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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Elements Allocation for Joint Active and Passive IRS Aide...
Chaoying Huang, Wen Chen, Qingqing Wu, Nan Cheng · 2024-04-10 · via cs.IT updates on arXiv.org

Unlike previous works that focused solely on passive intelligent reflecting surface (PIRS) or active IRS (AIRS), a novel joint AIRS and PIRS architecture has been developed to flexibly utilize their combined advantages in mitigating multiplicative path loss cost-effectively. In this paper, we consider the AIRS-PIRS jointly aided wireless point-to-point communication system with two different deployment schemes in three-dimensional (3D) space. To balance the trade-off between the square-order beamforming gain of PIRS and the unique power amplification gain of AIRS, we optimize the elements allocation and beamforming design of the two IRSs under various practical constraints from a rate-maximization perspective. Moreover, we derive a series of element-related closed-form analytical expressions and compare the performance of the two schemes. Our analysis shows that in both schemes, PIRS should be allocated more elements than AIRS, and the received signal-to-noise ratio (SNR) increases asymptotically with the cube of the number of reflecting elements, when the distance between AIRS and PIRS is sufficiently large. Last, simulation results validate our analysis and indicate that both schemes can achieve superior rate performance over various benchmarks.