arXiv subjects

Mina Khadem

Publications and source records attributed to Mina Khadem.

3 recordsLinked to original sources

arXiv2026

DRL-Based Spectrum Sharing for RIS-Aided Local High-Quality Wireless Networks

This paper investigates a smart spectrum-sharing framework for reconfigurable intelligent surface (RIS)-aided local high-quality wireless networks (LHQWNs) within a mobile network operator (MNO) ecosystem. Although RISs are often considered harmful due to interference, this work shows that properly controlled RISs can enhance quality of service (QoS). The proposed system enables temporary spectrum access for multiple vertical service providers (VSPs) by dynamically allocating radio resources. The spectrum is divided into dedicated subchannels assigned to individual VSPs and reusable subchannels shared among multiple VSPs, while RIS improves propagation conditions and zero-forcing (ZF) precoding is adopted at the multiantenna base station (BS) to cancel inter-user interference. We formulate a multi-VSP utility maximization problem that jointly optimizes subchannel assignment, transmit power, and RIS phase configuration while accounting for spectrum access costs, RIS leasing costs, and QoS constraints. The resulting mixed-integer non-linear program (MINLP) is modeled as a Markov decision process (MDP) and solved using deep reinforcement learning (DRL). Deep deterministic policy gradient (DDPG) and soft actor-critic (SAC) algorithms are developed and compared. Numerical results show that SAC generally outperforms DDPG in convergence, stability, and utility, particularly in larger-scale scenarios. In the reduced-scale ablation study, both joint DRL methods outperform the heuristic benchmark by at least 6.6% in final moving-average reward.

eess.SY↗

arXiv2024

AI-enabled Priority and Auction-Based Spectrum Management for 6G

In this paper, we present a quality of service (QoS)-aware priority-based spectrum management scheme to guarantee the minimum required bit rate of vertical sector players (VSPs) in the 5G and beyond generation, including the 6th generation (6G). VSPs are considered as spectrum leasers to optimize the overall spectrum efficiency of the network from the perspective of the mobile network operator (MNO) as the spectrum licensee and auctioneer. We exploit a modified Vickrey-Clarke-Groves (VCG) auction mechanism to allocate the spectrum to them where the QoS and the truthfulness of bidders are considered as two important parameters for prioritization of VSPs. The simulation is done with the help of deep deterministic policy gradient (DDPG) as a deep reinforcement learning (DRL)-based algorithm. Simulation results demonstrate that deploying the DDPG algorithm results in significant advantages. In particular, the efficiency of the proposed spectrum management scheme is about %85 compared to the %35 efficiency in traditional auction methods.

eess.SY↗

arXiv2023

Dynamic Fairness-Aware Spectrum Auction for Enhanced Licensed Shared Access in 6G Networks

This article introduces a new approach to address the spectrum scarcity challenge in 6G networks by implementing the enhanced licensed shared access (ELSA) framework. Our proposed auction mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism. Through comparison with traditional methods, the study demonstrates that the proposed auction method improves fairness significantly. We suggest using spectrum sensing and integrating UAV-based networks to enhance efficiency of the LSA system. This research employs two methods to solve the problem. We first propose a novel greedy algorithm, named market share based weighted greedy algorithm (MSWGA) to achieve better fairness compared to the traditional auction methods and as the second approach, we exploit deep reinforcement learning (DRL) algorithms, to optimize the auction policy and demonstrate its superiority over other methods. Simulation results show that the deep deterministic policy gradient (DDPG) method performs superior to soft actor critic (SAC), MSWGA, and greedy methods. Moreover, a significant improvement is observed in fairness index compared to the traditional greedy auction methods. This improvement is as high as about 27% and 35% when deploying the MSWGA and DDPG methods, respectively.

eess.SY↗