, we explore a multi-hop communications scenario, where the UAVs, the UEs, or both can relay the content to individual users. In this context, we formulate the problem for joint route selection and power allocation to minimize the overall system content delivery duration. First, motivated by the limitations of existing works, we consider the case where the nodes may transmit content simultaneously rather than sequentially and propose simple yet effective approach to allocate the transmission power. Second, we design a low-complexity greedy algorithm jointly handling route selection and power allocation. The simulation results demonstrate that the proposed greedy algorithm outperforms the benchmark algorithm by up to 56.98% in terms of content delivery duration while it achieves close-to-optimal performance.