Pub Date : 2022-04-22DOI: 10.1109/spawc51304.2022.9833966
Pham Q. Viet, Daniel Romero
Traditional localization algorithms based on features such as time difference of arrival are impaired by non-line of sight propagation, which negatively affects the consistency that they expect among distance estimates. Instead, fingerprinting localization is robust to these propagation conditions but requires the costly collection of large data sets. To alleviate these limitations, the present paper capitalizes on the recently-proposed notion of channel charting to learn the geometry of the space that contains the channel state information (CSI) measurements collected by the nodes to be localized. The proposed algorithm utilizes a deep neural network that learns distances between pairs of nodes using their measured CSI. Unlike standard channel charting approaches, this algorithm directly works with the physical geometry and therefore only implicitly learns the geometry of the radio domain. Simulation results demonstrate that the proposed algorithm outperforms its competitors and allows accurate localization in emergency scenarios using an unmanned aerial vehicle.
{"title":"Implicit Channel Charting with Application to UAV-aided Localization","authors":"Pham Q. Viet, Daniel Romero","doi":"10.1109/spawc51304.2022.9833966","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833966","url":null,"abstract":"Traditional localization algorithms based on features such as time difference of arrival are impaired by non-line of sight propagation, which negatively affects the consistency that they expect among distance estimates. Instead, fingerprinting localization is robust to these propagation conditions but requires the costly collection of large data sets. To alleviate these limitations, the present paper capitalizes on the recently-proposed notion of channel charting to learn the geometry of the space that contains the channel state information (CSI) measurements collected by the nodes to be localized. The proposed algorithm utilizes a deep neural network that learns distances between pairs of nodes using their measured CSI. Unlike standard channel charting approaches, this algorithm directly works with the physical geometry and therefore only implicitly learns the geometry of the radio domain. Simulation results demonstrate that the proposed algorithm outperforms its competitors and allows accurate localization in emergency scenarios using an unmanned aerial vehicle.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124540224","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}
Pub Date : 2022-04-18DOI: 10.48550/arXiv.2204.08367
Kyriakos Stylianopoulos, G. Alexandropoulos
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.
{"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}
Pub Date : 2022-04-10DOI: 10.48550/arXiv.2204.04795
Omar Hashash, Christina Chaccour, W. Saad
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.
{"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}
Pub Date : 2022-04-05DOI: 10.48550/arXiv.2204.02102
Gilles Callebaut, William Tarneberg, L. Perre, Emma Fitzgerald
Cell-Free networking is one of the prime candidates for 6G networks. Despite being capable of providing the 6G needs, practical limitations and considerations are often neglected in current research. In this work, we introduce the concept of federations to dynamically scale and select the best set of resources, e.g., antennas, computing and data resources, to serve a given application. Next to communication, 6G systems are expected to provide also wireless powering, positioning and sensing, further increasing the complexity of such systems. Therefore, each federation is self-managing and is distributed over the area in a cell-free manner. Next to the dynamic federations, new accompanying terminology is proposed to design cell-free systems taking into account practical limitations such as time synchronization and distributed processing. We conclude with an illustration with four federations, serving distinct applications, and introduce two new testbeds to study these architectures and concepts.
{"title":"Dynamic Federations for 6G Cell-Free Networking: Concepts and Terminology","authors":"Gilles Callebaut, William Tarneberg, L. Perre, Emma Fitzgerald","doi":"10.48550/arXiv.2204.02102","DOIUrl":"https://doi.org/10.48550/arXiv.2204.02102","url":null,"abstract":"Cell-Free networking is one of the prime candidates for 6G networks. Despite being capable of providing the 6G needs, practical limitations and considerations are often neglected in current research. In this work, we introduce the concept of federations to dynamically scale and select the best set of resources, e.g., antennas, computing and data resources, to serve a given application. Next to communication, 6G systems are expected to provide also wireless powering, positioning and sensing, further increasing the complexity of such systems. Therefore, each federation is self-managing and is distributed over the area in a cell-free manner. Next to the dynamic federations, new accompanying terminology is proposed to design cell-free systems taking into account practical limitations such as time synchronization and distributed processing. We conclude with an illustration with four federations, serving distinct applications, and introduce two new testbeds to study these architectures and concepts.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127291277","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}
Pub Date : 2022-04-04DOI: 10.48550/arXiv.2204.13996
Taha Yassine, Luc Le Magoarou, S. Paquelet, M. Crussiére
Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart, preserving as much as possible spatial neighborhoods. In this paper, a model-based deep learning approach to this problem is proposed. It builds on a physically motivated distance measure to structure and initialize a neural network that is subsequently trained using a triplet loss function. The proposed structure exhibits a low number of parameters and clever initialization leads to fast training. These two features make the proposed approach amenable to on-the-fly channel charting. The method is empirically assessed on realistic synthetic channels, yielding encouraging results.
{"title":"Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting","authors":"Taha Yassine, Luc Le Magoarou, S. Paquelet, M. Crussiére","doi":"10.48550/arXiv.2204.13996","DOIUrl":"https://doi.org/10.48550/arXiv.2204.13996","url":null,"abstract":"Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart, preserving as much as possible spatial neighborhoods. In this paper, a model-based deep learning approach to this problem is proposed. It builds on a physically motivated distance measure to structure and initialize a neural network that is subsequently trained using a triplet loss function. The proposed structure exhibits a low number of parameters and clever initialization leads to fast training. These two features make the proposed approach amenable to on-the-fly channel charting. The method is empirically assessed on realistic synthetic channels, yielding encouraging results.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122294462","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}
Pub Date : 2022-03-28DOI: 10.1109/spawc51304.2022.9833977
Elyes Balti, B. Evans
In this work, we consider a full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular network with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converter (DACs). Our first contribution is to provide a unified framework for reverse link analysis where matched filters are applied at the FD base stations (BSs) under channel hardening. Second, we derive the expressions of the signal-to-quantization-plus-interference-plus-noise ratio (SQINR) for general and special cases. Finally, we quantify effects of quantization error, pilot contamination, and full duplexing for a hexagonal cell lattice on spectral efficiency and cumulative distribution function (CDF) to show that FD outperforms half duplex (HD) in a wide variety of scenarios.
{"title":"Reverse Link Analysis for Full-Duplex Cellular Networks with Low Resolution ADC/DAC","authors":"Elyes Balti, B. Evans","doi":"10.1109/spawc51304.2022.9833977","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833977","url":null,"abstract":"In this work, we consider a full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular network with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converter (DACs). Our first contribution is to provide a unified framework for reverse link analysis where matched filters are applied at the FD base stations (BSs) under channel hardening. Second, we derive the expressions of the signal-to-quantization-plus-interference-plus-noise ratio (SQINR) for general and special cases. Finally, we quantify effects of quantization error, pilot contamination, and full duplexing for a hexagonal cell lattice on spectral efficiency and cumulative distribution function (CDF) to show that FD outperforms half duplex (HD) in a wide variety of scenarios.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121876196","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}
Pub Date : 2022-03-25DOI: 10.48550/arXiv.2203.13571
M. Fischer, Sebastian Dörner, Sebastian Cammerer, Takayuki Shimizu, Hongsheng Lu, S. Brink
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.
{"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}
Pub Date : 2022-03-24DOI: 10.48550/arXiv.2203.13663
Matin Mortaheb, Cemil Vahapoglu, S. Ulukus
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.
{"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}
Pub Date : 2022-03-24DOI: 10.1109/spawc51304.2022.9833999
Murat Bayraktar, J. Palacios, N. G. Prelcic, C. Zhang
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.
{"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}
Pub Date : 2022-03-24DOI: 10.48550/arXiv.2203.12957
Ema Becirovic, Zheng Chen, E. Larsson
We provide the optimal receive combining strategy for federated learning in multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform individual gradient sparsification which greatly improves performance in scenarios with heterogeneous (non i.i.d.) training data. The proposed method beats the benchmark by a wide margin.
{"title":"Optimal MIMO Combining for Blind Federated Edge Learning with Gradient Sparsification","authors":"Ema Becirovic, Zheng Chen, E. Larsson","doi":"10.48550/arXiv.2203.12957","DOIUrl":"https://doi.org/10.48550/arXiv.2203.12957","url":null,"abstract":"We provide the optimal receive combining strategy for federated learning in multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform individual gradient sparsification which greatly improves performance in scenarios with heterogeneous (non i.i.d.) training data. The proposed method beats the benchmark by a wide margin.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"30 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":"115474517","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}
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