Pub Date : 2023-07-02DOI: 10.1109/SSP53291.2023.10208046
Swati Bhattacharya, K. Hari, Y. Eldar
This paper proposes a Joint Channel Estimation and Symbol Detection (JED) scheme for overloaded multiple-input multiple-output (MIMO) wireless communication systems, with the number of receive antennas being less than or equal to the number of transmit antennas. Our proposed method for JED using Alternating Direction Method of Multipliers (JED-ADMM) markedly improves the symbol detection performance by yielding 12-16 dB gain in signal-to-noise ratio (SNR) for a bit error rate (BER) of 10−3 over state-of-the-art JED using Alternating Minimization (JED-AM). This gain in BER for the proposed JED-ADMM is also accompanied by a significant reduction in computational complexity (1/4 times) as compared to JED-AM.
{"title":"Joint Channel Estimation and Symbol Detection in Overloaded MIMO Using ADMM","authors":"Swati Bhattacharya, K. Hari, Y. Eldar","doi":"10.1109/SSP53291.2023.10208046","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208046","url":null,"abstract":"This paper proposes a Joint Channel Estimation and Symbol Detection (JED) scheme for overloaded multiple-input multiple-output (MIMO) wireless communication systems, with the number of receive antennas being less than or equal to the number of transmit antennas. Our proposed method for JED using Alternating Direction Method of Multipliers (JED-ADMM) markedly improves the symbol detection performance by yielding 12-16 dB gain in signal-to-noise ratio (SNR) for a bit error rate (BER) of 10−3 over state-of-the-art JED using Alternating Minimization (JED-AM). This gain in BER for the proposed JED-ADMM is also accompanied by a significant reduction in computational complexity (1/4 times) as compared to JED-AM.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114574845","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207970
Ori Aharon, J. Tabrikian
In this paper, a class of tight Bayesian bounds on the mean-squared-error is proposed. Tight bounds account for the contribution of sidelobes in the likelihood ratio or the ambiguity function. Since the distances between the main lobe and the sidelobes in the likelihood function may depend on the unknown parameter, a single, parameter-independent test-point may not be enough to provide a tight bound. In the proposed class of bounds, the shift test-points are substituted with arbitrary transformations, such that the same test-point can be uniformly optimal for the entire parameter space. The use of single testpoint simplifies the bound and allows providing insight into the considered problem. The proposed bound is applied to the problem of direction-of-arrival estimation using a linear array. Simulations show that the proposed bound accurately predicts the threshold phenomenon of the maximum a-posteriori probability estimator, and is tighter than the Weiss-Weinstein bound.
{"title":"A Simple and Tight Bayesian Lower Bound for Direction-of-Arrival Estimation","authors":"Ori Aharon, J. Tabrikian","doi":"10.1109/SSP53291.2023.10207970","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207970","url":null,"abstract":"In this paper, a class of tight Bayesian bounds on the mean-squared-error is proposed. Tight bounds account for the contribution of sidelobes in the likelihood ratio or the ambiguity function. Since the distances between the main lobe and the sidelobes in the likelihood function may depend on the unknown parameter, a single, parameter-independent test-point may not be enough to provide a tight bound. In the proposed class of bounds, the shift test-points are substituted with arbitrary transformations, such that the same test-point can be uniformly optimal for the entire parameter space. The use of single testpoint simplifies the bound and allows providing insight into the considered problem. The proposed bound is applied to the problem of direction-of-arrival estimation using a linear array. Simulations show that the proposed bound accurately predicts the threshold phenomenon of the maximum a-posteriori probability estimator, and is tighter than the Weiss-Weinstein bound.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116350581","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}
Fluid antenna system (FAS) is a flexible antenna structure that obtains tremendous space diversity by allowing the antenna to change its position (or port) in a given space. The extraordinary performance requires FAS to always switch to the port with the largest signal-to-noise ratio (SNR) from the large number of ports. In practice, however, this means that a large number of channel observations are required and the overhead could outweigh the benefits. In this paper, we exploit the spatial and temporal correlation of the port channels using a machine learning approach. The proposed algorithm first estimates all the port channels in space from a small number of observations, then predicts the port channels in the subsequent time slots. Re-observations are used to reduce error propagation in long short-term memory (LSTM) rolling window regression. Simulation results demonstrate that the proposed algorithm can achieve promising performance with few re-observations in high-mobility scenarios.
{"title":"Fast Port Selection using Temporal and Spatial Correlation for Fluid Antenna Systems","authors":"Shunhang Zhang, Jinghan Mao, Yanzhao Hou, Yu Chen, Kai‐Kit Wong, Qimei Cui, Xiaofeng Tao","doi":"10.1109/SSP53291.2023.10207934","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207934","url":null,"abstract":"Fluid antenna system (FAS) is a flexible antenna structure that obtains tremendous space diversity by allowing the antenna to change its position (or port) in a given space. The extraordinary performance requires FAS to always switch to the port with the largest signal-to-noise ratio (SNR) from the large number of ports. In practice, however, this means that a large number of channel observations are required and the overhead could outweigh the benefits. In this paper, we exploit the spatial and temporal correlation of the port channels using a machine learning approach. The proposed algorithm first estimates all the port channels in space from a small number of observations, then predicts the port channels in the subsequent time slots. Re-observations are used to reduce error propagation in long short-term memory (LSTM) rolling window regression. Simulation results demonstrate that the proposed algorithm can achieve promising performance with few re-observations in high-mobility scenarios.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127566953","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207996
Zai Yang, Kai Wang
Direction augmentation (DA), followed by a subspace method such as MUSIC or ESPRIT, is a successful approach that enables localization of more uncorrelated sources than sensors with a proper sparse linear array. In this paper, we carry out a nonasymptotic performance analysis of DA-ESPRIT in the practical scenario with finitely many snapshots. We show that more uncorrelated sources than sensors are guaranteed, with overwhelming probability, to be localized using DA-ESPRIT if the number of snapshots is greater than an explicit, problem-dependent threshold. Our result does not require a fixed source separation condition, which makes it unique among existing results. Numerical results corroborating our analysis are provided.
与使用适当稀疏线性阵列的传感器相比，使用方向增强（DA）和子空间方法（如 MUSIC 或 ESPRIT）可以定位更多不相关的信号源，是一种成功的方法。在本文中，我们对具有有限多个快照的实际场景中的 DA-ESPRIT 进行了非渐近性能分析。我们的研究表明，如果快照数量大于一个明确的、与问题相关的阈值，那么使用 DA-ESPRIT 可以保证以压倒性的概率定位到比传感器更多的不相关源。我们的结果不需要固定的源分离条件，这使得它在现有结果中独一无二。我们提供的数值结果证实了我们的分析。
{"title":"Nonasymptotic Analysis of Direct-Augmentation ESPRIT for Localization of More Sources Than Sensors Using Sparse Arrays","authors":"Zai Yang, Kai Wang","doi":"10.1109/SSP53291.2023.10207996","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207996","url":null,"abstract":"Direction augmentation (DA), followed by a subspace method such as MUSIC or ESPRIT, is a successful approach that enables localization of more uncorrelated sources than sensors with a proper sparse linear array. In this paper, we carry out a nonasymptotic performance analysis of DA-ESPRIT in the practical scenario with finitely many snapshots. We show that more uncorrelated sources than sensors are guaranteed, with overwhelming probability, to be localized using DA-ESPRIT if the number of snapshots is greater than an explicit, problem-dependent threshold. Our result does not require a fixed source separation condition, which makes it unique among existing results. Numerical results corroborating our analysis are provided.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126033738","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208083
V. Dieu, D. Tran, Khanh-Ly Can, T. Dao, Dinh-Dat Pham, Duc-Tan Tran
It has been well established that sleep posture plays a key role in sleep quality monitoring. Consequently, many noncontact and wearable devices, whose systems rely on sensors such as cameras, radar, wireless, and accelerometers, have been developed to classify sleep positions and postures. However, noncontact systems were often unsuccessful when facing limited conditions such as low light and physical obstacles. On the other hand, other systems currently in research, which involves wearable devices, may have used machine learning models but have not competently exploited other more accurate deep learning models. Recognizing scope for improvement, we propose an enhanced five-sleep-posture classification system (5-SPCS) where a novel integration of accelerometer and an LSTM deep learning model can classify sleep postures more efficiently than either one of them does separately. Our experiments showed that the 5-SPCS was capable of outperforming the baselines of existing machine learning-accelerometer systems at 99.6% accuracy.
{"title":"Enhancing sleep postures classification by incorporating acceleration sensor and LSTM model","authors":"V. Dieu, D. Tran, Khanh-Ly Can, T. Dao, Dinh-Dat Pham, Duc-Tan Tran","doi":"10.1109/SSP53291.2023.10208083","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208083","url":null,"abstract":"It has been well established that sleep posture plays a key role in sleep quality monitoring. Consequently, many noncontact and wearable devices, whose systems rely on sensors such as cameras, radar, wireless, and accelerometers, have been developed to classify sleep positions and postures. However, noncontact systems were often unsuccessful when facing limited conditions such as low light and physical obstacles. On the other hand, other systems currently in research, which involves wearable devices, may have used machine learning models but have not competently exploited other more accurate deep learning models. Recognizing scope for improvement, we propose an enhanced five-sleep-posture classification system (5-SPCS) where a novel integration of accelerometer and an LSTM deep learning model can classify sleep postures more efficiently than either one of them does separately. Our experiments showed that the 5-SPCS was capable of outperforming the baselines of existing machine learning-accelerometer systems at 99.6% accuracy.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125757445","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207995
Anju Anand, E. Akyol
We consider the design problem of a strategic quantizer over a noisy channel, extending the classical work on channel-optimized quantization to strategic settings where the encoder and the decoder have misaligned objectives. Building on our recent work on strategic quantization over noiseless channels, we employ a random channel index assignment mapping, as done in prior work on classical channel-optimized quantizer design literature, combined with a dynamic programming approach to optimize quantization boundaries. Our analysis and numerical results demonstrate several interesting aspects of channel-optimized strategic quantization which do not appear in its classical (nonstrategic) counterpart. The codes are available at: https://tinyurl.com/ssp2023dpnoise.
{"title":"Channel-Optimized Strategic Quantizer Design via Dynamic Programming","authors":"Anju Anand, E. Akyol","doi":"10.1109/SSP53291.2023.10207995","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207995","url":null,"abstract":"We consider the design problem of a strategic quantizer over a noisy channel, extending the classical work on channel-optimized quantization to strategic settings where the encoder and the decoder have misaligned objectives. Building on our recent work on strategic quantization over noiseless channels, we employ a random channel index assignment mapping, as done in prior work on classical channel-optimized quantizer design literature, combined with a dynamic programming approach to optimize quantization boundaries. Our analysis and numerical results demonstrate several interesting aspects of channel-optimized strategic quantization which do not appear in its classical (nonstrategic) counterpart. The codes are available at: https://tinyurl.com/ssp2023dpnoise.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124201826","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208034
Aurélien Olivier, C. Hoffmann, A. Mansour, L. Bressollette, Benoit Clement
Venous Thromboembolism (VTE) is a life-threatening disease encompassing pulmonary embolism and deep venous thrombosis (DVT). Pulmonary embolism occurs in 50% of patients with a proximal deep venous thrombosis. We aimed to predict the occurrence of a pulmonary embolism in patients with a DVT from clinical data and Ultrasound images of proximal thrombosis. To address this task, we proposed to use a Deep learning model that uses both images and 5 clinical factors as input and we aimed to measure the contributions compared to using only images. Promising results were obtained with both models compared to the state-of-art. The contribution of the clinical factors remains unclear but a gain in accuracy was observed when using smaller models.
{"title":"Fusion of images and clinical features for the prediction of Pulmonary embolism in Ultrasound imaging","authors":"Aurélien Olivier, C. Hoffmann, A. Mansour, L. Bressollette, Benoit Clement","doi":"10.1109/SSP53291.2023.10208034","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208034","url":null,"abstract":"Venous Thromboembolism (VTE) is a life-threatening disease encompassing pulmonary embolism and deep venous thrombosis (DVT). Pulmonary embolism occurs in 50% of patients with a proximal deep venous thrombosis. We aimed to predict the occurrence of a pulmonary embolism in patients with a DVT from clinical data and Ultrasound images of proximal thrombosis. To address this task, we proposed to use a Deep learning model that uses both images and 5 clinical factors as input and we aimed to measure the contributions compared to using only images. Promising results were obtained with both models compared to the state-of-art. The contribution of the clinical factors remains unclear but a gain in accuracy was observed when using smaller models.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125394108","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208010
David Sundström, J. Lindström, A. Jakobsson
Recent advances have shown that sound fields can be accurately interpolated between microphone measurements when the spatial covariance matrix is known. This matrix may be estimated in various ways; one promising approach is to use a plane wave formulation with sparse priors, although this may require the use of a many microphones to suppress the noise. To overcome this, we introduce a time domain formulation exploiting multiple time samples, posing the problem as an identification problem of a recursively estimated sample covariance matrix. A computationally efficient method is proposed to solve the resulting identification problem. Using both numerical experiments and anechoic data, the proposed method is shown to yield preferable performance as compared to current state of the art methods, notably for high frequencies sources and/or in cases when using few microphones.
{"title":"Recursive Spatial Covariance Estimation with Sparse Priors for Sound Field Interpolation","authors":"David Sundström, J. Lindström, A. Jakobsson","doi":"10.1109/SSP53291.2023.10208010","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208010","url":null,"abstract":"Recent advances have shown that sound fields can be accurately interpolated between microphone measurements when the spatial covariance matrix is known. This matrix may be estimated in various ways; one promising approach is to use a plane wave formulation with sparse priors, although this may require the use of a many microphones to suppress the noise. To overcome this, we introduce a time domain formulation exploiting multiple time samples, posing the problem as an identification problem of a recursively estimated sample covariance matrix. A computationally efficient method is proposed to solve the resulting identification problem. Using both numerical experiments and anechoic data, the proposed method is shown to yield preferable performance as compared to current state of the art methods, notably for high frequencies sources and/or in cases when using few microphones.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129710213","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10208059
J. McQuire, Paul Watson, Nick Wright, H. Hiden, M. Catt
This study introduces the Data Efficient Separable Transformer (DeSepTr) architecture, a novel framework for Human Activity Recognition (HAR) that utilizes a light-weight computer vision model to train a Vision Transformer (ViT) on spectrograms generated from wearable sensor data. The proposed model achieves strong results on several HAR tasks, including surface condition recognition and activity recognition. Compared to the ResNet-18 model, DeSepTr outperforms by 5.9% on out-of-distribution test data accuracy for surface condition recognition. The framework enables ViTs to learn from limited labeled training data and generalize to data from participants outside of the training cohort, potentially leading to the development of activity recognition models that are robust to the wider population. The results suggest that the DeSepTr architecture can overcome limitations related to the heterogeneity of individuals’ behavior patterns and the weak inductive bias of transformer algorithms.
{"title":"A Data Efficient Vision Transformer for Robust Human Activity Recognition from the Spectrograms of Wearable Sensor Data","authors":"J. McQuire, Paul Watson, Nick Wright, H. Hiden, M. Catt","doi":"10.1109/SSP53291.2023.10208059","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208059","url":null,"abstract":"This study introduces the Data Efficient Separable Transformer (DeSepTr) architecture, a novel framework for Human Activity Recognition (HAR) that utilizes a light-weight computer vision model to train a Vision Transformer (ViT) on spectrograms generated from wearable sensor data. The proposed model achieves strong results on several HAR tasks, including surface condition recognition and activity recognition. Compared to the ResNet-18 model, DeSepTr outperforms by 5.9% on out-of-distribution test data accuracy for surface condition recognition. The framework enables ViTs to learn from limited labeled training data and generalize to data from participants outside of the training cohort, potentially leading to the development of activity recognition models that are robust to the wider population. The results suggest that the DeSepTr architecture can overcome limitations related to the heterogeneity of individuals’ behavior patterns and the weak inductive bias of transformer algorithms.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133882583","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 : 2023-07-02DOI: 10.1109/SSP53291.2023.10207935
Jad Abou Chaaya, A. Coatanhay, A. Mansour, T. Marsault
Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular for both civil and military missions, and communication link establishment between the UAV and ground/aerial stations is a crucial factor for mission success. However, topography greatly affects the communication link, particularly when the UAV is flying at a low altitude between mountains of varying elevations. This paper proposes a system model based on the diffraction phenomenon with Multiple Knife Edges (MKE) to model the UAV-station channel when the Line of Sight (LoS) is absent. The objective is to optimize the trajectory of low/mid-altitude flying UAVs in complex propagation environments. To maximize communication quality, the paper also proposes an optimization formulation using Mixed Integer Linear Programming (MILP). The proposed approach is validated through simulations that limit LoS propagation using real terrain profiles. The approach finds the optimal UAV trajectory with the "best feasible" communication quality within physical limitations.
{"title":"Communication Quality Optimization for UAV Trajectory in Irregular Topography","authors":"Jad Abou Chaaya, A. Coatanhay, A. Mansour, T. Marsault","doi":"10.1109/SSP53291.2023.10207935","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207935","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular for both civil and military missions, and communication link establishment between the UAV and ground/aerial stations is a crucial factor for mission success. However, topography greatly affects the communication link, particularly when the UAV is flying at a low altitude between mountains of varying elevations. This paper proposes a system model based on the diffraction phenomenon with Multiple Knife Edges (MKE) to model the UAV-station channel when the Line of Sight (LoS) is absent. The objective is to optimize the trajectory of low/mid-altitude flying UAVs in complex propagation environments. To maximize communication quality, the paper also proposes an optimization formulation using Mixed Integer Linear Programming (MILP). The proposed approach is validated through simulations that limit LoS propagation using real terrain profiles. The approach finds the optimal UAV trajectory with the \"best feasible\" communication quality within physical limitations.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132258632","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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