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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
The Carbon Copy onto Dirty Paper Channel with Statistical...
Stefano Rini, Shlomo Shamai Shitz · 2016-02-06 · via cs.IT updates on arXiv.org

Costa's "writing on dirty paper" capacity result establishes that full state pre-cancellation can be attained in Gelfand-Pinsker channel with additive state and additive Gaussian noise. The "carbon copy onto dirty paper" channel is the extension of Costa's model to the compound setting: M receivers each observe the sum of the channel input, Gaussian noise and one of M Gaussian state sequences and attempt to decode the same common message. The state sequences are all non-causally known at the transmitter which attempts to simultaneously pre-code its transmission against the channel state affecting each output. In this correspondence we derive the capacity to within 2.25 bits-per-channel-use of the carbon copying onto dirty paper channel in which the state sequences are statistically equivalent, having the same variance and the same pairwise correlation. For this channel capacity is approached by letting the channel input be the superposition of two codewords: a base codeword, simultaneously decoded at each user, and a top codeword which is pre-coded against the state realization at each user for a portion 1/M of the time. The outer bound relies on a recursive bounding in which incremental side information is provided at each receiver. This result represents a significant first step toward determining the capacity of the most general "carbon copy onto dirty paper" channel in which state sequences appearing in the different channel outputs have any jointly Gaussian distribution.