[go: up one dir, main page]

Hammami et al., 2025 - Google Patents

Statistical multiport-network modeling and efficient discrete optimization of RIS

Hammami et al., 2025

View PDF
Document ID
8641598003527933354
Author
Hammami C
Magoarou L
del Hougne P
Publication year
Publication venue
arXiv preprint arXiv:2508.01776

External Links

Snippet

This Letter fills the research gap on physics-consistent optimization for reconfigurable intelligent surfaces (RISs) with mutual coupling (MC) and 1-bit-tunable elements, a common hardware constraint in existing RIS prototypes. We compare a model-based method …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/002—Quantum computers, i.e. information processing by using quantum superposition, coherence, decoherence, entanglement, nonlocality, teleportation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems

Similar Documents

Publication Publication Date Title
Singh et al. Ising machines’ dynamics and regularization for near-optimal MIMO detection
Cui et al. Jointly sparse signal recovery and support recovery via deep learning with applications in MIMO-based grant-free random access
Hu et al. Joint deep reinforcement learning and unfolding: Beam selection and precoding for mmWave multiuser MIMO with lens arrays
Tabi et al. Evaluation of quantum annealer performance via the massive MIMO problem
Jiang et al. Residual-aided end-to-end learning of communication system without known channel
Zhang et al. Hybrid quantum-classical neural networks for downlink beamforming optimization
Chen et al. Joint client selection and receive beamforming for over-the-air federated learning with energy harvesting
Li et al. Communication-efficient decentralized zeroth-order method on heterogeneous data
Hu et al. DDPG-driven deep-unfolding with adaptive depth for channel estimation with sparse Bayesian learning
Hammami et al. Statistical multiport-network modeling and efficient discrete optimization of RIS
Zhang et al. Deep reinforcement learning-empowered beamforming design for IRS-assisted MISO interference channels
Shi et al. Automatic high-performance neural network construction for channel estimation in IRS-aided communications
Doolittle et al. Maximal qubit violations of n-locality in star and chain networks
Xu et al. QC-ODKLA: Quantized and communication-censored online decentralized kernel learning via linearized ADMM
Khatri Policies for elementary link generation in quantum networks
Zhao et al. QAOA-assisted benders' decomposition for mixed-integer linear programming
KR20240018909A (en) Apparatus and method for classifying quantum states in quantum information integrated processing system
Almagboul et al. Deep-learning-based phase-only robust massive MU-MIMO hybrid beamforming
Zhao et al. Entanglement-induced provable and robust quantum learning advantages
del Hougne Ambiguity-Aware Segmented Estimation of Mutual Coupling in Large RIS: Algorithm and Experimental Validation
CN111738426A (en) A Stability Control Method of Quantum Cryptosystem Based on GRU Model
Sant et al. Insights into maximum likelihood detection for one-bit massive mimo communications
Wang et al. Learning Best Paths in Quantum Networks
Abdi et al. Indirect stochastic gradient quantization and its application in distributed deep learning
Wang et al. LOFT: Finding lottery tickets through filter-wise training