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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
Impact of Beam Misalignment on Hybrid Beamforming NOMA fo...
Mojtaba Ahmadi Almasi, Mojtaba Vaezi, Hani Mehrpouyan · 2018-08-02 · via cs.IT updates on arXiv.org

This paper studies hybrid beamforming (HB)-based non-orthogonal multiple access (NOMA) in multiuser millimeter wave (mmWave) communications. HB offers power-efficient and low-complexity precoding for downlink multiuser mmWave systems which increases multiplexing gain and spectral efficiency of the system. Applying NOMA to HB-based systems, called HB-NOMA, can scale the number of users while offering a high spectral efficiency. However, an imperfect correlation between the effective channels of users in each NOMA cluster seriously degrades the achievable rate of HB-NOMA. In this paper, first a sum-rate maximization problem is formulated for HB-NOMA, and an algorithm is proposed to solve it effectively. It is then shown that the relationship between the effective channels of the users in each NOMA cluster can be approximated by a correlation factor. Next, the effect of imperfect correlation is analyzed, and a lower bound on the achievable rate of the users is derived for both perfect and imperfect correlation. Finally, the rate gap resulting from an imperfect correlation is evaluated and a tight upper bound is derived for that. Simulation results show that low correlation degrades the achievable rate of users. The lower bounds are tight in the large dimensional regime and in single-path channels.