Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics

• URL: http://arxiv.org/abs/2408.13546v1
• Date: Sat, 24 Aug 2024 10:35:10 GMT
• Title: Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics
• Authors: Zonghui Yang, Shijian Gao, Xiang Cheng, Liuqing Yang,
• Abstract summary: Integrated sensing and communication (ISAC) technology plays a crucial role in vehicular networks. Double dynamics present significant challenges for real-time ISAC precoding design. We propose a synesthesia of machine (SoM)-enhanced precoding paradigm.
• Score: 15.847713094328286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrated sensing and communication (ISAC) technology plays a crucial role in vehicular networks. However, the communication channel within this context exhibits time-varying characteristics, and potential targets may move rapidly, resulting in double dynamics. These presents significant challenges for real-time ISAC precoding design that have not been thoroughly explored. While optimization-based precoding methods have been extensively studied, they are computationally complex and heavily rely on perfect prior information that is rarely available in situations with double dynamics. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm, where the base station leverages various modalities such as positioning and channel information to adapt to double dynamics, and effectively utilizes environmental information to stretch ISAC performance boundaries through a deep reinforcement learning framework. Additionally, a parameter-shared actor-critic architecture is tailored to expedite training in complex state and action spaces. Extensive experimental validation has demonstrated the multifaceted superiority of our method over existing approaches.

Related papers

• Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction [25.688521281119037]
  Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is challenging and crucial for optimizing downstream tasks. Traditional prediction approaches focus on four-dimensional (4D) CSI, which includes predictions over time, bandwidth, and antenna (TX and RX) space. We propose a novel context-conditionedtemporal predictive learning method to capture dependencies within 4D CSI data.
  arXiv  Detail & Related papers  (2024-09-16T04:15:36Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Doubly-Dynamic ISAC Precoding for Vehicular Networks: A Constrained Deep Reinforcement Learning (CDRL) Approach [11.770137653756697]
  Integrated sensing and communication (ISAC) technology is essential for supporting vehicular networks. The communication channel in this scenario exhibits time variations, and the potential targets may move rapidly, resulting in double dynamics. We propose using constrained deep reinforcement learning to facilitate dynamic updates to the ISAC precoder.
  arXiv  Detail & Related papers  (2024-05-23T09:19:14Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Efficient Encoder-Decoder and Dual-Path Conformer for Comprehensive Feature Learning in Speech Enhancement [0.2538209532048866]
  This paper proposes a time-frequency (T-F) domain speech enhancement network (DPCFCS-Net) It incorporates improved densely connected blocks, dual-path modules, convolution-augmented transformers (conformers), channel attention, and spatial attention. Compared with previous models, our proposed model has a more efficient encoder-decoder and can learn comprehensive features.
  arXiv  Detail & Related papers  (2023-06-09T12:52:01Z)
• Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identification [91.56939957189505]
  We propose a novel spatial-temporal complementary learning framework named Deeply-Coupled Convolution-Transformer (DCCT) for high-performance video-based person Re-ID. Our framework could attain better performances than most state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-27T12:16:44Z)
• Topics in Deep Learning and Optimization Algorithms for IoT Applications in Smart Transportation [0.0]
  This thesis investigates how different optimization algorithms and machine learning techniques can be leveraged to improve system performance. In the first topic, we propose an optimal transmission frequency management scheme using decentralized ADMM-based method. In the second topic, we leverage graph neural network (GNN) for demand prediction for shared bikes. In the last topic, we consider a highway traffic network scenario where frequent lane changing behaviors may occur with probability.
  arXiv  Detail & Related papers  (2022-10-13T11:45:30Z)
• Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization [74.34699679568818]
  Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. We propose a cross-modal consensus network (CO2-Net) to tackle this problem.
  arXiv  Detail & Related papers  (2021-07-27T04:21:01Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)

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.