arXiv subjects

Seyed Mohammad Karbasi

Publications and source records attributed to Seyed Mohammad Karbasi.

3 recordsLinked to original sources

arXiv2026

Robustness Guarantees for Optimal RIS Placement in Self-Localization Under Placement Errors

This paper studies the effect of Reconfigurable Intelligent Surface (RIS) placement errors on optimal RIS deployment for Time-of-Flight (ToF)-based self-localization. We consider a setup in which a source transmits a signal, receives the RIS-reflected echo, and estimates its own position from the corresponding ToF measurements. Under the conditions of moderate, i.i.d. Gaussian RIS position errors and i.i.d. Gaussian ToF noise, we demonstrate that the placement uncertainty manifests as a geometry-independent scaling factor in the Cramér-Rao Bound (CRB). Consequently, we prove that the optimal RIS placement remains unchanged by the presence of RIS position errors. This result is established for A-, D-, and E-optimality criteria. Simulation results verify the analysis and show that optimized RIS deployments retain their advantage even in the presence of placement uncertainty.

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arXiv2026

Joint Communication Sensing Beamforming and Uplink Power Control for Network-Assisted Full-Duplex Cell-Free ISAC Systems

In this paper, we investigate the problem of designing joint downlink (DL) communication beamformer and uplink (UL) power as well as sensing beamformer in a multi static integrated sensing and communication (ISAC) enabled cell free massive multiple input multiple output (CF mMIMO) system. To this goal, we first consider two priority based beamforming strategies. In the communication prioritized sensing design, we first design the communication beamformers and then the sensing beamformer is projected onto the nullspace of the effective communication channels to suppress its additional interference to both the DL UEs and UL receivers. In the sensing prioritized communication design, we select the sensing beamformer first according to the target direction, while the communication beamformers are subsequently optimized using a max min SINR formulation. In addition to these priority based designs, we formulate a sensing centric joint optimization problem to determine the optimal DL communication beamformers, sensing beamformer, and UL UE transmit powers. Our objective is to maximize the sensing SINR while guaranteeing individual DL and UL SINR requirements and satisfying the per AP and per UE transmitpower constraints. Since the resulting problem is non convex because of the coupled beamforming and UL power variables, the beamforming vectors are lifted into positive semidefinite transmit covariance matrices and semidefinite relaxation (SDR) is applied.

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arXiv2025

Multi-UAV Enabled Integrated Sensing and Wireless Powered Communication: A Robust Multi-Objective Approach

In this paper, we consider an integrated sensing and communication (ISAC) system with wireless power transfer (WPT) where multiple unmanned aerial vehicle (UAV)-based radars serve multiple clusters of energy-limited communication users in addition to their sensing functionality. In this architecture, the radars sense the environment in phase 1 (namely sensing phase) and meanwhile, the communications users (nodes) harvest and store the energy from the radar transmit signals. The stored energy is then used for information transmission from the nodes to UAVs in phase 2, i.e., uplink phase. Performance of the radar systems depends on the transmit signals as well as the receive filters; the energy of the transmit signals also affects the communication network because it serves as the source of uplink powers. Therefore, we cast a multi-objective design problem addressing performance of both radar and communication systems via optimizing UAV trajectories, radar transmit waveforms, radar receive filters, time scheduling and uplink powers. The design problem is further formulated as a robust non-convex optimization problem taking into account the the user location uncertainty. Hence, we devise a method based on alternating optimization followed by concepts of fractional programming, S-procedure, and tricky majorization-minimization (MM) technique to tackle it. Numerical examples illustrate the effectiveness of the proposed method for different scenarios.

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