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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
A Novel Cooperative Strategy for Wireless Multihop Backha...
Song-Nam Hong, Ivana Maric, Dennis Hui · 2015-08-01 · via cs.IT updates on arXiv.org

The 5G wireless network architecture will bring dense deployments of base stations called {\em small cells} for both outdoors and indoors traffic. The feasibility of their dense deployments depends on the existence of a high data-rate transport network that can provide high-data backhaul from an aggregation node where data traffic originates and terminates, to every such small cell. Due to the limited range of radio signals in the high frequency bands, multihop wireless connection may need to be established between each access node and an aggregation node. In this paper, we present a novel transmission scheme for wireless multihop backhaul for 5G networks. The scheme consists of 1) {\em group successive relaying} that established a relay schedule to efficiently exploit half-duplex relays and 2) an optimized quantize-map-and-forward (QMF) coding scheme that improves the performance of QMF and reduces the decoding complexity and the delay. We derive an achievable rate region of the proposed scheme and attain a closed-form expression in the asymptotic case for several network models of interests. It is shown that the proposed scheme provides a significant gain over multihop routing (based on decode-and-forward), which is a solution currently proposed for wireless multihop backhaul network. Furthermore, the performance gap increases as a network becomes denser. For the proposed scheme, we then develop energy-efficient routing that determines {\em groups} of participating relays for every hop. To reflect the metric used in the routing algorithm, we refer to it as {\em interference-harnessing} routing. By turning interference into a useful signal, each relay requires a lower transmission power to achieve a desired performance compared to other routing schemes. Finally, we present a low-complexity successive decoder, which makes it feasible to use the proposed scheme in practice.