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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
Breaking Through the Full-Duplex Wi-Fi Capacity Gain
Saulo Queiroz, João Vilela, Roberto Hexsel · 2016-05-31 · via cs.IT updates on arXiv.org

In this work we identify a seminal design guideline that prevents current Full-Duplex (FD) MAC protocols to scale the FD capacity gain (i.e. 2x the half-duplex throughput) in single-cell Wi-Fi networks. Under such guideline (referred to as 1:1), a MAC protocol attempts to initiate up to two simultaneous transmissions in the FD bandwidth. Since in single-cell Wi-Fi networks MAC performance is bounded by the PHY layer capacity, this implies gains strictly less than 2x over half-duplex at the MAC layer. To face this limitation, we argue for the 1:N design guideline. Under 1:N, FD MAC protocols 'see' the FD bandwidth through N>1 orthogonal narrow-channel PHY layers. Based on theoretical results and software defined radio experiments, we show the 1:N design can leverage the Wi-Fi capacity gain more than 2x at and below the MAC layer. This translates the denser modulation scheme incurred by channel narrowing and the increase in the spatial reuse offer enabled by channel orthogonality. With these results, we believe our design guideline can inspire a new generation of Wi-Fi MAC protocols that fully embody and scale the FD capacity gain.