In unmanned aerial vehicle (UAV)-assisted wireless networks, the potential of the high probability of line-of-sight links could be a disadvantage for other nearby wireless networks because of interference caused by a UAV. For this reason, using interference mitigation techniques (IMTs) for UAV-assisted wireless networks is necessary to minimize interference effects. For instance, the mobility of UAVs gives a chance to manage interference better to avoid being a source of interference or being susceptible to interference. After the first-generation mobile network was introduced in the early 1980s, mobile communication systems were developed through several stages of evolution over the following few decades to satisfy rising demand and more stringent specifications. Different IMTs are needed due to these generations’ differences. Unlike previous research, this research presents a systematic review of IMTs in the current and future UAV-assisted wireless networks and discusses their challenges. We also review the current research trends of UAV IMTs and their future insights. Our motivation for conducting this research is the need for a systematic review addressing these issues. In this systematic review, we use a more precise classification of the IMTs for UAV-assisted wireless networks. We classify them into four different techniques, namely, adaptive modulation and coding schemes, dynamic antenna pattern adjustment, dynamic transmit power control, and sub-channel scheduling. Moreover, we discuss the IMTs for UAVs using B5G wireless technologies such as intelligent reflecting surfaces, rate-splitting, super modular massive multiple-input multiple-output, Terahertz communications, holographic beamforming, blockchain-based spectrum sharing, and artificial intelligence and machine learning schemes. Furthermore, we identify the challenges of UAV IMTs and their open research directions such as wireless radio frequency spectrum scarcity, lack of networking scalability, beam distortion, resource-constrained management, and new sources of interference. We believe this research will help researchers choose the best technique to mitigate the interference effects in UAV-assisted wireless networks.

Fountain codes were introduced to provide high reliability and scalability and low complexities for networks such as the Internet. Luby‐transform (LT) codes, which are the first realisation of Fountain codes, achieve the capacity of the binary erasure channel (BEC) asymptotically and universally. Most previous work on single‐layer Fountain coding targets the design via the right degree distribution. The left degree distribution of an LT code is left as a Poisson to protect the universality. For finite lengths, this is no longer an issue; thus, the author's focus is on designing better codes for the BEC at practical lengths. Their left degree shaping provides codes outperforming LT codes and all other competing schemes in the literature. At a bit error rate of 10 ⁻⁷ and packet length k = 256, their scheme provides a realised rate of 0.6 which is 23.5% higher than that of Sorensen et al. ’s decreasing‐ripple‐size scheme.

Unmanned aerial vehicles (UAVs), also named as drones, have become a modern model to provide a quick wireless communication infrastructure. They have been used when conventional base stations’ capacity is suffering in some extreme cases such as congestion inside the cell or a special event. This paper proposes an efficient three‐dimension (3D) placement of a single UAV‐assisted wireless network in such cases. Our proposed model assists the ground base station (GBS) using the UAV to serve arbitrary distributed users considering the impact of the obstacle blockage over the well‐known air‐to‐ground (A2G) path model. This work is aimed at optimizing the percentage of available bandwidth that must be provided to the UAV in order to maximize the number of served users. In addition, it finds the 3D placement of the UAV base station (UAVBS) that maximizes the number of served users, each with maximum quality‐of‐service (QoS). The exhaustive search and particle swarm optimization (PSO) algorithms are used to find the problem’s solution.

Pilot overhead poses a significant challenge in mmWave massive multiple-input multiple-output (MIMO) systems, as it fundamentally limits the accurate acquisition of channel state information (CSI). In this paper, we propose an enhanced adaptive channel estimation technique that leverages compressive sensing (CS) principles to effectively mitigate pilot overhead while maintaining high estimation accuracy. The proposed approach combines compressive sampling matching pursuit (CoSaMP) and sparsity adaptive matching pursuit (SAMP) algorithms, augmented by a novel iterative reweighting strategy and adaptive thresholding mechanism. The simulation results demonstrate that the proposed method achieves superior normalized mean square error (NMSE) performance compared to traditional CS-based techniques. Furthermore, the proposed technique achieves a substantial reduction in computational complexity and pilot overhead compared to traditional channel estimation methods, offering significant improvements in the performance of mmWave MIMO systems.

Due to natural disasters, unmanned aerial vehicles (UAVs) can be deployed as aerial wireless base stations when conventional cellular networks are out of service. They can also supplement the mobile ground station to provide wireless devices with improved coverage and faster data rates. Cells on wheels (CoWs) can also be utilized to provide enhanced wireless coverage for short‐term demands. In this paper, a single CoW cooperates with a single UAV in order to provide maximum wireless coverage to ground users. The optimization problem is formulated to find the following: (1) the optimal 2D placement of the CoW, (2) the optimal 3D placement of the UAV, (3) the optimal bandwidth allocation, (4) the percentage of the available bandwidth that must be provided to the CoW and UAV, and (5) the priority of wireless coverage; which maximizes the number of covered users. We utilize the exhaustive search (ES) and particle swarm optimization (PSO) algorithms to solve the optimization problem. The effectiveness of the proposed algorithms is validated using simulation results.