2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)最新文献_第10页

Implicit Channel Charting with Application to UAV-aided Localization 隐式信道制图及其在无人机辅助定位中的应用

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-04-22 DOI: 10.1109/spawc51304.2022.9833966

Pham Q. Viet, Daniel Romero

引用次数: 1

Online RIS Configuration Learning for Arbitrary Large Numbers of 1-Bit Phase Resolution Elements 任意大量1位相位分辨率元素的在线RIS配置学习

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-04-18 DOI: 10.48550/arXiv.2204.08367

Kyriakos Stylianopoulos, G. Alexandropoulos

{"title":"Online RIS Configuration Learning for Arbitrary Large Numbers of 1-Bit Phase Resolution Elements","authors":"Kyriakos Stylianopoulos, G. Alexandropoulos","doi":"10.48550/arXiv.2204.08367","DOIUrl":"https://doi.org/10.48550/arXiv.2204.08367","url":null,"abstract":"Reinforcement Learning (RL) approaches are lately deployed for orchestrating wireless communications empowered by Reconfigurable Intelligent Surfaces (RISs), leveraging their online optimization capabilities. Most commonly, in RL-based formulations for realistic RISs with low resolution phase-tunable elements, each configuration is modeled as a distinct reflection action, resulting to inefficient exploration due to the exponential nature of the search space. In this paper, we consider RISs with 1-bit phase-resolution elements and model the reflection action as a binary vector including the feasible reflection coefficients. We then introduce two variations of the well-established Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) agents, aiming for effective exploration of the binary action spaces. For the case of DQN, we make use of an efficient approximation of the Q-function, whereas a discretization post-processing step is applied to the output of DDPG. Our simulations consider large-scale RISs, where existing tuning methods are largely impractical, and showcase that the proposed techniques greatly outperform the baseline in terms of the rate maximization objective. In addition, when dealing with moderate-scale RIS sizes, where the conventional DQN relying on configuration-based action spaces is feasible, the performance of the latter technique is similar to the proposed learning approach.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123265311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Edge Continual Learning for Dynamic Digital Twins over Wireless Networks 无线网络上动态数字孪生的边缘持续学习

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-04-10 DOI: 10.48550/arXiv.2204.04795

Omar Hashash, Christina Chaccour, W. Saad

{"title":"Edge Continual Learning for Dynamic Digital Twins over Wireless Networks","authors":"Omar Hashash, Christina Chaccour, W. Saad","doi":"10.48550/arXiv.2204.04795","DOIUrl":"https://doi.org/10.48550/arXiv.2204.04795","url":null,"abstract":"Digital twins (DTs) constitute a critical link between the real-world and the metaverse. To guarantee a robust connection between these two worlds, DTs should maintain accurate representations of the physical applications, while preserving synchronization between real and digital entities. In this paper, a novel edge continual learning framework is proposed to accurately model the evolving affinity between a physical twin (PT) and its corresponding cyber twin (CT) while maintaining their utmost synchronization. In particular, a CT is simulated as a deep neural network (DNN) at the wireless network edge to model an autonomous vehicle traversing an episodically dynamic environment As the vehicular PT updates its driving policy in each episode, the CT is required to concurrently adapt its DNN model to the PT, which gives rise to a de-synchronization gap. Considering the history-aware nature of DTs, the model update process is posed a dual objective optimization problem whose goal is to jointly minimize the loss function over all encountered episodes and the corresponding de-synchronization time. As the de-synchronization time continues to increase over sequential episodes, an elastic weight consolidation (EWC) technique that regularizes the DT history is proposed to limit de-synchronization time. Furthermore, to address the plasticity-stability tradeoff accompanying the progressive growth of the EWC regularization terms, a modified EWC method that considers fair execution between the historical episodes of the DTs is adopted. Ultimately, the proposed framework achieves a simultaneously accurate and synchronous CT model that is robust to catastrophic forgetting. Simulation results show that the proposed solution can achieve an accuracy of 90% while guaranteeing a minimal desynchronization time.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129111114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Dynamic Federations for 6G Cell-Free Networking: Concepts and Terminology 6G无蜂窝网络的动态联合:概念和术语

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-04-05 DOI: 10.48550/arXiv.2204.02102

Gilles Callebaut, William Tarneberg, L. Perre, Emma Fitzgerald

引用次数: 1

Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting 利用三重损失和非线性降维的动态通道图表

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-04-04 DOI: 10.48550/arXiv.2204.13996

Taha Yassine, Luc Le Magoarou, S. Paquelet, M. Crussiére

引用次数: 9

Reverse Link Analysis for Full-Duplex Cellular Networks with Low Resolution ADC/DAC 低分辨率ADC/DAC全双工蜂窝网络的反向链路分析

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-03-28 DOI: 10.1109/spawc51304.2022.9833977

Elyes Balti, B. Evans

引用次数: 1

Adaptive Neural Network-based OFDM Receivers 基于自适应神经网络的OFDM接收机

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-03-25 DOI: 10.48550/arXiv.2203.13571

M. Fischer, Sebastian Dörner, Sebastian Cammerer, Takayuki Shimizu, Hongsheng Lu, S. Brink

{"title":"Adaptive Neural Network-based OFDM Receivers","authors":"M. Fischer, Sebastian Dörner, Sebastian Cammerer, Takayuki Shimizu, Hongsheng Lu, S. Brink","doi":"10.48550/arXiv.2203.13571","DOIUrl":"https://doi.org/10.48550/arXiv.2203.13571","url":null,"abstract":"We propose and examine the idea of continuously adapting state-of-the-art neural network (NN)-based orthogonal frequency division multiplex (OFDM) receivers to current channel conditions. This online adaptation via retraining is mainly motivated by two reasons: First, receiver design typically focuses on the universal optimal performance for a wide range of possible channel realizations. However, in actual applications and within short time intervals, only a subset of these channel parameters is likely to occur, as macro parameters, e.g., the maximum channel delay, can assumed to be static. Second, in-the-field alterations like temporal interferences or other conditions out of the originally intended specifications can occur on a practical (real-world) transmission. While conventional (filter-based) systems would require reconfiguration or additional signal processing to cope with these unforeseen conditions, NN-based receivers can learn to mitigate previously unseen effects even after their deployment. For this, we showcase on-the-fly adaption to current channel conditions and temporal alterations solely based on recovered labels from an outer forward error correction (FEC) code without any additional piloting overhead. To underline the flexibility of the proposed adaptive training, we showcase substantial gains for scenarios with static channel macro parameters, for out-of-specification usage and for interference compensation.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115435995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning FedGradNorm:个性化联邦梯度标准化多任务学习

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-03-24 DOI: 10.48550/arXiv.2203.13663

Matin Mortaheb, Cemil Vahapoglu, S. Ulukus

{"title":"FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning","authors":"Matin Mortaheb, Cemil Vahapoglu, S. Ulukus","doi":"10.48550/arXiv.2203.13663","DOIUrl":"https://doi.org/10.48550/arXiv.2203.13663","url":null,"abstract":"Multi-task learning (MTL) is a novel framework to learn several tasks simultaneously with a single shared network where each task has its distinct personalized header network for fine-tuning. MTL can be implemented in federated learning settings as well, in which tasks are distributed across clients. In federated settings, the statistical heterogeneity due to different task complexities and data heterogeneity due to non-iid nature of local datasets can both degrade the learning performance of the system. In addition, tasks can negatively affect each other’s learning performance due to negative transference effects. To cope with these challenges, we propose FedGradNorm which uses a dynamic-weighting method to normalize gradient norms in order to balance learning speeds among different tasks. FedGradNorm improves the overall learning performance in a personalized federated learning setting. We provide convergence analysis for FedGradNorm by showing that it has an exponential convergence rate. We also conduct experiments on multi-task facial landmark (MTFL) and wireless communication system dataset (RadComDynamic). The experimental results show that our framework can achieve faster training performance compared to equal-weighting strategy. In addition to improving training speed, FedGradNorm also compensates for the imbalanced datasets among clients.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130244216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint Localization and Channel Estimation at mmWave 基于多维正交匹配追踪的ris辅助毫米波联合定位与信道估计

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-03-24 DOI: 10.1109/spawc51304.2022.9833999

Murat Bayraktar, J. Palacios, N. G. Prelcic, C. Zhang

{"title":"Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint Localization and Channel Estimation at mmWave","authors":"Murat Bayraktar, J. Palacios, N. G. Prelcic, C. Zhang","doi":"10.1109/spawc51304.2022.9833999","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833999","url":null,"abstract":"RIS-aided millimeter wave wireless systems benefit from robustness to blockage and enhanced coverage. In this paper, we study the ability of RIS to also provide enhanced localization capabilities as a by-product of communication. We consider sparse reconstruction algorithms to obtain high resolution channel estimates that are mapped to position information. In RIS-aided mmWave systems, the complexity of sparse recovery becomes a bottleneck, given the large number of elements of the RIS and the large communication arrays. We propose to exploit a multidimensional orthogonal matching pursuit strategy for compressive channel estimation in a RIS-aided millimeter wave system. We show how this algorithm, based on computing the projections on a set of independent dictionaries instead of a single large dictionary, enables high accuracy channel estimation at reduced complexity. We also combine this strategy with a localization approach which does not rely on the absolute time of arrival of the LoS path. Localization results in a realistic 3D indoor scenario show that RIS-aided wireless system can also benefit from a significant improvement in localization accuracy.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127071668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Optimal MIMO Combining for Blind Federated Edge Learning with Gradient Sparsification 基于梯度稀疏的盲联合边缘学习最优MIMO组合

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) Pub Date : 2022-03-24 DOI: 10.48550/arXiv.2203.12957

Ema Becirovic, Zheng Chen, E. Larsson

引用次数: 8