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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 Beamformed Control Channel Design for LTE with Fu...
Pavan Reddy M., Harish Kumar D., Saidhiraj Amuru, Kiran Kuchi · 2019-05-14 · via cs.IT updates on arXiv.org

The Full Dimension-MIMO (FD-MIMO) technology is capable of achieving huge improvements in network throughput with simultaneous connectivity of a large number of mobile wireless devices, unmanned aerial vehicles, and the Internet of Things (IoT). In FD-MIMO, with a large number of antennae at the base station and the ability to perform beamforming, the capacity of the physical downlink shared channel (PDSCH) has increased a lot. However, the current specifications of the 3rd Generation Partnership Project (3GPP) does not allow the base station to perform beamforming techniques for the physical downlink control channel (PDCCH), and hence, PDCCH has neither the capacity nor the coverage of PDSCH. Therefore, PDCCH capacity will still limit the performance of a network as it dictates the number of users that can be scheduled at a given time instant. In Release 11, 3GPP introduced enhanced PDCCH (EPDCCH) to increase the PDCCH capacity at the cost of sacrificing the PDSCH resources. The problem of enhancing the PDCCH capacity within the available control channel resources has not been addressed yet in the literature. Hence, in this paper, we propose a novel beamformed PDCCH (BF-PDCCH) design which is aligned to the 3GPP specifications and requires simple software changes at the base station. We rely on the sounding reference signals transmitted in the uplink to decide the best beam for a user and ingeniously schedule the users in PDCCH. We perform system level simulations to evaluate the performance of the proposed design and show that the proposed BF-PDCCH achieves larger network throughput when compared with the current state of art algorithms, PDCCH and EPDCCH schemes.