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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
On Downlink Interference Decoding In Multi-Cell Massive M...
Meysam Shahrbaf Motlagh, Subhajit Majhi, Patrick Mitran, Hideki · 2021-10-14 · via cs.IT updates on arXiv.org

In this paper, the downlink of a multi-cell massive MIMO system is considered where the channel state information (CSI) is estimated via pilot symbols that are orthogonal in a cell but re-used in other cells. Re-using the pilots, however, contaminates the CSI estimate at each base station (BS) by the channel of the users sharing the same pilot in other cells. The resulting inter-cell interference does not vanish even when the number of BS antennas $M$ is large, i.e., $M\rightarrow\infty$, and thus the rates achieved by treating interference as noise (TIN) saturate even if $M\rightarrow\infty$. In this paper, interference aware decoding schemes based on simultaneous unique decoding (SD) and simultaneous non-unique decoding (SND) of the full interference or a part of the interference (PD) are studied with two different linear precoding techniques: maximum ratio transmission (MRT) and zero forcing (ZF). The resulting rates are shown to grow unbounded as $M\rightarrow\infty$. In addition, the rates achievable via SD/SND/PD for finite $M$ are derived using a worst-case uncorrelated noise technique, which are shown to scale as $\mathcal{O}(\log M)$. To compare the performance of different schemes, the maximum symmetric rate problem is studied, where it is confirmed that with large, yet practical, values of $M$, SND strictly outperforms TIN, and also that PD strictly outperforms SND.