arXiv subjects

Mohammad Reza Fasihi

Publications and source records attributed to Mohammad Reza Fasihi.

7 recordsLinked to original sources

arXiv2026

Object Tracking Incorporating Transfer Learning into Unscented and Cubature Kalman Filters

We present a novel filtering algorithm that employs Bayesian transfer learning to address the challenges posed by mismatched intensity of the noise in a pair of sensors, each of which tracks an object using a nonlinear dynamic system model. In this setting, the primary sensor experiences a higher noise intensity in tracking the object than the source sensor. To improve the estimation accuracy of the primary sensor, we propose a framework that integrates Bayesian transfer learning into an Unscented Kalman Filter (UKF) and a Cubature Kalman Filter (CKF). In this approach, the parameters of the predicted observations in the source sensor are transferred to the primary sensor and used as an additional prior in the filtering process. Our simulation results show that the transfer learning approach significantly outperforms the conventional isolated UKF and CKF. Comparisons to a form of measurement vector fusion are also presented.

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arXiv2025

QoS-Aware State-Augmented Learnable Framework for 5G NR-U/Wi-Fi Coexistence: Impact of Parameter Selection and Enhanced Collision Resolution

Unlicensed spectrum supports diverse traffic with stringent Quality-of-Service (QoS) requirements. In NR-U/Wi-Fi coexistence,the values of MAC parameters critically influence delay, collision behavior, and airtime fairness and efficiency. In this paper, we investigate the impact of (i) cost scaling and violation modeling, (ii) choice of MAC parameters, and (iii) an enhanced collision resolution scheme for the Listen-Before-Talk (LBT) mechanism on the performance of a state-augmented constrained reinforcement learning controller for NR-U/Wi-Fi coexistence. Coexistence control is formulated as a constrained Markov decision process with an explicit delay constraint for high-priority traffic and fairness as the optimization goal. Our simulation results show three key findings: (1) signed, threshold-invariant cost scaling with temporal smoothing stabilizes learning and strengthens long-term constraint adherence; (2) use of the contention window parameter for control provides smoother adaptation and better delay compliance than other MAC parameters; and (3) enhanced LBT significantly reduces collisions and improves airtime efficiency. These findings provide practical insights for achieving robust, QoS-aware coexistence control.

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arXiv2025

Incorporating Bayesian Transfer Learning into Particle Filter for Dual-Tracking System with Asymmetric Noise Intensities

Using Bayesian transfer learning, we develop a particle filter approach for tracking a nonlinear dynamical model in a dual-tracking system where intensities of measurement noise for both sensors are asymmetric. The densities for Bayesian transfer learning are approximated with the sum of weighted particles to improve the tracking performance of the primary sensor, which experiences a higher noise intensity compared to the source sensor. We present simulation results that validate the effectiveness of the proposed approach compared to an isolated particle filter and transfer learning applied to the unscented Kalman filter and the cubature Kalman filter. Furthermore, increasing the number of particles shows an improvement in the performance of transfer learning applied to the particle filter with a higher rate compared to the isolated particle filter. However, increasing the number of particles raises computational time per step. Moreover, the performance gain from incorporating Bayesian transfer learning is approximately linearly proportional to the absolute difference value between the noise intensities of the sensors in the dual-tracking system.

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arXiv2025

QoS-aware State-Augmented Learnable Algorithm for Wireless Coexistence Parameter Management

Efficient and fair coexistence in unlicensed spectrum is essential to support heterogeneous networks such as 5G NR-U and Wi-Fi, which often contend for shared wireless resources. We introduce a general framework for wireless Coexistence Parameter Management (CPM) based on state-augmented constrained reinforcement learning. We propose a novel algorithm, QaSAL-CPM, which incorporates state-augmentation by embedding the dual variables in the constrained optimization formulation directly into the agent's observation space. This method enables the agent to respond to constraint violations in real time while continuing to optimize a primary performance objective. Through extensive simulations of 5G NR-U and Wi-Fi coexistence scenarios, we show that QaSAL-CPM achieves reliable QoS compliance and improved policy robustness across various transmitter densities compared to previous approaches. The proposed framework offers a scalable and adaptive solution for real-time coexistence optimization in next-generation wireless networks.

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arXiv2025

Device-to-Device Communication in 5G/6G: Architectural Foundations and Convergence with Enabling Technologies

Device-to-Device (D2D) communication is a promising solution to meet the growing demands of 5G and future 6G networks by enabling direct communication between user devices. It enhances spectral efficiency (SE) and energy efficiency (EE), reduces latency, and supports proximity-based services. As wireless systems evolve toward 5G and 6G paradigms, the integration of D2D with advanced cellular technologies introduces new opportunities and challenges. This survey paper reviews the architectural foundations of D2D communication and explores its integration with key 5G/6G enabling technologies. We review standardization efforts, analyze core challenges, and highlight future research directions to unlock the full potential of D2D in next-generation wireless networks.

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arXiv2025

QaSAL: QoS-aware State-Augmented Learnable Algorithms for Coexistence of 5G NR-U/Wi-Fi

With the increasing demand for wireless connectivity, ensuring the efficient coexistence of multiple radio access technologies in shared unlicensed spectrum has become an important issue. This paper focuses on optimizing Medium Access Control (MAC) parameters to enhance the coexistence of 5G New Radio in Unlicensed Spectrum (NR-U) and Wi-Fi networks operating in unlicensed spectrum with multiple priority classes of traffic that may have varying quality-of-service (QoS) requirements. In this context, we tackle the coexistence parameter management problem by introducing a QoS-aware State-Augmented Learnable (QaSAL) framework, designed to improve network performance under various traffic conditions. Our approach augments the state representation with constraint information, enabling dynamic policy adjustments to enforce QoS requirements effectively. Simulation results validate the effectiveness of QaSAL in managing NR-U and Wi-Fi coexistence, demonstrating improved channel access fairness while satisfying a latency constraint for high-priority traffic.

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arXiv2025

Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learning

Coexistence of 5G new radio unlicensed (NR-U) and Wi-Fi is highly prone to the collisions among NR-U gNBs (5G base stations) and Wi-Fi APs (access points). To improve performance and fairness for both networks, various collision resolution mechanisms have been proposed to replace the simple listen-before-talk (LBT) scheme used in the current 5G standard. We address two gaps in the literature: first, the lack of a comprehensive performance comparison among the proposed collision resolution mechanisms and second, the impact of multiple traffic priority classes. Through extensive simulations, we compare the performance of several recently proposed collision resolution mechanisms for NR-U/Wi-Fi coexistence. We extend one of these mechanisms to handle multiple traffic priorities. We then develop a traffic-aware multi-objective deep reinforcement learning algorithm for the scenario of coexistence of high-priority traffic gNB user equipment (UE) with multiple lower-priority traffic UEs and Wi-Fi stations. The objective is to ensure low latency for high-priority gNB traffic while increasing the airtime fairness among the NR-U and Wi-Fi networks. Our simulation results show that the proposed algorithm lowers the channel access delay of high-priority traffic while improving the fairness among both networks.

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