Pub Date : 2024-08-28DOI: 10.1109/OJCOMS.2024.3451313
Rodney Martinez Alonso;Robbert Beerten;Achiel Colpaert;Andrea P. Guevara;Sofie Pollin
Cell-free massive MIMO networks have recently emerged as an attractive solution capable of solving the performance degradation at the cell edge of cellular networks. For scalability reasons, usercentric clusters were recently proposed to serve users via a subset of APs. In the case of dynamic mobile scenarios, this network organization requires predictive algorithms for forecasting propagation parameters to maintain performance by proactively allocating new APs to a user. However, a major scientific challenge is the accuracy of predicting the channel gain evolution in non-stationary channels with low computational complexity, considering the uncertainty caused by user mobility. The novelty of this paper is the design of a multidimensional BiLSTM-based multivariate channel gain forecasting algorithm achieving a similar accuracy to previous research at reduced computational complexity. Indeed, thanks to the combination of dual prediction by the multidimensional BiLSTM exploiting the channel diversity from multiple antennas, our model mitigates the error propagation typically faced by sequential neural networks. Our model has a lower error by at least a factor of 2.7 and lower complexity by a factor of 3.6 (for a single prediction), compared to hybrid CNN-LSTM model. Also, in contrast to parallel transformer solutions, the growth rate of the complexity of our algorithm is significantly lower.
{"title":"Trajectory-Unaware Channel Gain Forecast in a Distributed Massive MIMO System Based on a Multivariate BiLSTM Model","authors":"Rodney Martinez Alonso;Robbert Beerten;Achiel Colpaert;Andrea P. Guevara;Sofie Pollin","doi":"10.1109/OJCOMS.2024.3451313","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3451313","url":null,"abstract":"Cell-free massive MIMO networks have recently emerged as an attractive solution capable of solving the performance degradation at the cell edge of cellular networks. For scalability reasons, usercentric clusters were recently proposed to serve users via a subset of APs. In the case of dynamic mobile scenarios, this network organization requires predictive algorithms for forecasting propagation parameters to maintain performance by proactively allocating new APs to a user. However, a major scientific challenge is the accuracy of predicting the channel gain evolution in non-stationary channels with low computational complexity, considering the uncertainty caused by user mobility. The novelty of this paper is the design of a multidimensional BiLSTM-based multivariate channel gain forecasting algorithm achieving a similar accuracy to previous research at reduced computational complexity. Indeed, thanks to the combination of dual prediction by the multidimensional BiLSTM exploiting the channel diversity from multiple antennas, our model mitigates the error propagation typically faced by sequential neural networks. Our model has a lower error by at least a factor of 2.7 and lower complexity by a factor of 3.6 (for a single prediction), compared to hybrid CNN-LSTM model. Also, in contrast to parallel transformer solutions, the growth rate of the complexity of our algorithm is significantly lower.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1109/OJCOMS.2024.3450809
Mohammed S. Elbasheir;Rashid A. Saeed;Salaheldin Edam
Nowadays, significant developments in wireless technologies and solutions have led to the rapid expansion of mobile networks, and it’s expected to grow more, particularly with the launch of the Fifth Generation New Radio (5G NR). The deployment of a large number of base stations (BSs) is raising concerns about the potential for increased exposure to electromagnetic field radiation (EMF). Many international and national regulators have set guidelines and regulations to control the amount of EMF radiation. This paper presents a design model to de-concentrate the total exposure from sectorized antennas of the multi-technology base station with no drawback on network coverage level and key performance indicators (KPIs). The model applies the concept of weighted antenna’s azimuth to spread the total exposure by horizontally separating the installed antennas in the same sector. A set of simulations is conducted to calculate the reduction in total exposure ratio (TER) for widely used setups in antenna deployment for multi-technology mobile networks. Additionally, A field test was done in a life network to evaluate the proposed model in the geographical cluster, and a set of field measurements was conducted to assess the TER and the compliance distance (CD) before and after the test implementation. Further, the operation support system (OSS) records and counters were analyzed to evaluate the impact on the network coverage and capacity behavior, especially for the carried traffic and number of users. The pre-and-post results show that the TER and CD are improved by a valuable reduction after applying the proposed model. Overall, the system records show no significant impacts were registered on network coverage level and capacity performance for all transmitting technologies of the sites involved in the test.
如今，无线技术和解决方案的重大发展导致了移动网络的快速扩张，尤其是随着第五代新无线电（5G NR）的推出，预计这种扩张将更加迅猛。大量基站（BS）的部署引发了人们对电磁场辐射（EMF）暴露可能增加的担忧。许多国际和国家监管机构已经制定了控制电磁场辐射量的指导方针和法规。本文提出了一种设计模型，可在不影响网络覆盖水平和关键性能指标（KPIs）的情况下，分散来自多技术基站扇区化天线的总辐射量。该模型应用了加权天线方位角的概念，通过水平分离同一扇区内已安装的天线来分散总曝光量。该模型进行了一系列模拟，以计算在多技术移动网络天线部署中广泛使用的设置所减少的总暴露率（TER）。此外，还在一个生活网络中进行了实地测试，以评估地理集群中的拟议模型，并进行了一系列实地测量，以评估测试实施前后的总暴露率和符合距离（CD）。此外，还分析了运营支持系统（OSS）的记录和计数器，以评估其对网络覆盖和容量行为的影响，尤其是对承载流量和用户数量的影响。前后结果表明，在应用建议的模型后，TER 和 CD 都有了可观的改善。总体而言，系统记录显示，对参与测试的所有站点的所有传输技术而言，网络覆盖水平和容量性能均未受到明显影响。
{"title":"Weighted Antenna’s Azimuth for Minimal EMF With Sustainable KPIs of Multi-Technology BS","authors":"Mohammed S. Elbasheir;Rashid A. Saeed;Salaheldin Edam","doi":"10.1109/OJCOMS.2024.3450809","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3450809","url":null,"abstract":"Nowadays, significant developments in wireless technologies and solutions have led to the rapid expansion of mobile networks, and it’s expected to grow more, particularly with the launch of the Fifth Generation New Radio (5G NR). The deployment of a large number of base stations (BSs) is raising concerns about the potential for increased exposure to electromagnetic field radiation (EMF). Many international and national regulators have set guidelines and regulations to control the amount of EMF radiation. This paper presents a design model to de-concentrate the total exposure from sectorized antennas of the multi-technology base station with no drawback on network coverage level and key performance indicators (KPIs). The model applies the concept of weighted antenna’s azimuth to spread the total exposure by horizontally separating the installed antennas in the same sector. A set of simulations is conducted to calculate the reduction in total exposure ratio (TER) for widely used setups in antenna deployment for multi-technology mobile networks. Additionally, A field test was done in a life network to evaluate the proposed model in the geographical cluster, and a set of field measurements was conducted to assess the TER and the compliance distance (CD) before and after the test implementation. Further, the operation support system (OSS) records and counters were analyzed to evaluate the impact on the network coverage and capacity behavior, especially for the carried traffic and number of users. The pre-and-post results show that the TER and CD are improved by a valuable reduction after applying the proposed model. Overall, the system records show no significant impacts were registered on network coverage level and capacity performance for all transmitting technologies of the sites involved in the test.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1109/OJCOMS.2024.3451308
Donatella Darsena;Ivan Iudice;Francesco Verde
Integration of unmanned aerial vehicles (UAVs) for surveillance or monitoring applications into fifth generation (5G) New Radio (NR) cellular networks is an intriguing problem that has recently tackled a lot of interest in both academia and industry. For an efficient spectrum usage, we consider a recently-proposed sky-ground nonorthogonal multiple access (NOMA) scheme, where a cellular-connected UAV acting as aerial user (AU) and a static terrestrial user (TU) are paired to simultaneously transmit their uplink signals to a base station (BS) in the same time-frequency resource blocks. In such a case, due to the highly dynamic nature of the UAV, the signal transmitted by the AU experiences both time dispersion due to multipath propagation effects and frequency dispersion caused by Doppler shifts. On the other hand, for a static ground network, frequency dispersion of the signal transmitted by the TU is negligible and only multipath effects have to be taken into account. To decode the superposed signals at the BS through successive interference cancelation, accurate estimates of both the AU and TU channels are needed. In this paper, we propose channel estimation procedures that suitably exploit the different circular/noncircular modulation formats (modulation diversity) and the different almost-cyclostationarity features (Doppler diversity) of the AU and TU by means of widely-linear time-varying processing. Our estimation approach is semi-blind since Doppler shifts and time delays of the AU are estimated based on the received data only, whereas the remaining relevant parameters of the AU and TU channels are acquired relying also on the available training symbols, which are transmitted by the AU and TU in a nonorthogonal manner. Monte Carlo numerical results demonstrate that the proposed channel estimation algorithms can satisfactorily acquire all the relevant parameters in different operative conditions.
将用于监视或监测应用的无人驾驶飞行器（UAV）集成到第五代（5G）新无线电（NR）蜂窝网络中是一个引人入胜的问题，最近在学术界和工业界都引起了广泛关注。为了有效利用频谱，我们考虑了最近提出的天-地非正交多址接入（NOMA）方案，在该方案中，作为空中用户（AU）的蜂窝连接无人机与静态地面用户（TU）配对，在相同的时频资源块中同时向基站（BS）传输上行链路信号。在这种情况下，由于无人机的高动态特性，空中用户传输的信号既会因多径传播效应而出现时间色散，也会因多普勒频移而出现频率色散。另一方面，在静态地面网络中，由 TU 传输的信号的频散可以忽略不计，只需考虑多径效应。要在 BS 上通过连续干扰消除对叠加信号进行解码，需要对 AU 和 TU 信道进行精确估计。在本文中，我们提出了信道估计程序，通过广泛的线性时变处理，适当利用 AU 和 TU 的不同环形/非环形调制格式（调制多样性）和不同的几乎同步特性（多普勒多样性）。我们的估计方法是半盲式的，因为 AU 的多普勒频移和时延仅根据接收到的数据进行估计，而 AU 和 TU 信道的其余相关参数则根据可用的训练符号获取，训练符号由 AU 和 TU 以非正交方式传输。蒙特卡洛数值结果表明，所提出的信道估计算法能在不同工作条件下令人满意地获取所有相关参数。
{"title":"Channel State Acquisition in Uplink NOMA for Cellular-Connected UAV: Exploitation of Doppler and Modulation Diversities","authors":"Donatella Darsena;Ivan Iudice;Francesco Verde","doi":"10.1109/OJCOMS.2024.3451308","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3451308","url":null,"abstract":"Integration of unmanned aerial vehicles (UAVs) for surveillance or monitoring applications into fifth generation (5G) New Radio (NR) cellular networks is an intriguing problem that has recently tackled a lot of interest in both academia and industry. For an efficient spectrum usage, we consider a recently-proposed sky-ground nonorthogonal multiple access (NOMA) scheme, where a cellular-connected UAV acting as aerial user (AU) and a static terrestrial user (TU) are paired to simultaneously transmit their uplink signals to a base station (BS) in the same time-frequency resource blocks. In such a case, due to the highly dynamic nature of the UAV, the signal transmitted by the AU experiences both time dispersion due to multipath propagation effects and frequency dispersion caused by Doppler shifts. On the other hand, for a static ground network, frequency dispersion of the signal transmitted by the TU is negligible and only multipath effects have to be taken into account. To decode the superposed signals at the BS through successive interference cancelation, accurate estimates of both the AU and TU channels are needed. In this paper, we propose channel estimation procedures that suitably exploit the different circular/noncircular modulation formats (modulation diversity) and the different almost-cyclostationarity features (Doppler diversity) of the AU and TU by means of widely-linear time-varying processing. Our estimation approach is semi-blind since Doppler shifts and time delays of the AU are estimated based on the received data only, whereas the remaining relevant parameters of the AU and TU channels are acquired relying also on the available training symbols, which are transmitted by the AU and TU in a nonorthogonal manner. Monte Carlo numerical results demonstrate that the proposed channel estimation algorithms can satisfactorily acquire all the relevant parameters in different operative conditions.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/OJCOMS.2024.3449341
Beomsoo Ko;Hwanjin Kim;Minje Kim;Junil Choi
Channel prediction compensates for outdated channel state information in multiple-input multiple-output (MIMO) systems. Machine learning (ML) techniques have recently been implemented to design channel predictors by leveraging the temporal correlation of wireless channels. However, most ML-based channel prediction techniques have only considered offline training when generating channel predictors, which can result in poor performance when encountering channel environments different from the ones they were trained on. To ensure prediction performance in varying channel conditions, we propose an online re-training framework that trains the channel predictor from scratch to effectively capture and respond to changes in the wireless environment. The training time includes data collection time and neural network training time, and should be minimized for practical channel predictors. To reduce the training time, especially data collection time, we propose a novel ML-based channel prediction technique called aggregated learning (AL) approach for wideband massive MIMO systems. In the proposed AL approach, the training data can be split and aggregated either in an array domain or frequency domain, which are the channel domains of MIMO-OFDM systems. This processing can significantly reduce the time for data collection. Our numerical results show that the AL approach even improves channel prediction performance in various scenarios with small training time overhead.
信道预测可以补偿多输入多输出（MIMO）系统中过时的信道状态信息。最近，人们利用无线信道的时间相关性，采用机器学习（ML）技术设计信道预测器。然而，大多数基于 ML 的信道预测技术在生成信道预测器时只考虑了离线训练，这可能导致在遇到与训练时不同的信道环境时性能不佳。为了确保在不同信道条件下的预测性能，我们提出了一个在线再训练框架，从头开始训练信道预测器，以有效捕捉和应对无线环境的变化。训练时间包括数据收集时间和神经网络训练时间，对于实用的信道预测器来说，训练时间应最小化。为了减少训练时间，尤其是数据收集时间，我们提出了一种基于 ML 的新型信道预测技术，即针对宽带大规模 MIMO 系统的聚合学习（AL）方法。在所提出的 AL 方法中，训练数据可以在阵列域或频域（即 MIMO-OFDM 系统的信道域）中进行拆分和聚合。这种处理方法可以大大减少数据收集的时间。我们的数值结果表明，AL 方法甚至能在各种情况下提高信道预测性能，而训练时间开销很小。
{"title":"Machine Learning-Based Channel Prediction in Wideband Massive MIMO Systems With Small Overhead for Online Training","authors":"Beomsoo Ko;Hwanjin Kim;Minje Kim;Junil Choi","doi":"10.1109/OJCOMS.2024.3449341","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3449341","url":null,"abstract":"Channel prediction compensates for outdated channel state information in multiple-input multiple-output (MIMO) systems. Machine learning (ML) techniques have recently been implemented to design channel predictors by leveraging the temporal correlation of wireless channels. However, most ML-based channel prediction techniques have only considered offline training when generating channel predictors, which can result in poor performance when encountering channel environments different from the ones they were trained on. To ensure prediction performance in varying channel conditions, we propose an online re-training framework that trains the channel predictor from scratch to effectively capture and respond to changes in the wireless environment. The training time includes data collection time and neural network training time, and should be minimized for practical channel predictors. To reduce the training time, especially data collection time, we propose a novel ML-based channel prediction technique called aggregated learning (AL) approach for wideband massive MIMO systems. In the proposed AL approach, the training data can be split and aggregated either in an array domain or frequency domain, which are the channel domains of MIMO-OFDM systems. This processing can significantly reduce the time for data collection. Our numerical results show that the AL approach even improves channel prediction performance in various scenarios with small training time overhead.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10647106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/OJCOMS.2024.3449701
Vitali Korzhun;Andrea M. Tonello
In-band full-duplex (IBFD) is an attractive technology in broadband power line communication (BB-PLC) because it helps to improve spectral efficiency. However, IBFD is challenging since it requires additional hardware and advanced signal processing to mitigate self-interference (SI) signals. SI cancelation architectures and channel estimation techniques determine the overall IBFD performance. Accurate SI channel estimation is required since imperfect SI cancelation reduces signal-to-interference-plus-noise ratio (SINR), causing an increase in data errors and a decrease in data rates. Although channel estimation can be improved by sending additional training symbols, increasing the training duration will lower data throughput. Thus, the training symbol number is an essential trade-off for IBFD performance in BB-PLC. In this paper, we investigate IBFD performance in single-input single-output (SISO), single-input multiple-output (SIMO), and multiple-input multiple-output (MIMO) communication scenarios, including the influence of the training period. By analyzing error vectors on a constellation diagram, we obtain the closed-form expressions for the symbol error probability (SEP) affected by IBFD and the training duration. Based on the obtained expressions, we propose a bit allocation algorithm to determine bit loading to ensure reliable IBFD communication. Furthermore, we suggest a procedure to compute the optimal training symbol number that maximizes throughput in IBFD. Using the proposed bit allocation strategy and a database of measured channels, we estimated the achievable bidirectional throughput and the throughput gain in IBFD compared to time division duplexing (TDD).
{"title":"Cooperative Bit Allocation in In-Band Full-Duplex Power Line Communication","authors":"Vitali Korzhun;Andrea M. Tonello","doi":"10.1109/OJCOMS.2024.3449701","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3449701","url":null,"abstract":"In-band full-duplex (IBFD) is an attractive technology in broadband power line communication (BB-PLC) because it helps to improve spectral efficiency. However, IBFD is challenging since it requires additional hardware and advanced signal processing to mitigate self-interference (SI) signals. SI cancelation architectures and channel estimation techniques determine the overall IBFD performance. Accurate SI channel estimation is required since imperfect SI cancelation reduces signal-to-interference-plus-noise ratio (SINR), causing an increase in data errors and a decrease in data rates. Although channel estimation can be improved by sending additional training symbols, increasing the training duration will lower data throughput. Thus, the training symbol number is an essential trade-off for IBFD performance in BB-PLC. In this paper, we investigate IBFD performance in single-input single-output (SISO), single-input multiple-output (SIMO), and multiple-input multiple-output (MIMO) communication scenarios, including the influence of the training period. By analyzing error vectors on a constellation diagram, we obtain the closed-form expressions for the symbol error probability (SEP) affected by IBFD and the training duration. Based on the obtained expressions, we propose a bit allocation algorithm to determine bit loading to ensure reliable IBFD communication. Furthermore, we suggest a procedure to compute the optimal training symbol number that maximizes throughput in IBFD. Using the proposed bit allocation strategy and a database of measured channels, we estimated the achievable bidirectional throughput and the throughput gain in IBFD compared to time division duplexing (TDD).","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/OJCOMS.2024.3450348
Mohammad Javad Saber;Mazen Hasna
High-altitude platform (HAP) stations are pivotal in non-terrestrial networks, enhancing communication capabilities and extending cost-effective network access to rural or remote areas. HAP-assisted free-space optical (FSO) communications provide a promising solution for improving data rates. To safeguard against eavesdropping, especially during emergencies, we propose a physical-layer security mechanism to enhance the control signaling resilience in disaster response and network failure detection. We investigate the secrecy performance of an integrated HAP-based FSO and unmanned aerial vehicle (UAV)-enabled radio frequency (RF) downlink system using the decode-and-forward relaying protocol. While optical links inherently provide better security, our focus is on the eavesdropping threats to the RF link. We derive novel and exact analytical and asymptotic closed-form expressions for the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (PSPSC). Our results reveal the significant impact of atmospheric turbulence, RF fading, pointing errors, and optical detection technologies on the overall secrecy performance, providing valuable insights for designing secure mixed FSO and RF downlink communication systems.
{"title":"Security Analysis of Integrated HAP-Based FSO and UAV-Enabled RF Downlink Communications","authors":"Mohammad Javad Saber;Mazen Hasna","doi":"10.1109/OJCOMS.2024.3450348","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3450348","url":null,"abstract":"High-altitude platform (HAP) stations are pivotal in non-terrestrial networks, enhancing communication capabilities and extending cost-effective network access to rural or remote areas. HAP-assisted free-space optical (FSO) communications provide a promising solution for improving data rates. To safeguard against eavesdropping, especially during emergencies, we propose a physical-layer security mechanism to enhance the control signaling resilience in disaster response and network failure detection. We investigate the secrecy performance of an integrated HAP-based FSO and unmanned aerial vehicle (UAV)-enabled radio frequency (RF) downlink system using the decode-and-forward relaying protocol. While optical links inherently provide better security, our focus is on the eavesdropping threats to the RF link. We derive novel and exact analytical and asymptotic closed-form expressions for the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (PSPSC). Our results reveal the significant impact of atmospheric turbulence, RF fading, pointing errors, and optical detection technologies on the overall secrecy performance, providing valuable insights for designing secure mixed FSO and RF downlink communication systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/OJCOMS.2024.3449633
Karel Toledo;Jorge Torres Gómez;Falko Dressler;M. Julia Fernández-Getino García
The Internet of Things (IoT) enables the interconnection of multiple devices, typically sharing network resources. These devices must identify a suitable time to access the channel without interfering with each other, which can lead to additional energy consumption. To extend the network lifetime, cooperative strategies have been proposed that modify the device operations between the ON/OFF states to conserve energy. However, the challenge of selecting active devices for spectrum sensing increases with mobile agents, referred to as Internet of Mobile Things (IoMT), since their positions may be unknown. To deal with this uncertainty, we propose using the ordered weighted averaging (OWA) operator, which provides a framework for decision-making under ignorance, to model the position uncertainty resulting from node movement. We estimate node positions by assigning representative values for their distances to the fusion center and primary user. We then determine the optimal number of active nodes that minimize energy consumption while meeting detection constraints. We evaluate performance for different scenarios in networks of various sizes consistent with smart agriculture environments, employing optimistic and pessimistic approaches. The quality of decisions is validated under the assumption of nodes governed by particular mobility patterns.
{"title":"Energy-Aware Cooperative Spectrum Sensing Under Ignorance on Internet of Mobile Things","authors":"Karel Toledo;Jorge Torres Gómez;Falko Dressler;M. Julia Fernández-Getino García","doi":"10.1109/OJCOMS.2024.3449633","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3449633","url":null,"abstract":"The Internet of Things (IoT) enables the interconnection of multiple devices, typically sharing network resources. These devices must identify a suitable time to access the channel without interfering with each other, which can lead to additional energy consumption. To extend the network lifetime, cooperative strategies have been proposed that modify the device operations between the ON/OFF states to conserve energy. However, the challenge of selecting active devices for spectrum sensing increases with mobile agents, referred to as Internet of Mobile Things (IoMT), since their positions may be unknown. To deal with this uncertainty, we propose using the ordered weighted averaging (OWA) operator, which provides a framework for decision-making under ignorance, to model the position uncertainty resulting from node movement. We estimate node positions by assigning representative values for their distances to the fusion center and primary user. We then determine the optimal number of active nodes that minimize energy consumption while meeting detection constraints. We evaluate performance for different scenarios in networks of various sizes consistent with smart agriculture environments, employing optimistic and pessimistic approaches. The quality of decisions is validated under the assumption of nodes governed by particular mobility patterns.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646367","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/OJCOMS.2024.3450089
Sajjad Hussain;Syed Faraz Naeem Bacha;Adnan Ahmad Cheema;Berk Canberk;Trung Q. Duong
Unmanned aerial vehicles (UAVs) are envisioned to play a pivotal role in modern telecommunication and wireless sensor networks, offering unparalleled flexibility and mobility for communication and data collection in diverse environments. This paper presents a comprehensive investigation into the performance of supervised machine learning (ML) models for path loss (PL) prediction in UAV-assisted millimeter-wave (mmWave) radio networks. Leveraging a unique set of interpretable geometrical features, six distinct ML models–linear regression (LR), support vector regressor (SVR), K nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN)–are rigorously evaluated using a massive dataset generated from extensive raytracing (RT) simulations in a typical urban environment. Our results demonstrate that the RF algorithm outperforms other models showcasing superior predictive performance for the test dataset with a root mean square error (RMSE) of 2.38 dB. The proposed ML models demonstrate superior accuracy compared to 3GPP and ITU-R models for mmWave radio networks. This study thoroughly investigates the adaptability of these models to unseen environments and examines the feasibility of training them with sparse datasets to improve accuracy. The reduction in computation time achieved by using ML models instead of extensive RT computations for sparse training datasets is evaluated, and an efficient algorithm for training such models is proposed. Additionally, the sensitivity of ML models to noisy input features is analyzed. We also assess the importance of geometrical features and the impact of sequentially increasing the number of these features on model performance. The results emphasize the significance of the proposed geometrical features and demonstrate the potential of ML models to provide computationally efficient and relatively accurate PL predictions in diverse urban environments.
无人飞行器（UAVs）在现代电信和无线传感器网络中发挥着举足轻重的作用，可在各种环境中为通信和数据收集提供无与伦比的灵活性和机动性。本文对无人机辅助毫米波（mmWave）无线电网络中用于路径损耗（PL）预测的有监督机器学习（ML）模型的性能进行了全面研究。利用一组独特的可解释几何特征，使用典型城市环境中大量光线跟踪（RT）模拟生成的海量数据集，对线性回归（LR）、支持向量回归器（SVR）、K 近邻（KNN）、随机森林（RF）、极梯度提升（XGBoost）和深度神经网络（DNN）六种不同的 ML 模型进行了严格评估。结果表明，射频算法优于其他模型，在测试数据集上显示出卓越的预测性能，均方根误差 (RMSE) 为 2.38 dB。与 3GPP 和 ITU-R 模型相比，所提出的 ML 模型在毫米波无线网络方面表现出更高的准确性。这项研究深入探讨了这些模型对未知环境的适应性，并研究了用稀疏数据集训练这些模型以提高准确性的可行性。研究评估了使用 ML 模型代替大量 RT 计算稀疏训练数据集所减少的计算时间，并提出了训练此类模型的高效算法。此外，我们还分析了 ML 模型对噪声输入特征的敏感性。我们还评估了几何特征的重要性以及依次增加这些特征的数量对模型性能的影响。结果强调了所提出的几何特征的重要性，并证明了 ML 模型在不同城市环境中提供计算效率高且相对准确的 PL 预测的潜力。
{"title":"Geometrical Features Based-mmWave UAV Path Loss Prediction Using Machine Learning for 5G and Beyond","authors":"Sajjad Hussain;Syed Faraz Naeem Bacha;Adnan Ahmad Cheema;Berk Canberk;Trung Q. Duong","doi":"10.1109/OJCOMS.2024.3450089","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3450089","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are envisioned to play a pivotal role in modern telecommunication and wireless sensor networks, offering unparalleled flexibility and mobility for communication and data collection in diverse environments. This paper presents a comprehensive investigation into the performance of supervised machine learning (ML) models for path loss (PL) prediction in UAV-assisted millimeter-wave (mmWave) radio networks. Leveraging a unique set of interpretable geometrical features, six distinct ML models–linear regression (LR), support vector regressor (SVR), K nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN)–are rigorously evaluated using a massive dataset generated from extensive raytracing (RT) simulations in a typical urban environment. Our results demonstrate that the RF algorithm outperforms other models showcasing superior predictive performance for the test dataset with a root mean square error (RMSE) of 2.38 dB. The proposed ML models demonstrate superior accuracy compared to 3GPP and ITU-R models for mmWave radio networks. This study thoroughly investigates the adaptability of these models to unseen environments and examines the feasibility of training them with sparse datasets to improve accuracy. The reduction in computation time achieved by using ML models instead of extensive RT computations for sparse training datasets is evaluated, and an efficient algorithm for training such models is proposed. Additionally, the sensitivity of ML models to noisy input features is analyzed. We also assess the importance of geometrical features and the impact of sequentially increasing the number of these features on model performance. The results emphasize the significance of the proposed geometrical features and demonstrate the potential of ML models to provide computationally efficient and relatively accurate PL predictions in diverse urban environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/ojcoms.2024.3449563
Afroditi Blika, Stefanos Palmos, George Doukas, Vangelis Lamprou, Sotiris Pelekis, Michael Kontoulis, Christos Ntanos, Dimitris Askounis
{"title":"Federated Learning For Enhanced Cybersecurity And Trustworthiness In 5G and 6G Networks: A Comprehensive Survey","authors":"Afroditi Blika, Stefanos Palmos, George Doukas, Vangelis Lamprou, Sotiris Pelekis, Michael Kontoulis, Christos Ntanos, Dimitris Askounis","doi":"10.1109/ojcoms.2024.3449563","DOIUrl":"https://doi.org/10.1109/ojcoms.2024.3449563","url":null,"abstract":"","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209473","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 : 2024-08-26DOI: 10.1109/ojcoms.2024.3449692
Engin Zeydan, Luis Blanco, Josep Mangues-Bafalluy, Suayb S. Arslan, Yekta Turk, Awaneesh Kumar Yadav, Madhusanka Liyanage
{"title":"Blockchain-Based Self-Sovereign Identity: Taking Control of Identity in Federated Learning","authors":"Engin Zeydan, Luis Blanco, Josep Mangues-Bafalluy, Suayb S. Arslan, Yekta Turk, Awaneesh Kumar Yadav, Madhusanka Liyanage","doi":"10.1109/ojcoms.2024.3449692","DOIUrl":"https://doi.org/10.1109/ojcoms.2024.3449692","url":null,"abstract":"","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209471","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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