Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network

• URL: http://arxiv.org/abs/2510.02108v1
• Date: Thu, 02 Oct 2025 15:15:50 GMT
• Title: Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network
• Authors: Jinshuo Zhang, Yafei Wang, Xinping Yi, Wenjin Wang, Shi Jin, Symeon Chatzinotas, Björn Ottersten,
• Abstract summary: We propose an end-to-end deep learning (DL) framework with low inference complexity for symbol-level precoding.<n>We show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods.
• Score: 84.22115118596741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although symbol-level precoding (SLP) based on constructive interference (CI) exploitation offers performance gains, its high complexity remains a bottleneck. This paper addresses this challenge with an end-to-end deep learning (DL) framework with low inference complexity that leverages the structure of the optimal SLP solution in the closed-form and its inherent tensor equivariance (TE), where TE denotes that a permutation of the input induces the corresponding permutation of the output. Building upon the computationally efficient model-based formulations, as well as their known closed-form solutions, we analyze their relationship with linear precoding (LP) and investigate the corresponding optimality condition. We then construct a mapping from the problem formulation to the solution and prove its TE, based on which the designed networks reveal a specific parameter-sharing pattern that delivers low computational complexity and strong generalization. Leveraging these, we propose the backbone of the framework with an attention-based TE module, achieving linear computational complexity. Furthermore, we demonstrate that such a framework is also applicable to imperfect CSI scenarios, where we design a TE-based network to map the CSI, statistics, and symbols to auxiliary variables. Simulation results show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods and maintaining strong generalization across user numbers and symbol block lengths.

Related papers

• Structure-Aware Robust Counterfactual Explanations via Conditional Gaussian Network Classifiers [0.26999000177990923]
  This work presents a structure-aware robustness-and-counterfactual search method based on conditional conditional graphs.<n>Results show that our method achieves strong consistency, with direct optimization of the original formulation providing especially stable dependencies.<n>The proposed framework lays the groundwork for future advances in counterfactual reasoning under noncyclic constraints.
  arXiv  Detail & Related papers  (2026-02-08T15:51:45Z)
• Do It for HER: First-Order Temporal Logic Reward Specification in Reinforcement Learning (Extended Version) [49.462399222747024]
  We propose a novel framework for the logical specification of non-Markovian rewards in Decision Processes (MDPs) with large state spaces.<n>Our approach leverages Linear Temporal Logic Modulo Theories over finite traces (LTLfMT)<n>We introduce a method based on reward machines and Hindsight Experience Replay (HER) to translate first-order logic specifications and address reward sparsity.
  arXiv  Detail & Related papers  (2026-02-05T22:11:28Z)
• STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization [18.162186876640764]
  Combinatorial optimization (CO) problems, central to operation research and theoretical computer science, present significant computational challenges due to their NP-hard nature.<n>We propose STRCMP, a novel structure-aware algorithm discovery framework that systematically integrates structure priors to enhance solution quality and solving efficiency.<n>Our framework combines a graph neural network (GNN) for extracting structural embeddings from CO instances with an LLM conditioned on these embeddings to identify high-performing algorithms in the form of solver-specific codes.
  arXiv  Detail & Related papers  (2025-05-22T15:37:42Z)
• Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study [0.0]
  We propose a novel patternaware framework to reduce effective computational complexity across domains.<n>With rigorous definitions, theorems, and a meta learning driven solver pipeline, we introduce metrics like Pattern Utilization Efficiency (PUE) and achieve up to 79 percent solution quality gains.
  arXiv  Detail & Related papers  (2025-03-12T11:05:06Z)
• Combinatorial Optimization via LLM-driven Iterated Fine-tuning [47.66752049943335]
  We present a novel way to integrate flexible, context-dependent constraints into optimization by leveraging Large Language Models (LLMs)<n>Our framework balances locally constraints with rigorous global optimization more effectively than baseline sampling methods.
  arXiv  Detail & Related papers  (2025-03-10T04:58:18Z)
• Pointer Networks with Q-Learning for Combinatorial Optimization [55.2480439325792]
  We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) Our empirical results demonstrate the efficacy of this approach, also testing the model in unstable environments.
  arXiv  Detail & Related papers  (2023-11-05T12:03:58Z)
• Semantic Probabilistic Layers for Neuro-Symbolic Learning [83.25785999205932]
  We design a predictive layer for structured-output prediction (SOP) It can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints. Our Semantic Probabilistic Layer (SPL) can model intricate correlations, and hard constraints, over a structured output space.
  arXiv  Detail & Related papers  (2022-06-01T12:02:38Z)
• Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging [71.57324258813675]
  A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies. A neural network is employed to act as structure prior and reveal the underlying signal interdependencies. Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
  arXiv  Detail & Related papers  (2022-03-29T21:00:39Z)
• Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders [1.0124625066746595]
  A robust topology optimization (RTO) problem identifies a design with the best average performance. A neural network method is proposed that offers computational efficiency. Numerical application of the method is shown on the robust design of L-bracket structure with single point load as well as multiple point loads.
  arXiv  Detail & Related papers  (2021-07-19T20:40:51Z)
• Constrained Combinatorial Optimization with Reinforcement Learning [0.30938904602244344]
  This paper presents a framework to tackle constrained optimization problems using deep Reinforcement Learning (RL) We extend the Neural Combinatorial Optimization (NCO) theory in order to deal with constraints in its formulation. In that context, the solution is iteratively constructed based on interactions with the environment.
  arXiv  Detail & Related papers  (2020-06-22T03:13:07Z)
• Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems [59.804810122136345]
  We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
  arXiv  Detail & Related papers  (2020-06-15T02:57:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.