Publications

2023

1. 

   Adversarial Attacks on Graph Neural Networks based Spatial Resource Management in P2P Wireless Communications

   Ahmad Ghasemi, Ehsan Zeraatkar, Majid Moradikia, and 2 more authors

   2023

   Abs arXiv Bib HTML

   This paper introduces adversarial attacks targeting a Graph Neural Network (GNN) based radio resource management system in point to point (P2P) communications. Our focus lies on perturbing the trained GNN model during the test phase, specifically targeting its vertices and edges. To achieve this, four distinct adversarial attacks are proposed, each accounting for different constraints, and aiming to manipulate the behavior of the system. The proposed adversarial attacks are formulated as optimization problems, aiming to minimize the system’s communication quality. The efficacy of these attacks is investigated against the number of users, signal-to-noise ratio (SNR), and adversary power budget. Furthermore, we address the detection of such attacks from the perspective of the Central Processing Unit (CPU) of the system. To this end, we formulate an optimization problem that involves analyzing the distribution of channel eigenvalues before and after the attacks are applied. This formulation results in a Min-Max optimization problem, allowing us to detect the presence of attacks. Through extensive simulations, we observe that in the absence of adversarial attacks, the eigenvalues conform to Johnson’s SU distribution. However, the attacks significantly alter the characteristics of the eigenvalue distribution, and in the most effective attack, they even change the type of the eigenvalue distribution.

   @misc{ghasemi2023adversarial,
     title = {Adversarial Attacks on Graph Neural Networks based Spatial Resource Management in P2P Wireless Communications},
     author = {Ghasemi, Ahmad and Zeraatkar, Ehsan and Moradikia, Majid and Seyed and Zekavat},
     year = {2023},
     eprint = {2312.08181},
     archiveprefix = {arXiv},
     primaryclass = {eess.SP},
     doi = {10.1109/TVT.2024.3360145},
   }

2. IEEE WiSEE

   Adversarial Attacks on Resource Management in P2P Wireless Communications

   Ahmad Ghasemi, Ehsan Zeraatkar, Majid Moradikia, and 1 more author

   In 2023 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), 2023

   Abs Bib HTML Code

   This paper explores adversarial attacks on a Graph Neural Network (GNN) based radio resource management in point-to-point (P2P) communications. The trained GNN model, which receives information from transceiver pairs, is targeted during the test phase. The paper introduces a novel adversarial attack that modifies the vertices of the GNN model, taking into account various constraints. The attack’s effectiveness is evaluated based on the number of users and signal-to-noise ratio (SNR). The proposed attack formulates optimization problems aimed at minimizing system communication quality, incorporating specific constraints.

   @inproceedings{10289604,
     author = {Ghasemi, Ahmad and Zeraatkar, Ehsan and Moradikia, Majid and Zekavat, Seyed Reza},
     booktitle = {2023 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)},
     title = {Adversarial Attacks on Resource Management in P2P Wireless Communications},
     year = {2023},
     volume = {},
     number = {},
     pages = {148-153},
     doi = {10.1109/WiSEE58383.2023.10289604},
     dimensions = {true},
   }

2022

1. IEEE USNC-URSI

   On Eigenvalue Distribution of Imperfect CSI in mmWave Communications

   Ahmad Ghasemi, and Seyed Reza Zekavat

   In 2022 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), 2022

   Abs Bib HTML

   Imperfect Channel State Information (CSI) reduces communication performance of mmWave communication that uses massive antennas. The knowledge of eigenvalue distribution of the imperfectly estimated CSI between transmitter (TX) and users’ antennas is key to techniques that suppress the impact of imperfect CSI on communication performance. This paper numerically depicts that this distribution follow a power-law distribution. In addition, this paper investigates the impact of channel estimation error, the number of antenna elements at TX and users, and the number of users on the eigen value distribution.

   @inproceedings{9887493,
     author = {Ghasemi, Ahmad and Zekavat, Seyed Reza},
     booktitle = {2022 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)},
     title = {On Eigenvalue Distribution of Imperfect CSI in mmWave Communications},
     year = {2022},
     volume = {},
     number = {},
     award = {Travel Award},
     pages = {56-57},
     doi = {10.23919/USNC-URSI52669.2022.9887493},
   }

2021

1. IEEE TWC

   Low-Cost mmWave MIMO Multi-Streaming via Bi-Clustering, Graph Coloring, and Hybrid Beamforming

   Ahmad Ghasemi, and Seyed A. Zekavat

   IEEE Transactions on Wireless Communications, 2021

   Abs Bib HTML

   This paper proposes and analyses a set of novel and optimum multi-streaming techniques for mmWave multi-input multi-output systems. It formulates an optimization problem that enables exploiting available uncorrelated paths between the transmitter (Tx) and the receiver (Rx) to enhance the system throughput. In the proposed approach, antenna arrays at Tx/Rx are modeled as a Bipartite graph. Next, two bi-clustering algorithms are applied to the graph to simultaneously cluster antenna elements at Tx and Rx. In addition, the paper shows how the selection of subchannels and their corresponding subantenna arrays can be reduced to a variant of the graph coloring problem, based on which, two algorithms are proposed to find the optimum subchannels and subantenna arrays. Moreover, the paper defines two new beamforming methods and proves that those methods satisfy constant modulus and total power constraints. The first modified beamforming method uses singular vectors (SVs) of subchannels between Tx/Rx and incorporates the Power Iteration algorithm to decrease singular value decomposition complexity. The second newly proposed beamforming method finds precoders/combiners without using SVs which reduces the computational complexity. Performance evaluations in terms data streaming sum-rate demonstrate that the proposed technique increases the throughput using a low processing complexity.

   @article{9351753,
     author = {Ghasemi, Ahmad and Zekavat, Seyed A.},
     journal = {IEEE Transactions on Wireless Communications},
     title = {Low-Cost mmWave MIMO Multi-Streaming via Bi-Clustering, Graph Coloring, and Hybrid Beamforming},
     year = {2021},
     volume = {20},
     number = {7},
     pages = {4113-4127},
     doi = {10.1109/TWC.2021.3056077},
   }

2. IJWIN

   An overview on position location: Past, present, future

   Seyed Zekavat, R Michael Buehrer, Gregory D Durgin, and 4 more authors

   International Journal of Wireless Information Networks, 2021

   Abs Bib HTML

   Prior to the 21st century, positioning technologies had limited applications including air traffic control, air and sea navigation, satellite communications and related military uses. Today, positioning technologies have deeply merged with daily life and enabled many novel sensors, systems and services. For example, navigation systems are the enablers of road traffic prediction, assisted and autonomous driving, and several aspects of healthcare. They have also facilitated worldwide services provided by companies such as Uber and Lyft. In fact, in many aspects of modern life, localization systems are deemed essential to day-to-day living and are contributing to our general well-being, the economy, and security. Accordingly, position location technologies have become key components of many worldwide industries. These positioning technologies include the Global Positioning System (GPS), WiFi-based indoor localization, cell-phone based localization (including the fusion of GPS, cell-tower based localization and dead-reckoning), and inertial/dead-reckoning techniques. Tracking technologies are also considered key components for localization, as are the more recently integrated concepts of machine learning and artificial intelligence. This paper provides a review of the history of localization, the main technological enablers of localization and assesses the future directions of localization methods.

   @article{zekavat2021overview,
     title = {An overview on position location: Past, present, future},
     author = {Zekavat, Seyed and Buehrer, R Michael and Durgin, Gregory D and Lovisolo, Lisandro and Wang, Zhonghai and Goh, Shu Ting and Ghasemi, Ahmad},
     journal = {International Journal of Wireless Information Networks},
     volume = {28},
     pages = {45--76},
     year = {2021},
     publisher = {Springer},
   }

2020

1. IEEE WOCC

   Joint Hybrid Beamforming and Dynamic Antenna Clustering for Massive MIMO

   Ahmad Ghasemi, and Seyed Reza Zekavat

   In 2020 29th Wireless and Optical Communications Conference (WOCC), 2020

   Abs Bib HTML

   This paper offers a new approach for antenna clustering and hybrid beamforming applicable to massive MIMO systems. Simultaneous clustering and hybrid beamforming across Tx and Rx antennas is an NP-hard problem. To address this issue, first, the paper proposes an antenna clustering that is applied to both Tx and Rx. In this regard, antenna arrays at Tx and Rx are modeled as a Bipartite graph and for the first time, one bi-clustering algorithm, Spectral Co-Clustering algorithm, is applied to achieve simultaneous clustering. Next, singular vectors of subchannels, which are the channels between subantenna arrays of Tx and Rx, are comprised to determine optimal precoders/combiners. Performance evaluations in terms of Tx-Rx data streaming sum-rate demonstrate the effectiveness of the proposed algorithm.

   @inproceedings{9114913,
     author = {Ghasemi, Ahmad and Zekavat, Seyed Reza},
     booktitle = {2020 29th Wireless and Optical Communications Conference (WOCC)},
     title = {Joint Hybrid Beamforming and Dynamic Antenna Clustering for Massive MIMO},
     year = {2020},
     volume = {},
     number = {},
     pages = {1-6},
     doi = {10.1109/WOCC48579.2020.9114913},
     award = {Best Paper Award},
   }

2014

1. IET

   Channel assignment based on bee algorithms in multi-hop cognitive radio networks

   Ahmad Ghasemi, Mohammad Ali Masnadi-Shirazi, Mehrzad Biguesh, and 1 more author

   IET Communications, 2014

   Abs HTML

   Spectrum management policies are responsible for poor utilisation of the radio spectrum. By carrying out dynamic spectrum management (DSM), cognitive radio (CR) can increase the radio spectrum in wireless systems efficiently. CR technology accounts for the improvement in the spectrum utilisation significantly. One issue of DSM in CR is the assignment of frequency channels among its users. Herein, a general model and four utility functions for optimal channel assignment in open spectrum systems such as CR networks have been defined. First, a new utility function with a better fairness than the other functions is proposed. Then, two new different channel assignment methods, based on the artificial bee colony (ABC) and bee swarm optimisation (BSO) algorithms, are proposed, whereas other certain evolutionary algorithms and colour sensitive graph colouring (CSGC) are used to compare the performances. In order to decrease the search space, based on the channel availability and interference constraints a mapping process between the channel assignment matrix and the position of the bees has been proposed. Our simulation results, compared to the optimal solutions, show that our algorithms drastically improve network performance by reducing interference.

2012

1. IST

   Spectrum allocation based on Artificial Bee Colony in Cognitive Radio networks

   Ahmad Ghasemi, Alireza Fakharzadeh Jahromi, Mohammad Ali Masnadi-Shirazi, and 2 more authors

   In 6th International Symposium on Telecommunications (IST), 2012

   Abs HTML

   Cognitive Radio (CR) has been regarded as a promising technology to improve the spectrum utilization significantly. One of the considered issues in CR is the allocation of frequency channels between its users. In this paper, at first, the model is presented, second, new spectrum assignment methods based on Artificial Bee Colony (ABC) algorithm is proposed. In this algorithm in order to decrease the search space is proposed a mapping process between the channel assignment matrix and the position of the bees of ABC based on the characteristics of the channel availability and the interference constraints. The Results show our proposed method has results mush near to the optimal values, which are calculated using exhaustive search. Corresponding results show that our proposed method fast converge in maximizing three objective functions which are: Max-Sum-Reward (MSR), Max-Min-Reward (MMR), and Max-Proportional-Fair (MPF); this feature makes our proposed method useful for practical applications.