Pub Date : 2022-07-04DOI: 10.1109/spawc51304.2022.9834004
Lucas Ribeiro, Markus Leinonen, Isuru Rathnayaka, H. Al-Tous, M. Juntti
Serving a plethora of devices in massive machinetype communications (mMTC) can rely on spatial multiplexing enabled by massive multiple-input multiple-output (mMIMO) technology. To release the full potential, accurate channel estimation is needed. Due to the large numbers of devices it necessitates pilot reuse. We propose a pilot allocation algorithm based on multi-point channel charting (CC) to mitigate inevitable pilot contamination in a multi-cell multi-sector mMTC network with spatially correlated mMIMO channels. The generated CC represents an effective interference map from channel covariance matrices to capture the degree of pilot contamination caused by sharing the same pilot sequence among multiple users. The map is then fed into a greedy algorithm that aims at optimizing the reuse pattern of orthogonal pilot sequences to minimize the performance degradation caused by pilot contamination. The proposed CC-based method is empirically shown to obtain notable gains over a reuse-factor-aware random pilot allocation, yet leaving room for further improvements.
{"title":"Channel Charting Aided Pilot Allocation in Multi-Cell Massive MIMO mMTC Networks","authors":"Lucas Ribeiro, Markus Leinonen, Isuru Rathnayaka, H. Al-Tous, M. Juntti","doi":"10.1109/spawc51304.2022.9834004","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9834004","url":null,"abstract":"Serving a plethora of devices in massive machinetype communications (mMTC) can rely on spatial multiplexing enabled by massive multiple-input multiple-output (mMIMO) technology. To release the full potential, accurate channel estimation is needed. Due to the large numbers of devices it necessitates pilot reuse. We propose a pilot allocation algorithm based on multi-point channel charting (CC) to mitigate inevitable pilot contamination in a multi-cell multi-sector mMTC network with spatially correlated mMIMO channels. The generated CC represents an effective interference map from channel covariance matrices to capture the degree of pilot contamination caused by sharing the same pilot sequence among multiple users. The map is then fed into a greedy algorithm that aims at optimizing the reuse pattern of orthogonal pilot sequences to minimize the performance degradation caused by pilot contamination. The proposed CC-based method is empirically shown to obtain notable gains over a reuse-factor-aware random pilot allocation, yet leaving room for further improvements.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"75 2-3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123564340","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9834028
Hui Zhao, Antonio Bazco-Nogueras, P. Elia
The use of vector coded caching has been shown to provide important gains and, more importantly, to alleviate the impact of the file-size constraint, which prevents coded caching from obtaining its ideal gains in practical settings. In this work, we analyze the performance of vector coded caching in the massive MIMO regime, aiming at understanding the benefits that allowing users to cache a practical amount of data could bring to realistic settings in such massive MIMO regime. In particular, we separately consider two linear precoding schemes and analyze the corresponding throughput, for which we derive simple but precise upper and lower bounds. These bounds enable us to characterize the delivery speed-up gain over the uncoded caching setting when the CSI acquisition costs are taken into account. Numerical results demonstrate the tightness of the derived bounds and show a significant boost over uncoded caching and the standard cacheless setting.
{"title":"Vector Coded Caching Greatly Enhances Massive MIMO","authors":"Hui Zhao, Antonio Bazco-Nogueras, P. Elia","doi":"10.1109/spawc51304.2022.9834028","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9834028","url":null,"abstract":"The use of vector coded caching has been shown to provide important gains and, more importantly, to alleviate the impact of the file-size constraint, which prevents coded caching from obtaining its ideal gains in practical settings. In this work, we analyze the performance of vector coded caching in the massive MIMO regime, aiming at understanding the benefits that allowing users to cache a practical amount of data could bring to realistic settings in such massive MIMO regime. In particular, we separately consider two linear precoding schemes and analyze the corresponding throughput, for which we derive simple but precise upper and lower bounds. These bounds enable us to characterize the delivery speed-up gain over the uncoded caching setting when the CSI acquisition costs are taken into account. Numerical results demonstrate the tightness of the derived bounds and show a significant boost over uncoded caching and the standard cacheless setting.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128730364","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833967
Kai Wan, Minquan Cheng, G. Caire
This paper studies the cache-aided multiple-input single-output (MISO) broadcast problem with one-shot linear delivery, where a server with L antennas and N files is connected to K single-antenna users (each with a memory of M files) through a wireless broadcast channel, with the objective to maximize the sum Degree-of-Freedom (sum-DoF) in the whole system. Recently, a construction structure, referred to as Multiple-antenna Placement Delivery Array (MAPDA), was proposed to construct coded caching schemes for this cacheaided MISO broadcast problem based on the joint design of coded caching and zero-forcing (ZF) precoding. In this paper, we first propose an upper bound on the sum-DoF of any MAPDA scheme given a fixed cache placement. Then, under a class of cyclic placements which leads to subpacketizations on the files linear with K, we propose two MAPDAs for the case L < KM/N achieving the sum-DoF 2L, which is order optimal within a factor of 2 when M/N ≤ 1/2 compared to the upper bound under the cyclic placement.
{"title":"Multiple-antenna Placement Delivery Array with Cyclic Placement","authors":"Kai Wan, Minquan Cheng, G. Caire","doi":"10.1109/spawc51304.2022.9833967","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833967","url":null,"abstract":"This paper studies the cache-aided multiple-input single-output (MISO) broadcast problem with one-shot linear delivery, where a server with L antennas and N files is connected to K single-antenna users (each with a memory of M files) through a wireless broadcast channel, with the objective to maximize the sum Degree-of-Freedom (sum-DoF) in the whole system. Recently, a construction structure, referred to as Multiple-antenna Placement Delivery Array (MAPDA), was proposed to construct coded caching schemes for this cacheaided MISO broadcast problem based on the joint design of coded caching and zero-forcing (ZF) precoding. In this paper, we first propose an upper bound on the sum-DoF of any MAPDA scheme given a fixed cache placement. Then, under a class of cyclic placements which leads to subpacketizations on the files linear with K, we propose two MAPDAs for the case L < KM/N achieving the sum-DoF 2L, which is order optimal within a factor of 2 when M/N ≤ 1/2 compared to the upper bound under the cyclic placement.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129033243","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833957
Xiao Meng, F. Liu, W. Yuan, Qixun Zhang
In this paper, we propose a sensing-assisted beam-forming design for integrated sensing and communication (ISAC) system in a vehicle-to-infrastructure (V2I) network, where a road side unit (RSU) provides localization and communication services to the vehicles on an arbitrarily shaped road. In our proposed scheme, the position and motion of the vehicles are decomposed into longitudinal and lateral directions to simplify the kinematic functions. We establish a curvilinear coordinate system based on the road geometry and employ an extended Kalman filter (EKF) to accurately estimate and predict the state of the vehicles. By employing such prediction, we construct a beamformer directing to the vehicles to acquire high array gain and corresponding high quality of service. Numerical results validate the feasibility of tracking and predicting the state of the vehicles by applying a curvilinear coordinate system. The superiority of the proposed algorithm in both communication and tracking metrics is also verified.
{"title":"Sensing Assisted Predictive Beamforming for V2I Networks: Tracking on the Complicated Road : (Invited Paper)","authors":"Xiao Meng, F. Liu, W. Yuan, Qixun Zhang","doi":"10.1109/spawc51304.2022.9833957","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833957","url":null,"abstract":"In this paper, we propose a sensing-assisted beam-forming design for integrated sensing and communication (ISAC) system in a vehicle-to-infrastructure (V2I) network, where a road side unit (RSU) provides localization and communication services to the vehicles on an arbitrarily shaped road. In our proposed scheme, the position and motion of the vehicles are decomposed into longitudinal and lateral directions to simplify the kinematic functions. We establish a curvilinear coordinate system based on the road geometry and employ an extended Kalman filter (EKF) to accurately estimate and predict the state of the vehicles. By employing such prediction, we construct a beamformer directing to the vehicles to acquire high array gain and corresponding high quality of service. Numerical results validate the feasibility of tracking and predicting the state of the vehicles by applying a curvilinear coordinate system. The superiority of the proposed algorithm in both communication and tracking metrics is also verified.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122701013","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833978
Jipeng Gan, Jun Wu, Pei Li, Zehao Chen, Zehao Chen, Jia Zhang, Jian-Duo He
Cooperative spectrum sensing (CSS) is crucial for cognitive radio (CR) to improve spectrum sensing performance. However, the cooperative paradigm is threatened by Byzantine attacks. To ensure the security and energy efficiency (EE) of CSS, in this paper, we propose a malicious exploitation algorithm. Firstly, we distinguish normal users (NUs) from malicious users (MUs) based on the historical performance of secondary users (SUs). Unlike most previous studies, we innovatively improve CSS detection performance by exploiting sensing information from MUs. In addition, we select specific SUs instead of all SUs in data fusion, which reduces the number of samples submitted by SUs to the fusion center (FC). Finally, we further introduce a sequential differential mechanism that substantially reduces samples to improve the EE of CSS. Finally, the numerical simulation results validate the effectiveness of our proposed algorithm.
{"title":"Malicious Exploitation of Byzantine Attack for Cooperative Spectrum Sensing","authors":"Jipeng Gan, Jun Wu, Pei Li, Zehao Chen, Zehao Chen, Jia Zhang, Jian-Duo He","doi":"10.1109/spawc51304.2022.9833978","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833978","url":null,"abstract":"Cooperative spectrum sensing (CSS) is crucial for cognitive radio (CR) to improve spectrum sensing performance. However, the cooperative paradigm is threatened by Byzantine attacks. To ensure the security and energy efficiency (EE) of CSS, in this paper, we propose a malicious exploitation algorithm. Firstly, we distinguish normal users (NUs) from malicious users (MUs) based on the historical performance of secondary users (SUs). Unlike most previous studies, we innovatively improve CSS detection performance by exploiting sensing information from MUs. In addition, we select specific SUs instead of all SUs in data fusion, which reduces the number of samples submitted by SUs to the fusion center (FC). Finally, we further introduce a sequential differential mechanism that substantially reduces samples to improve the EE of CSS. Finally, the numerical simulation results validate the effectiveness of our proposed algorithm.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123017658","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833965
Yuyang Wang, Shiva R. Iyer, D. Chizhik, Jinfeng Du, R. Valenzuela
Channel modeling is critical for coverage prediction, system level simulations, and wireless propagation characterization. Industry practice applies linear fit to the pathloss in decibels against the logarithm of the distance. Simple linear fit, however, cannot fully capture the shadowing effects in the channel, especially for a link with rich scatterings such as non-line-of-sight (NLOS) links in a complex propagation environment. In this paper, we propose an interpretable hybrid learning model with expert knowledge to predict the channel pathloss in desert-like environment using terrain profiles. We apply an autoencoder to extract compressed information from terrain profiles. The compressed representation of terrain, combined with features selected based on expert knowledge such as LOS/NLOS indicator and curvature of the terrain, are used to predict the pathloss. We show that a Random Forest regression model outperforms CNN/DNN models in generalizability of predicting unseen data by training and testing in disjoint sectors of the measured areas.
{"title":"Channel Prediction over Irregular Terrains: Deep Autoencoder with Random Forest","authors":"Yuyang Wang, Shiva R. Iyer, D. Chizhik, Jinfeng Du, R. Valenzuela","doi":"10.1109/spawc51304.2022.9833965","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833965","url":null,"abstract":"Channel modeling is critical for coverage prediction, system level simulations, and wireless propagation characterization. Industry practice applies linear fit to the pathloss in decibels against the logarithm of the distance. Simple linear fit, however, cannot fully capture the shadowing effects in the channel, especially for a link with rich scatterings such as non-line-of-sight (NLOS) links in a complex propagation environment. In this paper, we propose an interpretable hybrid learning model with expert knowledge to predict the channel pathloss in desert-like environment using terrain profiles. We apply an autoencoder to extract compressed information from terrain profiles. The compressed representation of terrain, combined with features selected based on expert knowledge such as LOS/NLOS indicator and curvature of the terrain, are used to predict the pathloss. We show that a Random Forest regression model outperforms CNN/DNN models in generalizability of predicting unseen data by training and testing in disjoint sectors of the measured areas.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121437833","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833971
Sai Wang, Yi Gong
Federated edge learning has attracted great attention for edge intelligent networks. Due to the limited computation and energy, mobile devices usually need to offload data to nearby edge servers. Facing this scenario, we design a cloud-aided federated edge learning (CA-FEEL) framework where the edges cooperate with the cloud to train a federated learning model. Specifically, the edges adopt the gradient descent (GD) method in parallel to update the edge parameters and the cloud averages them to update the global parameter. By theoretical analysis, we find that the covariance of non-independent and identically distributed (non-IID) data sets hinders the convergence of the GD based FL. Thus, we propose a CA-FEEL algorithm by adding a simple judgment condition. It is proved to have a theoretical guarantee of convergence for convex and smooth problems. Experiment results indicate that the proposed algorithm outperforms the standard federated learning in terms of the convergence rate and accuracy.
{"title":"Convergence Analysis of Cloud-Aided Federated Edge Learning on Non-IID Data","authors":"Sai Wang, Yi Gong","doi":"10.1109/spawc51304.2022.9833971","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833971","url":null,"abstract":"Federated edge learning has attracted great attention for edge intelligent networks. Due to the limited computation and energy, mobile devices usually need to offload data to nearby edge servers. Facing this scenario, we design a cloud-aided federated edge learning (CA-FEEL) framework where the edges cooperate with the cloud to train a federated learning model. Specifically, the edges adopt the gradient descent (GD) method in parallel to update the edge parameters and the cloud averages them to update the global parameter. By theoretical analysis, we find that the covariance of non-independent and identically distributed (non-IID) data sets hinders the convergence of the GD based FL. Thus, we propose a CA-FEEL algorithm by adding a simple judgment condition. It is proved to have a theoretical guarantee of convergence for convex and smooth problems. Experiment results indicate that the proposed algorithm outperforms the standard federated learning in terms of the convergence rate and accuracy.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122378033","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833926
B. Manoj, P. M. Santos, Meysam Sadeghi, E. Larsson
Deep learning (DL) is a powerful technique for many real-time applications, but it is vulnerable to adversarial attacks. Herein, we consider DL-based modulation classification, with the objective to create DL models that are robust against attacks. Specifically, we introduce three defense techniques: i) randomized smoothing, ii) hybrid projected gradient descent adversarial training, and iii) fast adversarial training, and evaluate them under both white-box (WB) and black-box (BB) attacks. We show that the proposed fast adversarial training is more robust and computationally efficient than the other techniques, and can create models that are extremely robust to practical (BB) attacks.
{"title":"Toward Robust Networks against Adversarial Attacks for Radio Signal Modulation Classification","authors":"B. Manoj, P. M. Santos, Meysam Sadeghi, E. Larsson","doi":"10.1109/spawc51304.2022.9833926","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833926","url":null,"abstract":"Deep learning (DL) is a powerful technique for many real-time applications, but it is vulnerable to adversarial attacks. Herein, we consider DL-based modulation classification, with the objective to create DL models that are robust against attacks. Specifically, we introduce three defense techniques: i) randomized smoothing, ii) hybrid projected gradient descent adversarial training, and iii) fast adversarial training, and evaluate them under both white-box (WB) and black-box (BB) attacks. We show that the proposed fast adversarial training is more robust and computationally efficient than the other techniques, and can create models that are extremely robust to practical (BB) attacks.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"289 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115218843","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9834022
Rami Klaimi, Stefan Weithoffer, C. A. Nour
Non-binary forward error correction (FEC) codes have been getting more attention lately in the coding society thanks mainly to their improved error correcting capabilities. Indeed, they reveal their full potential in the case of a one-to-one mapping between the code symbols over Galois fields (GF) and constellation points of the same order. Previously, we proposed non-binary FEC code designs targeting a given classical constellation through the optimization of the minimum Euclidean distance between candidate codewords. To go a step further, a better Euclidean distance spectrum can be achieved through the joint optimization of code parameters and positions of constellation symbols. However, this joint optimization for high order GFs reveals to be intractable in number of cases to evaluate. Therefore in this work, we propose a solution based on the multi-agent Deep Q-Network (DQN) algorithm. Applied to non-binary turbo codes (NB-TCs) over GF(64), the proposal largely improves performance by significantly lowering the error floor region of the resulting coded modulation scheme.
{"title":"Improved Non-Uniform Constellations for Non-Binary Codes Through Deep Reinforcement Learning","authors":"Rami Klaimi, Stefan Weithoffer, C. A. Nour","doi":"10.1109/spawc51304.2022.9834022","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9834022","url":null,"abstract":"Non-binary forward error correction (FEC) codes have been getting more attention lately in the coding society thanks mainly to their improved error correcting capabilities. Indeed, they reveal their full potential in the case of a one-to-one mapping between the code symbols over Galois fields (GF) and constellation points of the same order. Previously, we proposed non-binary FEC code designs targeting a given classical constellation through the optimization of the minimum Euclidean distance between candidate codewords. To go a step further, a better Euclidean distance spectrum can be achieved through the joint optimization of code parameters and positions of constellation symbols. However, this joint optimization for high order GFs reveals to be intractable in number of cases to evaluate. Therefore in this work, we propose a solution based on the multi-agent Deep Q-Network (DQN) algorithm. Applied to non-binary turbo codes (NB-TCs) over GF(64), the proposal largely improves performance by significantly lowering the error floor region of the resulting coded modulation scheme.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127488100","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833916
M. Laakso, A. Dowhuszko, R. Wichman
In Visible Light Communications (VLC) systems, the Light-Emitting Diode (LED) is the dominant source of non-linearity and memory effects, which are originated on phenomena that take place in both the electrical and optical domains. The impact that these LED non-idealities have on the received data symbols becomes even more notable with OFDM waveforms due to their high Peak-to-Average power Ratio (PAPR) of these signals. One simple way to address this problem consists in selecting a suitable Input Back-Off (IBO) value, forcing the LED to work in its linear region. However, such an approach limits the VLC system coverage, as it reduces the dynamic range of the OFDM signal that modulates the intensity of the optical wireless link. To provide a balance between these two conflicting goals, the use of digital predistortion can be considered instead, in order to compensate nonlinear distortion and memory effects that are added in the VLC transmitter. For this purpose, this paper studies the sources of nonlinearity and memory in phosphor-converted (PC)-LEDs in both electrical and optical domains. After that, different approaches are presented to model these effects in the PC-LED, namely the Wiener-Hammerstein, memory polynomial, and Convolutional Neural Network (CNN) models. Finally, the performance of each of these approaches for digital predistortion are experimentally evaluated in a software-defined VLC demonstrator, observing a notable improvement on the Error Vector Magnitude (EVM) when compared to the case in which no compensation is performed in transmission.
{"title":"Predistortion of OFDM signals for VLC systems using phosphor-converted LEDs","authors":"M. Laakso, A. Dowhuszko, R. Wichman","doi":"10.1109/spawc51304.2022.9833916","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833916","url":null,"abstract":"In Visible Light Communications (VLC) systems, the Light-Emitting Diode (LED) is the dominant source of non-linearity and memory effects, which are originated on phenomena that take place in both the electrical and optical domains. The impact that these LED non-idealities have on the received data symbols becomes even more notable with OFDM waveforms due to their high Peak-to-Average power Ratio (PAPR) of these signals. One simple way to address this problem consists in selecting a suitable Input Back-Off (IBO) value, forcing the LED to work in its linear region. However, such an approach limits the VLC system coverage, as it reduces the dynamic range of the OFDM signal that modulates the intensity of the optical wireless link. To provide a balance between these two conflicting goals, the use of digital predistortion can be considered instead, in order to compensate nonlinear distortion and memory effects that are added in the VLC transmitter. For this purpose, this paper studies the sources of nonlinearity and memory in phosphor-converted (PC)-LEDs in both electrical and optical domains. After that, different approaches are presented to model these effects in the PC-LED, namely the Wiener-Hammerstein, memory polynomial, and Convolutional Neural Network (CNN) models. Finally, the performance of each of these approaches for digital predistortion are experimentally evaluated in a software-defined VLC demonstrator, observing a notable improvement on the Error Vector Magnitude (EVM) when compared to the case in which no compensation is performed in transmission.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134361921","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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