Ahmad Ghasemi

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.

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.

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.

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.

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.