arXiv subjects

Masoud Ghazikor

Publications and source records attributed to Masoud Ghazikor.

6 recordsLinked to original sources

arXiv2026

Partially-Observable Transmission Control for UAV-Enabled Federated Learning in IoT Networks

Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable. In this paper, we develop a packet-level transmission framework that captures buffer overflow, delay violations, and transmission errors, and uses the resulting packet delivery ratio (PDR) to represent partial-update reception through a packetized, Bernoulli-masked FL aggregation process. We then formulate a fairness-consensus bilevel (FCB) optimization that jointly controls (i) transmission thresholds to maximize the average PDR while reaching consensus under partial observability and (ii) transmission powers to improve the worst PDR and enforce fairness across IoT learners. To solve this problem, we propose an alternating FCB optimizer composed of a consensus-based threshold controller (CTC), which drives the IoT learners toward a PDR-efficient consensus on transmission thresholds, and a fairness-based power controller (FPC), which updates transmission powers to improve the worst PDR and ensure fairness under the resulting consensus thresholds. Numerical results on CNN-based FL tasks show that the FCB optimizer improves FL aggregation and training performance by enhancing packet-level update delivery, consistently outperforming baseline transmission policies.

cs.IT↗

arXiv2025

Performance Analysis of RIS-Assisted UAV Communication in NOMA Networks

This paper investigates the performance of downlink non-orthogonal multiple access (NOMA) communication in unmanned aerial vehicle (UAV) networks enhanced by partitionable reconfigurable intelligent surfaces (RISs). We analyze three types of links between base station (BS) and UAVs: direct, RIS-only indirect, and composite links, under both Line-of-Sight (LoS) and Non-LoS (NLoS) propagation. The RIS-only indirect link and direct link are modeled using double Nakagami-m and Nakagami-m fading, respectively, while the composite link follows a combined fading channel model. Closed-form expressions for the cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) are derived for all links, enabling tractable outage probability analysis. Then, we formulate a fairness-efficiency bilevel optimization problem to minimize the maximum outage probability among UAVs while minimizing the total number of required RIS reflecting elements. Accordingly, an RIS-assisted UAV Outage Minimization (RUOM) algorithm is proposed, which fairly allocates the NOMA power coefficients while minimizing the total number of RIS reflecting elements required, subject to NOMA-defined constraints, RIS resource limitations, and maximum allowable outage threshold. Simulation results validate the analytical models and demonstrate that the proposed RUOM algorithm significantly improves fairness and efficiency in BS-UAV communication.

cs.IT↗

arXiv2025

Channel-Aware Distributed Transmission Control and Video Streaming in UAV Networks

In this paper, we study the problem of distributed transmission control and video streaming optimization for UAVs operating in unlicensed spectrum bands. We develop a rigorous cross-layer analysis framework that jointly considers three inter-dependent factors: (i) in-band interference introduced by ground-aerial nodes at the physical layer, (ii) limited-size queues with delay-constrained packet arrival at the MAC layer, and (iii) video encoding rate at the application layer. First, we formulate an optimization problem to maximize the average throughput by optimizing the fading threshold. To this end, we jointly analyze the queue-related packet loss probabilities as well as the outage probability due to the low SINR. We introduce the DTC algorithm that maximizes the average throughput by adjusting transmission policies to balance the trade-offs between packet drop from queues vs. transmission errors due to low SINRs. Second, we incorporate the video distortion model to develop distributed PSNR optimization for video streaming. The formulated optimization incorporates two cross-layer parameters, specifically the fading threshold and video encoding rate. To tackle this problem, we develop the JDVT-EC algorithm that enhances the average PSNR for all nodes by fine-tuning transmission policies and video encoding rates to balance the trade-offs between packet loss and lossy video compression distortions. Through extensive numerical analysis, we thoroughly examine the proposed algorithms and demonstrate that they are able to find the optimal transmission policies and video encoding rates under various scenarios. Notably, our approach improves the average throughput by 1.7% to 51.65% compared to various baselines. Additionally, we demonstrate an average PSNR increase of 0.24 dB and 1.7 dB compared to separately optimizing the fading threshold and video encoding rate, respectively.

cs.IT↗

arXiv2025

pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data

Traditional Federated Learning (FL) approaches often struggle with data heterogeneity across clients, leading to suboptimal model performance for individual clients. To address this issue, Personalized Federated Learning (PFL) emerges as a solution to the challenges posed by non-independent and identically distributed (non-IID) and unbalanced data across clients. Furthermore, in most existing decentralized machine learning works, a perfect communication channel is considered for model parameter transmission between clients and servers. However, decentralized PFL over wireless links introduces new challenges, such as resource allocation and interference management. To overcome these challenges, we formulate a joint optimization problem that incorporates the underlying device-to-device (D2D) wireless channel conditions into a server-free PFL approach. The proposed method, dubbed pFedWN, optimizes the learning performance for each client while accounting for the variability in D2D wireless channels. To tackle the formulated problem, we divide it into two sub-problems: PFL neighbor selection and PFL weight assignment. The PFL neighbor selection is addressed through channel-aware neighbor selection within unlicensed spectrum bands such as ISM bands. Next, to assign PFL weights, we utilize the Expectation-Maximization (EM) method to evaluate the similarity between clients' data and obtain optimal weight distribution among the chosen PFL neighbors. Empirical results show that pFedWN provides efficient and personalized learning performance with non-IID and unbalanced datasets. Furthermore, it outperforms the existing FL and PFL methods in terms of learning efficacy and robustness, particularly under dynamic and unpredictable wireless channel conditions.

cs.LG↗

arXiv2024

Interference-Aware Queuing Analysis for Distributed Transmission Control in UAV Networks

In this paper, we investigate the problem of distributed transmission control for unmanned aerial vehicles (UAVs) operating in unlicensed spectrum bands. We develop a rigorous interference-aware queuing analysis framework that jointly considers two inter-dependent factors: (i) limited-size queues with delay-constrained packet arrival, and (ii) in-band interference introduced by other ground/aerial users. We aim to optimize the expected throughput by jointly analyzing these factors. In the queuing analysis, we explore two packet loss probabilities including, buffer overflow model and time threshold model. For interference analysis, we investigate the outage probability and packet losses due to low signal-to-interference-plus-noise ratio (SINR). We introduce two algorithms namely, Interference-Aware Transmission Control (IA-TC), and Interference-Aware Distributed Transmission Control (IA-DTC). These algorithms maximize the expected throughput by adjusting transmission policies to balance the trade-offs between packet drop from queues vs. transmission errors due to low SINRs. We implement the proposed algorithms and demonstrate that the optimal transmission policy under various scenarios is found.

cs.IT↗

arXiv2023

Exploring the Interplay of Interference and Queues in Unlicensed Spectrum Bands for UAV Networks

In this paper, we present an analytical framework to explore the interplay of signal interference and transmission queue management, and their impacts on the performance of unmanned aerial vehicles (UAVs) when operating in the unlicensed spectrum bands. In particular, we develop a comprehensive framework to investigate the impact of other interference links on the UAV as it communicates with the ground users. To this end, we provide closed-form expressions for packet drop probabilities in the queue due to buffer overflow or large queuing delay, which are expressed in terms of a transmission policy as a function of the channel fading threshold $β$. The overall packet loss caused either by interference signals or queuing packet drop is obtained, which, in turn, yields in obtaining the expected throughput performance. Through extensive numerical results, we investigate the impact of the channel fading threshold $β$, which plays an important role in balancing the trade-offs between packet loss due to queue drop or transmission error due to large interference levels.

cs.IT↗