Pub Date : 2024-12-23DOI: 10.1109/OJCOMS.2024.3521651
Anis Elgabli;Wessam Mesbah
In this paper, we start with a comprehensive evaluation of the effect of adding differential privacy (DP) to federated learning (FL) approaches, focusing on methodologies employing global (stochastic) gradient descent (SGD/GD), and local SGD/GD techniques. These global and local techniques are commonly referred to as FedSGD/FedGD and FedAvg, respectively. Our analysis reveals that, as far as only one local iteration is performed by each client before transmitting to the parameter server (PS) for FedGD, both FedGD and FedAvg achieve the same accuracy/loss for the same privacy guarantees, despite requiring different perturbation noise power. Furthermore, we propose a novel DP mechanism, which is shown to ensure privacy without compromising performance. In particular, we propose the sharing of a random seed (or a specified sequence of random seeds) among collaborative clients, where each client uses this seed to introduces perturbations to its updates prior to transmission to the PS. Importantly, due to the random seed sharing, clients possess the capability to negate the noise effects and recover their original global model. This mechanism preserves privacy both at a “curious” PS or at external eavesdroppers without compromising the performance of the final model at each client, thus mitigating the risk of inversion attacks aimed at retrieving (partially or fully) the clients’ data. Furthermore, the importance and effect of clipping in the practical implementation of DP mechanisms, in order to upper bound the perturbation noise, is discussed. Moreover, owing to the ability to cancel noise at individual clients, our proposed approach enables the introduction of arbitrarily high perturbation levels, and hence, clipping can be totally avoided, resulting in the same performance of noise-free standard FL approaches.
{"title":"A Novel Approach for Differential Privacy-Preserving Federated Learning","authors":"Anis Elgabli;Wessam Mesbah","doi":"10.1109/OJCOMS.2024.3521651","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3521651","url":null,"abstract":"In this paper, we start with a comprehensive evaluation of the effect of adding differential privacy (DP) to federated learning (FL) approaches, focusing on methodologies employing global (stochastic) gradient descent (SGD/GD), and local SGD/GD techniques. These global and local techniques are commonly referred to as FedSGD/FedGD and FedAvg, respectively. Our analysis reveals that, as far as only one local iteration is performed by each client before transmitting to the parameter server (PS) for FedGD, both FedGD and FedAvg achieve the same accuracy/loss for the same privacy guarantees, despite requiring different perturbation noise power. Furthermore, we propose a novel DP mechanism, which is shown to ensure privacy without compromising performance. In particular, we propose the sharing of a random seed (or a specified sequence of random seeds) among collaborative clients, where each client uses this seed to introduces perturbations to its updates prior to transmission to the PS. Importantly, due to the random seed sharing, clients possess the capability to negate the noise effects and recover their original global model. This mechanism preserves privacy both at a “curious” PS or at external eavesdroppers without compromising the performance of the final model at each client, thus mitigating the risk of inversion attacks aimed at retrieving (partially or fully) the clients’ data. Furthermore, the importance and effect of clipping in the practical implementation of DP mechanisms, in order to upper bound the perturbation noise, is discussed. Moreover, owing to the ability to cancel noise at individual clients, our proposed approach enables the introduction of arbitrarily high perturbation levels, and hence, clipping can be totally avoided, resulting in the same performance of noise-free standard FL approaches.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"466-476"},"PeriodicalIF":6.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10812948","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938173","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 : 2024-12-23DOI: 10.1109/OJCOMS.2024.3521940
Wessam Mesbah
Differential privacy (DP) has been widely used in communication systems, especially those using federated learning or distributed computing. DP comes in the data preparation stage before line coding and transmission. In contrast to the literature where differential privacy is mainly discussed from the point of view of data/computer science, in this paper we approach DP from a perspective that provides a better understanding to the communications engineering community. From this perspective, we show the contrast between the MAP detection problem in communications and the DP problem. In this paper, we consider two DP mechanisms, namely, the Gaussian Mechanism (GM) and the Laplacian Mechanism (LM). We explain why the definition of $epsilon$ -DP is associated with the LM, while we must resort to the definition of ( $epsilon, delta$ )-DP if the GM is used. Furthermore, we derive a new lower bound on the perturbation noise required for the GM to guarantee ( $epsilon, delta$ )-DP. Although no closed form is obtained for the new lower bound, a very simple one dimensional search algorithm can be used to achieve the lowest possible noise variance. Since the perturbation noise is known to negatively affect the performance of the data analysis (such as the convergence in federated learning), the new lower bound on the perturbation noise is expected to improve the performance over the classical GM. Moreover, we derive the perturbation noise required for both the LM and the GM in case of the adversary having auxiliary information in the form of the prior probabilities of the different databases. We show that the availability of auxiliary information at the adversary, is equivalent to reducing the tolerable privacy leakage, and hence it requires more perturbation noise. Finally, we analytically derive the border between the region where GM is better to use and the region where LM is better to use.
{"title":"DP With Auxiliary Information: Gaussian Mechanism Versus Laplacian Mechanism","authors":"Wessam Mesbah","doi":"10.1109/OJCOMS.2024.3521940","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3521940","url":null,"abstract":"Differential privacy (DP) has been widely used in communication systems, especially those using federated learning or distributed computing. DP comes in the data preparation stage before line coding and transmission. In contrast to the literature where differential privacy is mainly discussed from the point of view of data/computer science, in this paper we approach DP from a perspective that provides a better understanding to the communications engineering community. From this perspective, we show the contrast between the MAP detection problem in communications and the DP problem. In this paper, we consider two DP mechanisms, namely, the Gaussian Mechanism (GM) and the Laplacian Mechanism (LM). We explain why the definition of \u0000<inline-formula> <tex-math>$epsilon$ </tex-math></inline-formula>\u0000-DP is associated with the LM, while we must resort to the definition of (\u0000<inline-formula> <tex-math>$epsilon, delta$ </tex-math></inline-formula>\u0000)-DP if the GM is used. Furthermore, we derive a new lower bound on the perturbation noise required for the GM to guarantee (\u0000<inline-formula> <tex-math>$epsilon, delta$ </tex-math></inline-formula>\u0000)-DP. Although no closed form is obtained for the new lower bound, a very simple one dimensional search algorithm can be used to achieve the lowest possible noise variance. Since the perturbation noise is known to negatively affect the performance of the data analysis (such as the convergence in federated learning), the new lower bound on the perturbation noise is expected to improve the performance over the classical GM. Moreover, we derive the perturbation noise required for both the LM and the GM in case of the adversary having auxiliary information in the form of the prior probabilities of the different databases. We show that the availability of auxiliary information at the adversary, is equivalent to reducing the tolerable privacy leakage, and hence it requires more perturbation noise. Finally, we analytically derive the border between the region where GM is better to use and the region where LM is better to use.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"143-153"},"PeriodicalIF":6.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10813007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938106","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 : 2024-12-23DOI: 10.1109/OJCOMS.2024.3522189
Deeb Assad Tubail;Mohammed Zourob;Salama Ikki
This work aims to enhance and generalize the joint position-velocity tracking process in millimeter wave (mmWave) systems that suffer from hardware impairments (HWIs), all while considering computational complexity. Initially, we investigate the performance of two widely used traditional trackers: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). Through this investigation, we identify the strengths and limitations of these trackers. Besides, we evaluate the gap between traditional tracking performance and the theoretical optimum by deriving the Bayesian Cramér-Rao Bound (BCRB) as a benchmark. Our findings reveal a significant disparity between the performance of traditional trackers and the benchmark, with performance being influenced by noise characteristics, initial conditions, and the accuracy of prior knowledge about the transition model. To address these challenges, we propose a neural network (NN)-based approach to achieve accurate and generalized tracking without relying on prior knowledge of the transition model, initial conditions, or noise characteristics. Specifically, our method trains a NN that performs effectively under any noise conditions, without needing to recognize the transition model or initial state. To manage the computational demands of the training phase, we employ a low-complexity algorithm, the Extreme Learning Machine (ELM), which calculates weights and biases through closed-form solution, avoiding complex optimization processes. Finally, we validate the accuracy and generality of the ELM tracker through computer simulations, testing it under various scenarios, including Gaussian and non-Gaussian HWI distortions, as well as systems with known transition models and those involving uncharacterized inputs.
{"title":"Enhancing and Generalizing Position-Velocity Tracking in Imperfect mmWave Systems Using a Low-Complexity Neural Network","authors":"Deeb Assad Tubail;Mohammed Zourob;Salama Ikki","doi":"10.1109/OJCOMS.2024.3522189","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3522189","url":null,"abstract":"This work aims to enhance and generalize the joint position-velocity tracking process in millimeter wave (mmWave) systems that suffer from hardware impairments (HWIs), all while considering computational complexity. Initially, we investigate the performance of two widely used traditional trackers: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). Through this investigation, we identify the strengths and limitations of these trackers. Besides, we evaluate the gap between traditional tracking performance and the theoretical optimum by deriving the Bayesian Cramér-Rao Bound (BCRB) as a benchmark. Our findings reveal a significant disparity between the performance of traditional trackers and the benchmark, with performance being influenced by noise characteristics, initial conditions, and the accuracy of prior knowledge about the transition model. To address these challenges, we propose a neural network (NN)-based approach to achieve accurate and generalized tracking without relying on prior knowledge of the transition model, initial conditions, or noise characteristics. Specifically, our method trains a NN that performs effectively under any noise conditions, without needing to recognize the transition model or initial state. To manage the computational demands of the training phase, we employ a low-complexity algorithm, the Extreme Learning Machine (ELM), which calculates weights and biases through closed-form solution, avoiding complex optimization processes. Finally, we validate the accuracy and generality of the ELM tracker through computer simulations, testing it under various scenarios, including Gaussian and non-Gaussian HWI distortions, as well as systems with known transition models and those involving uncharacterized inputs.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"236-251"},"PeriodicalIF":6.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10812951","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938346","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 : 2024-12-19DOI: 10.1109/OJCOMS.2024.3520822
Xiangyu Cui;Ki-Hong Park;Mohamed Slim Alouini
With the fast development of communication technology, mobile networks have been evolving from the fifth generation (5G) to the sixth generation (6G). One of the most important technologies in 5G is massive multiple input multiple output (MIMO). In 6G, it has been extended to extremely large-scale MIMO (XL-MIMO) over the TeraHz band, which makes it easier for users to fall into the near-field communication range. However, the previous performance analysis based on the far-field assumption can be very inaccurate under the near-field scenario. Hence, it is necessary to use the near-field channel models to redo these analyses. In this work, we summarize previous analytical results on received signal-to-noise ratio for specific near-field wave models. Then, we derive the generalized formula for the received power of different wave models and antenna structures. We newly derive our closed-form formula for the correlation between different users by the stationary phase method. These results can be applied to different beam-forming schemes and the multipath case. Based on these analytical results, we manage to make a sum rate analysis for different antenna arrays and near-field channel models in a multi-user XL-MIMO system. Finally, with the modification by our analytical result, we show a dramatic speed-up of the previous user selection algorithm, while reaching the same sum rate.
{"title":"Near-Field Analysis of Extremely Large-Scale MIMO: Power, Correlation, and User Selection","authors":"Xiangyu Cui;Ki-Hong Park;Mohamed Slim Alouini","doi":"10.1109/OJCOMS.2024.3520822","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3520822","url":null,"abstract":"With the fast development of communication technology, mobile networks have been evolving from the fifth generation (5G) to the sixth generation (6G). One of the most important technologies in 5G is massive multiple input multiple output (MIMO). In 6G, it has been extended to extremely large-scale MIMO (XL-MIMO) over the TeraHz band, which makes it easier for users to fall into the near-field communication range. However, the previous performance analysis based on the far-field assumption can be very inaccurate under the near-field scenario. Hence, it is necessary to use the near-field channel models to redo these analyses. In this work, we summarize previous analytical results on received signal-to-noise ratio for specific near-field wave models. Then, we derive the generalized formula for the received power of different wave models and antenna structures. We newly derive our closed-form formula for the correlation between different users by the stationary phase method. These results can be applied to different beam-forming schemes and the multipath case. Based on these analytical results, we manage to make a sum rate analysis for different antenna arrays and near-field channel models in a multi-user XL-MIMO system. Finally, with the modification by our analytical result, we show a dramatic speed-up of the previous user selection algorithm, while reaching the same sum rate.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"252-270"},"PeriodicalIF":6.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10810362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938347","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 : 2024-12-19DOI: 10.1109/OJCOMS.2024.3520234
Soheil Hosseini;Ignacio De Miguel;Noemí Merayo;Óscar González De Dios;Rubén M. Lorenzo;Ramón J. Durán Barroso
Band division multiplexing (BDM) is a promising technology to upgrade the capacity of elastic optical networks (EON). By using both C and L bands instead of just the C-band, networks can support a larger number of connections. This paper presents and analyzes a set of novel strategies to ensure resiliency in multiband optical networks leveraging the unique characteristics of these networks and their amplifiers. We investigate two new protection methods for these networks, erbium-doped fiber amplifier (EDFA) protection and hybrid protection, and compare them with two established protection techniques, dedicated path protection (DPP) and shared backup path protection (SBPP). Recognizing that protection requirements vary among services, we also propose a service level agreement (SLA) differentiated protection mechanism that combines these strategies. High-priority clients receive robust protection against single failures of fibers or EDFAs through DPP or SBPP, while low-priority clients are safeguarded at least against EDFA failures via EDFA or hybrid protection. Our studies explore the most effective combinations of these techniques across various scenarios, demonstrating the advantages of SLA-differentiated protection over the utilization of a single classical protection method for all clients. Furthermore, we propose and analyze a more flexible protection mechanism that allows clients to divide their traffic into two segments: one requiring a high level of protection and the other a lower level. This flexibility enhances the protection options available, enabling a finer categorization of user needs and making it an attractive choice for network operators.
{"title":"Enabling Service Level Agreement-Differentiated Protection in C+L Multiband Optical Networks","authors":"Soheil Hosseini;Ignacio De Miguel;Noemí Merayo;Óscar González De Dios;Rubén M. Lorenzo;Ramón J. Durán Barroso","doi":"10.1109/OJCOMS.2024.3520234","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3520234","url":null,"abstract":"Band division multiplexing (BDM) is a promising technology to upgrade the capacity of elastic optical networks (EON). By using both C and L bands instead of just the C-band, networks can support a larger number of connections. This paper presents and analyzes a set of novel strategies to ensure resiliency in multiband optical networks leveraging the unique characteristics of these networks and their amplifiers. We investigate two new protection methods for these networks, erbium-doped fiber amplifier (EDFA) protection and hybrid protection, and compare them with two established protection techniques, dedicated path protection (DPP) and shared backup path protection (SBPP). Recognizing that protection requirements vary among services, we also propose a service level agreement (SLA) differentiated protection mechanism that combines these strategies. High-priority clients receive robust protection against single failures of fibers or EDFAs through DPP or SBPP, while low-priority clients are safeguarded at least against EDFA failures via EDFA or hybrid protection. Our studies explore the most effective combinations of these techniques across various scenarios, demonstrating the advantages of SLA-differentiated protection over the utilization of a single classical protection method for all clients. Furthermore, we propose and analyze a more flexible protection mechanism that allows clients to divide their traffic into two segments: one requiring a high level of protection and the other a lower level. This flexibility enhances the protection options available, enabling a finer categorization of user needs and making it an attractive choice for network operators.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"316-331"},"PeriodicalIF":6.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10807265","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938354","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 : 2024-12-19DOI: 10.1109/OJCOMS.2024.3520636
Minseung Park;Minsu Choi;Jong-Moon Chung
Non-terrestrial networks (NTNs) that support communication via unmanned aerial vehicles (UAVs) in 6G systems are attracting considerable interest. To meet the quality of service (QoS) requirements for future 6G devices, the backhaul network of NTNs must provide higher throughput and more secure wireless connectivity. It is anticipated that backhaul NTNs with high throughput free space optical (FSO) communication will play a significant role in supporting multiple users. However, establishing a stable FSO communication link using UAVs can be disrupted by various obstacles, including clouds and atmospheric conditions. To address this challenge, it is essential to consider the effect of clouds on the FSO backhaul network. In this paper, the method for optimizing the deployment of multiple UAVs considering the probability of cloud attenuation is proposed. Specifically, this paper presents a cloud-aware dynamic UAV operation (CDUO) scheme, which contains an optimization process using the generalized Lagrange multiplier method (GLMM). Simulation results (based on blimp-type unmanned aerial stations (UASs)) show that the CDUO scheme can provide a superior outage probability performance compared to existing schemes. The CDUO scheme can be utilized in NTNs that operate above the clouds.
{"title":"Cloud Aware Dynamic UAV Operation for 6G FSO Backhaul Network Performance Improvement","authors":"Minseung Park;Minsu Choi;Jong-Moon Chung","doi":"10.1109/OJCOMS.2024.3520636","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3520636","url":null,"abstract":"Non-terrestrial networks (NTNs) that support communication via unmanned aerial vehicles (UAVs) in 6G systems are attracting considerable interest. To meet the quality of service (QoS) requirements for future 6G devices, the backhaul network of NTNs must provide higher throughput and more secure wireless connectivity. It is anticipated that backhaul NTNs with high throughput free space optical (FSO) communication will play a significant role in supporting multiple users. However, establishing a stable FSO communication link using UAVs can be disrupted by various obstacles, including clouds and atmospheric conditions. To address this challenge, it is essential to consider the effect of clouds on the FSO backhaul network. In this paper, the method for optimizing the deployment of multiple UAVs considering the probability of cloud attenuation is proposed. Specifically, this paper presents a cloud-aware dynamic UAV operation (CDUO) scheme, which contains an optimization process using the generalized Lagrange multiplier method (GLMM). Simulation results (based on blimp-type unmanned aerial stations (UASs)) show that the CDUO scheme can provide a superior outage probability performance compared to existing schemes. The CDUO scheme can be utilized in NTNs that operate above the clouds.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"130-142"},"PeriodicalIF":6.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10810353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938105","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}
The programmability of radio access networks was already introduced in the fifth generation of mobile networks by leading the pathway towards the Open RAN. To cater the stringent requirements emerging with the novel services and use cases, Open RAN is becoming integral to 6G as well. The most highlighted characteristics of Open RAN such as disaggregated architecture, cloudification of network functions, open interfaces, and intelligent network management are also associated with many challenges related to the security of the end-to-end telco networks. Most existing research works and specifications focus on security in Open RAN in 5G. In this paper, we discuss the role of Open RAN in 6G focusing on three aspects such as security, privacy, and trust, while highlighting their emerging trends and relevance to 6G. Our contributions include presenting a holistic view of Open RAN security within the 6G ecosystem, with insights into use case and stakeholder perspectives and standardization efforts. This includes a concise update on Open RAN security in the 5G era and their applicability to 6G exploration. Moreover, we identify the key research endeavors focused on privacy and trust in Open RAN, with an emphasis on their development toward 6G. Finally, we discuss the possibilities of incorporating energy awareness, quantum-safe solutions, and other emerging technological enablers with Open RAN security.
{"title":"Security, Privacy, and Trust for Open Radio Access Networks in 6G","authors":"Pawani Porambage;Maria Christopoulou;Bin Han;Mohammad Asif Habibi;Hanna Bogucka;Pawel Kryszkiewicz","doi":"10.1109/OJCOMS.2024.3519725","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3519725","url":null,"abstract":"The programmability of radio access networks was already introduced in the fifth generation of mobile networks by leading the pathway towards the Open RAN. To cater the stringent requirements emerging with the novel services and use cases, Open RAN is becoming integral to 6G as well. The most highlighted characteristics of Open RAN such as disaggregated architecture, cloudification of network functions, open interfaces, and intelligent network management are also associated with many challenges related to the security of the end-to-end telco networks. Most existing research works and specifications focus on security in Open RAN in 5G. In this paper, we discuss the role of Open RAN in 6G focusing on three aspects such as security, privacy, and trust, while highlighting their emerging trends and relevance to 6G. Our contributions include presenting a holistic view of Open RAN security within the 6G ecosystem, with insights into use case and stakeholder perspectives and standardization efforts. This includes a concise update on Open RAN security in the 5G era and their applicability to 6G exploration. Moreover, we identify the key research endeavors focused on privacy and trust in Open RAN, with an emphasis on their development toward 6G. Finally, we discuss the possibilities of incorporating energy awareness, quantum-safe solutions, and other emerging technological enablers with Open RAN security.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"332-361"},"PeriodicalIF":6.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10807044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938355","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 : 2024-12-18DOI: 10.1109/OJCOMS.2024.3520005
Shehu Lukman Ayinla;Azrina Abd Aziz;Micheal Drieberg;Misfa Susanto;Afidalina Tumian;Mazlaini Yahya
WiFi fingerprinting based on the Received Signal Strength Indicator (RSSI) is a widely used technique for indoor localization. However, achieving the necessary precision for most smart applications has proven difficult due to various challenges, including the time-varying characteristics of indoor RSSI, device heterogeneity, and ambiguity in the positional fingerprints. To address these concerns, Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL) have been used to classify and predict indoor locations. While these techniques have shown promising results, they face two significant challenges. First, their complex multilayer architecture requires extensive training iterations. Second, the distribution of the input layer fluctuates as the network’s parameters are updated. In this study, we propose a WiFi indoor localization framework based on Recursive Feature Elimination with Cross-Validation (RFECV) and Deep Neural Network with Batch Normalization (DNNBN). The CV process is repeated multiple times to assess the RFE’s generalization ability for optimal feature selection, and the BN algorithm is integrated into each layer of the DNN to ensure consistent activation value distribution and stabilization of the training process. Two datasets were used to assess the performance of the proposed RFECV-DNNBN method. The results show that the method addresses the challenges faced by existing AI techniques and outperforms current methods in classification and regression tasks. Our proposed framework achieved 100% accuracy in building classification and 94.69% and 96.39% in floor classification on the UJIIndoorLoc and UTSIndoorLoc datasets, respectively, demonstrating its effectiveness. Additionally, it achieved a Mean Absolute Error (MAE) of 5.08 m and 4.29 m for the respective datasets, highlighting its potential in multi-floored environments.
{"title":"An Enhanced Deep Neural Network Approach for WiFi Fingerprinting-Based Multi-Floor Indoor Localization","authors":"Shehu Lukman Ayinla;Azrina Abd Aziz;Micheal Drieberg;Misfa Susanto;Afidalina Tumian;Mazlaini Yahya","doi":"10.1109/OJCOMS.2024.3520005","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3520005","url":null,"abstract":"WiFi fingerprinting based on the Received Signal Strength Indicator (RSSI) is a widely used technique for indoor localization. However, achieving the necessary precision for most smart applications has proven difficult due to various challenges, including the time-varying characteristics of indoor RSSI, device heterogeneity, and ambiguity in the positional fingerprints. To address these concerns, Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL) have been used to classify and predict indoor locations. While these techniques have shown promising results, they face two significant challenges. First, their complex multilayer architecture requires extensive training iterations. Second, the distribution of the input layer fluctuates as the network’s parameters are updated. In this study, we propose a WiFi indoor localization framework based on Recursive Feature Elimination with Cross-Validation (RFECV) and Deep Neural Network with Batch Normalization (DNNBN). The CV process is repeated multiple times to assess the RFE’s generalization ability for optimal feature selection, and the BN algorithm is integrated into each layer of the DNN to ensure consistent activation value distribution and stabilization of the training process. Two datasets were used to assess the performance of the proposed RFECV-DNNBN method. The results show that the method addresses the challenges faced by existing AI techniques and outperforms current methods in classification and regression tasks. Our proposed framework achieved 100% accuracy in building classification and 94.69% and 96.39% in floor classification on the UJIIndoorLoc and UTSIndoorLoc datasets, respectively, demonstrating its effectiveness. Additionally, it achieved a Mean Absolute Error (MAE) of 5.08 m and 4.29 m for the respective datasets, highlighting its potential in multi-floored environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"560-575"},"PeriodicalIF":6.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10806782","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993668","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 : 2024-12-17DOI: 10.1109/OJCOMS.2024.3519314
Ahmet Enes Duranay;Xhelja Kodheli;Amr M. Abdelhady;Abdulkadir Celik;Ahmed M. Eltawil;Hüseyin Arslan
Post-earthquake scenarios have brought connectivity challenges to the forefront of research in recent years. Particularly, the randomness and large-scale of road and telecom network infrastructure damage within the aftermath hinders communications coverage restoration during the most critical hours when lives are at stake. This paper proposes a seismic-based post-earthquake city and cellular network model to statistically predict the status of road closures and base station failures based on fundamental earthquake measurements. The presented model considers a generic Manhattan grid-based city model, with buildings featuring random heights. In addition, it quantifies the probability of building collapse and the consequent probability of road closure which accounts for the random debris nature. Moreover, the model accounts for the dependencies between the debris width, height, and the relative location with respect to the earthquake epicenter. Furthermore, a routing algorithm for movable and deployable resource units (MDRUs) that exploits the derived statistical model is proposed to ensure that MDRUs are efficiently deployed and connectivity is restored swiftly. The proposed routing algorithm is extensively tested over a large set of simulation scenarios depicting different earthquake magnitudes and was shown to provide up to 31% traveling time reduction compared to a blind distance-based approach. Finally, the conducted simulations showed the effectiveness of the proposed MDRUs deployment approach in restoring the communications coverage from a signal-to-interference plus noise ratio perspective in the majority of the considered locations.
{"title":"Post-Earthquake Network Restoration: Statistical Seismic Road Closure Prediction and Efficient MDRU Routing","authors":"Ahmet Enes Duranay;Xhelja Kodheli;Amr M. Abdelhady;Abdulkadir Celik;Ahmed M. Eltawil;Hüseyin Arslan","doi":"10.1109/OJCOMS.2024.3519314","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3519314","url":null,"abstract":"Post-earthquake scenarios have brought connectivity challenges to the forefront of research in recent years. Particularly, the randomness and large-scale of road and telecom network infrastructure damage within the aftermath hinders communications coverage restoration during the most critical hours when lives are at stake. This paper proposes a seismic-based post-earthquake city and cellular network model to statistically predict the status of road closures and base station failures based on fundamental earthquake measurements. The presented model considers a generic Manhattan grid-based city model, with buildings featuring random heights. In addition, it quantifies the probability of building collapse and the consequent probability of road closure which accounts for the random debris nature. Moreover, the model accounts for the dependencies between the debris width, height, and the relative location with respect to the earthquake epicenter. Furthermore, a routing algorithm for movable and deployable resource units (MDRUs) that exploits the derived statistical model is proposed to ensure that MDRUs are efficiently deployed and connectivity is restored swiftly. The proposed routing algorithm is extensively tested over a large set of simulation scenarios depicting different earthquake magnitudes and was shown to provide up to 31% traveling time reduction compared to a blind distance-based approach. Finally, the conducted simulations showed the effectiveness of the proposed MDRUs deployment approach in restoring the communications coverage from a signal-to-interference plus noise ratio perspective in the majority of the considered locations.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"671-689"},"PeriodicalIF":6.3,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10804642","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993670","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 : 2024-12-13DOI: 10.1109/OJCOMS.2024.3516763
Hussein Hammoud;Zihang Cheng;Jorge Gomez-Ponce;Seun Sangodoyin;Jason Kahn;Andreas F. Molisch
Recent interest in Device-to-Device (D2D) communication systems, particularly by Public Safety Organizations (PSOs), arises from scenarios where no infrastructure is available. One notable scenario is Indoor-to-Outdoor (I2O), where emergency responders inside buildings communicate with command posts on the street. We aim to understand the propagation channel for designing wireless systems in such contexts. We report findings from a comprehensive measurement campaign in the public safety frequency band near 800 MHz, assessing multiple-input-multiple-output (MIMO) channels between users across five floors of a California office building and a multi-antenna base station at street level. This study provides insights as well as statistical channel models for pathgain, delay spread, angular spreads, and power distribution among Multi-Path Components (MPCs) and their dependence on indoor-user height. In Line-of-Sight (LoS) scenarios, 10 dB pathgain variation was observed between ground and the 5th floor, while Non-Line-Of-Sight (NLoS) cases showed no such variation. Delay spreads exhibited a similar trend in LoS scenarios, with a 10 ns variation based on RX height, while NLoS scenarios remained heightindependent. Angular spreads were consistently large for indoor units, regardless of height. Analysis of the probability density function of the MPC powers using a 5D κ parameter revealed approximately 8 dB for LoS scenarios and −1 dB for NLoS scenarios, assuming beamforming for maximum received power.
{"title":"Propagation Channel Measurements for Indoor-to-Outdoor Communications for Device-to-Device Public Safety Applications","authors":"Hussein Hammoud;Zihang Cheng;Jorge Gomez-Ponce;Seun Sangodoyin;Jason Kahn;Andreas F. Molisch","doi":"10.1109/OJCOMS.2024.3516763","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3516763","url":null,"abstract":"Recent interest in Device-to-Device (D2D) communication systems, particularly by Public Safety Organizations (PSOs), arises from scenarios where no infrastructure is available. One notable scenario is Indoor-to-Outdoor (I2O), where emergency responders inside buildings communicate with command posts on the street. We aim to understand the propagation channel for designing wireless systems in such contexts. We report findings from a comprehensive measurement campaign in the public safety frequency band near 800 MHz, assessing multiple-input-multiple-output (MIMO) channels between users across five floors of a California office building and a multi-antenna base station at street level. This study provides insights as well as statistical channel models for pathgain, delay spread, angular spreads, and power distribution among Multi-Path Components (MPCs) and their dependence on indoor-user height. In Line-of-Sight (LoS) scenarios, 10 dB pathgain variation was observed between ground and the 5th floor, while Non-Line-Of-Sight (NLoS) cases showed no such variation. Delay spreads exhibited a similar trend in LoS scenarios, with a 10 ns variation based on RX height, while NLoS scenarios remained heightindependent. Angular spreads were consistently large for indoor units, regardless of height. Analysis of the probability density function of the MPC powers using a 5D κ parameter revealed approximately 8 dB for LoS scenarios and −1 dB for NLoS scenarios, assuming beamforming for maximum received power.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"154-170"},"PeriodicalIF":6.3,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10798454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938107","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}
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