Dynamic Mobility-Aware Interference Avoidance for Aerial Base Stations in Cognitive Radio Networks

Abstract

Aerial base station (ABS) is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an ABS network. With this consideration, an adaptive dynamic interference avoidance scheme is proposed in this work for ABSs coexisting with a primary network. In the proposed scheme, the mobile ABSs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity maximum flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the ABS network. In addition, a distributed algorithm is proposed to compute the optimal ABS moving directions. Moreover, the trade-off between the maximum flow and the shortest path trajectories is investigated and an energy-efficient approach is developed as well. Simulation results show that the proposed approach is effective in improving the maximum network flow and the energy-efficient approach can save up to 39% of the energy for the ABSs with marginal degradation in the maximum network flow.

Interference Avoidance Position Planning in UAV-Assisted Wireless Communication

“…Then, the SIR at UAV ⌊N 2⌋+1 is inspected (line 10). If this SIR satisfies the desired SIR of the system (SIR ⌊N 2⌋+1 ≥ Γ), the algorithm stops; otherwise, it moves the first and the last UAVs and starts over with a new desired value for Γ (lines [11][12][13][14]. Note that simultaneous identification of the positions achieved through using forward Algorithm 1: Distributed position planning for multiple UAVs input : Horizontal step size .…”

“…Our work contributes to the literature by addressing the relay position planning problem in the presence of an MSI in 3-D space. Considering different interpretations for the MSI, e.g., a primary transmitter in UAV cognitive radio networks [2], [11], an eNodeB in UAV-assisted LTE-U/WiFi public safety networks [12], a malicious user in drone delivery application, or a base station in surveillance application, our paper can be adapted to multiple real-world scenarios. We address the UAV placement planning in the presence of an MSI for both two-hop and multi-hop relay communication settings.…”

Reinforcement Learning for Interference Avoidance Game in RF-Powered Backscatter Communications

“…Select φ n ∈ Φ based on (19). 4 Observe the utility U U and the SINR n of the system. 5 s n+1 = SINR n .…”

“…Ambient backscatter communication has been introduced recently, which can provide the possibility of communication between two nodes using available ambient RF signals without using any active radio transmission [1], [2]. Thus, it can be a promising technology for beyond 5G [3] which is low-cost and energy efficient for battery-free applications such as internet of things (IoT), drones [4], MIMO communication [5], [6] and sensor networks. In such networks, the user tags can harvest energy from the ambient signals and transmit the information to the receiver over ambient RF carriers without using any dedicated signals.…”

Interference Avoidance in UAV-Assisted Networks: Joint 3D Trajectory Design and Power Allocation

“…Moreover, (7c) satisfies the condition that the interference produced by the each UAV at any SI is always less than a predefined threshold I max j for the jth SI. It is worth mentioning that given the UAVs transmission powers, there is no guarantee that the interference constraint is met at the co-existed primary network with the 3D trajectory design solely [20]. On the other hand, assuming fixed locations for UAVs, solely optimizing the UAV transmission powers leads to a poor performance at the UAV relay network.…”