Jul 2026· International Journal of Communication Systems· Vol 39· 0 citations· 41 references
TL;DR
The secondary throughput for NOMA‐based rotating UAV‐equipped CR networks under conditions of imperfect successive interference cancellation (i‐SIC) has been evaluated through closed‐form mathematical expressions and every analytical closed form equation has been validated using the MATLAB simulation testbed.
Abstract
This study investigates the performance of a cooperative non‐orthogonal multiple access (NOMA)‐based hybrid cognitive radio (CR) network aided by rotating unmanned aerial vehicle (UAV). RF energy harvesting, Nakagami‐m fading between terrestrial users, and line‐of‐sight (LOS) linkages between UAV and different users have been taken into account for this proposed CR network. UAV rotates periodically over a circular region of interest (CROI) to locate ideal primary user (PU) bands, which is used by UAV to establish links between secondary users (SU) that experience severe fading on direct connections. All sources and the UAV make sensing judgments and transmit them to the fusion center (FC) in order to collaboratively determine the condition of a PU band using AND rule. According to the fusion center result, SU and UAV make adaptive transition between overlay and underlay protocols in order to improve spectrum efficiency (SE) of the system. The secondary throughput for NOMA‐based rotating UAV‐equipped CR networks under conditions of imperfect successive interference cancellation (i‐SIC) has been evaluated through closed‐form mathematical expressions. The proposed system throughput is examined for different network factors, like the radius of the UAV trajectory, height of UAV, and harvesting angle. Lastly, every analytical closed form equation has been validated using the MATLAB simulation testbed.
Uncrewed aerial vehicle (UAV)-assisted free-space optics (FSO) communication provides an effective means to extend the capability and resilience of existing network infrastructures by offering flexible aerial relaying. In particular, the use of UAVs as relay nodes helps to mitigate the strict line-of-sight (LoS) requirements of conventional FSO links. In this work, a multi-UAV-based FSO communication system employing decode-and-forward (DF) relaying with energy harvesting (EH) at the relay is investigated, where both partial relay selection (PRS) and opportunistic relay selection (ORS) strategies are adopted to determine the optimal UAV for data forwarding. The optical wireless channel is modeled using two comprehensive turbulence distributions, namely the Málaga distribution and the doubly inverted Gamma–Gamma (IGGG) distribution, which jointly capture the effects of atmospheric turbulence, atmospheric attenuation, 2D and 3D pointing error misalignment, and angle-of-arrival (AoA) fluctuations. Closed-form expressions are derived for key performance metrics, including outage probability (OP), while the average symbol error rate (SER) and ergodic capacity (EC) are evaluated using Gauss–Laguerre (GL) quadrature under both relay selection schemes and channel models. Moreover, an asymptotic performance analysis is carried out in the high signal-to-noise ratio (SNR) regime, through which the diversity gain of the proposed system is explicitly characterized. The accuracy of the analytical results is validated through extensive Monte-Carlo simulations. Numerical results illustrate the influence of relay selection strategies, turbulence severity, and system parameters on the overall performance and provide clear insights into the achievable diversity gains and robustness of multi-UAV-assisted FSO systems under realistic atmospheric conditions.
Prashant Sharma, Deepshikha Singh, S. R.· IEEE Open Journal of the Com...· 0 citations
Due to the sparse node distribution and the harsh propagation environment in Maritime Internet of Things (MIoT), traditional local mobile self-organizing networks relying on direct Device-to-device (D2D) communications face limited coverage and frequent link outages. To address these issues, this letter investigates the unmanned aerial vehicle (UAV)-assisted MIoT, where UAVs serve as aerial base stations to provide enhanced coverage. Using stochastic geometry, we develop a system model that consists of the D2D tier and the UAV tier, respectively employing the Fluctuating Two-Ray (FTR) model and Nakagami- $m$ model. Then, analytical expressions of coverage probability and achievable rate, along with their tight upper and lower bounds, are derived. Simulation results validate the theoretical analysis, confirming both the coverage improvement from UAV deployment and the effectiveness of the FTR model. It is further shown that by optimizing the UAV deployment with appropriate density, altitude, and antenna array size, the inter-layer interference can be effectively mitigated thus improving the coverage probability and achievable rate.
Xinyu Du, Xian Zhang, Jiu Xie et al.· IEEE Wireless Communications...· 0 citations
Flying ad hoc networks (FANETs), consisting of selforganizing unmanned aerial vehicles (UAVs), offer infrastructureless connectivity in a wide range of mission critical applications. In this paper, a non-orthogonal multiple access (NOMA) based FANET is considered in which cooperative UAVs act as decodeand-forward relays to serve the user equipments (UEs) in their coverage area. The partitioning of UEs into clusters around their nearest UAV and the UAV locations are determined using transmit signal-to-noise ratio (SNR) weighted K-means clustering. The weighting accounts for the constraint that UAVs in practical deployments may operated with different transmit powers due to, e.g., targeted coverage range, available energy budget, actual or intended mission duration. Simulation results for the outage probability and sum rate of the NOMA-based FANET with transmit SNR weighted K-means clustering for Nakagami-m fading are provided for a variety of system parameters.
T. Chu, H. Zepernick, Alexander Westerhagen· IEEE International Conferenc...· 0 citations
Driven by the vision of a thriving low-altitude economy and aiming to provide on-demand services for diverse entities, this paper investigates an integrated sensing and communication (ISAC)-enabled low-altitude wireless network (LAWN). Benefiting from flexible mobility and cost-effective cooperative deployment, multiple ISAC-enabled uncrewed aerial vehicles (UAVs) are emerging as an ISAC paradigm for on-demand deployment in LAWN. However, due to the complex inter-UAV interference and resource coupling in LAWN, it is difficult to properly coordinate different constrained resources, including spatial deployment, energy, and wireless channels, to simultaneously meet the sensing and communication requirements. To address these challenges, this paper formulates a sensing–communication optimization (SCO) problem in LAWN by jointly optimizing subcarrier allocation, transmit power allocation, and three-dimensional (3D) UAV deployments to maximize network utility while satisfying quality of service (QoS) requirements for multiple users and target sensing mutual information (MI) requirements. To enable efficient solutions, we propose a hierarchical optimization approach that vertically decouples the SCO problem into two subproblems: a top level employing a Gibbs Sampling–based multi-UAV 3D deployment algorithm for efficient exploration and deployment optimization, and a bottom level performing resource allocation via a dual-based joint power and subcarrier allocation algorithm. Simulation results demonstrate that the proposed approach achieves a favorable trade-off between communication and sensing and significantly enhances the overall performance and adaptability of the LAWN.
Cheng Ma, Zewei Jing, Qinghai Yang et al.· IEEE Transactions on Wireles...· 0 citations
This paper investigates a UAV-assisted multi-panel intelligent reflecting surface (IRS) architecture to enhance link reliability and spectral efficiency in next-generation wireless networks. Unlike conventional IRS deployments with fixed geometries, UAV mobility introduces rapidly varying propagation conditions that challenge classical diversity and combining schemes. To address this, a Maximal Ratio Combining with Generalized Selection (MRC-GS) framework is proposed, in which the UAV optimizes its aerial position while IRS panels are selectively activated based on their contribution to the received signal. A joint optimization problem is formulated to design the IRS phase shifts, UAV position, and receiver combining weights. Hardware impairments are explicitly incorporated in the receiver combining design via impairments-aware noise covariance. The IRS phase optimization adopts a tractable effective-channel formulation and establishes the equivalence between maximizing the effective channel gain and signal-to-impairments-plus-noise ratio (SINR) under the adopted hardware impairment model. By activating only a dominant subset of IRS panels, the proposed approach improves energy efficiency and reduces computational overhead compared to full-activation schemes. Moreover, a low-complexity closed-form phase update is also provided for large-scale deployments. Simulation results demonstrate that the proposed MRC-GS-enabled UAV-IRS system reduces bit error rate (BER) and enhances spectral efficiency compared to benchmark single-IRS and full-combining schemes.
Nasir Saeed, Shumaila Javaid, Naveed Khan et al.· IEEE Open Journal of the Com...· 0 citations
A cooperative resource-allocation framework for C-NOMA-enabled multi-uncrewed aerial vehicle (UAV) millimeter-wave (mmWave) networks with two-dimensional (2D) HB and an EE-maximization problem that jointly considers user subclustering, power allocation (PA), and subchannel assignment (SA) under transmit-power and SIC constraints is developed.
Muhammet Hevesli, A. M. Seid, Mohamed M. Abdallah et al.· IEEE Open Journal of the Com...· 0 citations
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