Tootfinder

Optimized Multi-Contrast Self-Supervised MRI Reconstruction using Learned k-space Partitioning
Brenden Kadota, Charles Millard, Mark Chiew
https://arxiv.org/abs/2606.19182 https…

Maximizing Connectivity of Uplink RIS-Assisted UAV Networks
Mohammed Saif, Shahrokh Valaee
https://arxiv.org/abs/2606.11523 https://arxiv.org/pdf/2606.11523 https://arxiv.org/html/2606.11523
arXiv:2606.11523v1 Announce Type: new
Abstract: In this paper, we present a new approach for unmanned aerial vehicle (UAV) positioning and reconfigurable intelligent surface (RIS) partitioning to enhance connectivity of uplink RIS-assisted UAV networks. To achieve this, our approach optimizes RIS-aided link selection, RIS partitioning, and UAV positions to maximize network connectivity characterized by its Fiedler value. Meanwhile, it maintains a specific signal-to-interference plus noise ratio (SINR) constraint for user equipment (UE), which is influenced by RIS partitioning and UAV reliability. The network connectivity optimization problem is formulated using the Fiedler value subject to RIS elements allocation and SINR constraints. This problem is a computationally expensive combinatorial optimization, necessitating an efficient iterative approach. In particular, we propose a perturbation method for RIS-aided link selection, and derive a closed-form solution for RIS partitioning, with each partition tailored to optimize SINR for individual UAV. For the given RIS-aided links and RIS partitioning, we then show that the problem of UAV positioning can be formulated as a low complexity semi-definite programming (SDP) optimization problem, which can be solved using off-the-shelf CVX solvers. Our simulations show the potential gain of UAV positioning and RIS partitioning compared to the benchmark schemes from the literature.
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IOAH3: Importance-Driven Adaptive Spatial Partitioning
Ehsaneddin Jalilian
https://arxiv.org/abs/2606.18280 https://arxiv.org/pdf/2606.18280

Joint Movable Antenna Positioning and RIS Partitioning for Sum-Rate Maximization
Mohammed Saif
https://arxiv.org/abs/2606.11519 https://arxiv.org/pdf/2606.11519 https://arxiv.org/html/2606.11519
arXiv:2606.11519v1 Announce Type: new
Abstract: This paper investigates the utility of the movable antenna (MA) and reconfigurable intelligent surface (RIS) framework for downlink wireless communications. In the considered scenario, a base station (BS) is equipped with two sub-arrays of MAs transmits signals to the users via the RIS. By jointly exploiting the antenna-positioning flexibility of MAs and the RIS element selection capability, the proposed joint MA-RIS framework introduces additional design degrees of freedom to enhance desired signals and mitigate inter-user interference, thereby maximizing the network sum-rate. To this end, we formulate a joint optimization problem involving MA positioning, sub-array beamforming, and RIS element selection, subject to the minimum antenna separation and transmit power constraints. The resulting problem is highly non-convex and challenging to solve directly. To address this issue, an alternating optimization framework is developed that decomposes the problem into three tractable subproblems. Specifically, zero-forcing beamforming is employed for transmit beamformer design, a low-complexity one-dimensional search is derived for RIS element selection, and the MA positioning problem is solved using block coordinate descent (BCD) and convex optimization techniques implemented via CVX. Simulation results demonstrate that the proposed joint MA-RIS framework significantly improves the achievable sum-rate compared with conventional fixed MAs and benchmark schemes with random configurations.
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Joint Movable Antenna Positioning and RIS Partitioning for Sum-Rate Maximization
Mohammed Saif
https://arxiv.org/abs/2606.11519 https://arxiv.org/pdf/2606.…