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cs.IT updates on arXiv.org

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
On the Beamformed Broadcast Signaling for Millimeter Wave...
Yilin Li, Jian Luo, Mario Castaneda, Richard Stirling-Gallacher, · 2017-09-25 · via cs.IT updates on arXiv.org

Availability of abundant spectrum has enabled millimeter wave (mm-wave) as a prominent candidate solution for the next generation cellular networks. Highly directional transmissions are essential for exploitation of mm-wave bands to compensate high propagation loss and attenuation. The directional transmission, nevertheless, necessitates a specific design for mm-wave initial cell discovery, as conventional omni-directional broadcast signaling may fail in delivering the cell discovery information. To address this issue, this paper provides an analytical framework for mm-wave beamformed cell discovery based on an information theoretical approach. Design options are compared considering four fundamental and representative broadcast signaling schemes to evaluate discovery latency and signaling overhead. The schemes are then simulated under realistic system parameters. Analytical and simulation results reveals four key findings: (i) For cell discovery without knowledge of beacon timing, analog/hybrid beamforming performs as well as digital beamforming in terms of cell discovery latency; (ii) Single beam exhaustive scan optimize the latency, however leads to overhead penalty; (iii) Multi-beam simultaneous scan can significantly reduce the overhead, and provide the flexibility to achieve trade-off between the latency and the overhead; (iv) The latency and the overhead are relatively insensitive to extreme low block error rates.