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Gross, James, ProfessorORCID iD iconorcid.org/0000-0001-6682-6559
Publications (10 of 204) Show all publications
Islam, T. U., Gross, J., Zhang, H. & et al., . (2025). Design and implementation of ARA wireless living lab for rural broadband and applications. Computer Networks, 263, Article ID 111188.
Open this publication in new window or tab >>Design and implementation of ARA wireless living lab for rural broadband and applications
2025 (English)In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 263, article id 111188Article in journal (Refereed) Published
Abstract [en]

Addressing the broadband gap between rural and urban regions requires rural-focused wireless research and innovation. In the meantime, rural regions provide rich, diverse use cases of advanced wireless, and they offer unique real-world settings for piloting applications that advance the frontiers of wireless systems (e.g., teleoperation of ground and aerial vehicles). To fill the broadband gap and to leverage the unique opportunities that rural regions provide for piloting advanced wireless applications, we design and implement the ARA wireless living lab for research and innovation in rural wireless systems and their applications in precision agriculture, community services, and so on. ARA focuses on the unique community, application, and economic context of rural regions, and it features the first-of-its-kind, real-world deployment of long-distance, high-capacity terrestrial wireless x-haul and access platforms as well as Low-Earth-Orbit (LEO) satellite communications platforms across a rural area of diameter over 30 km. The high-capacity x-haul platforms operate at the 11 GHz, 14 GHz, 71–86 GHz, and 194 THz bands and offer communication capacities of up to 160 Gbps at per-hop distances up to 15+ km. The wireless access platforms feature 5G-and-beyond MIMO systems operating at the 460–776 MHz, 3.4–3.6 GHz, and 27.5–28.35 GHz bands and with 14, 192, and 384 antenna elements per sector respectively, and they offer up to 3+ Gbps wireless access throughput and up to 10+ km effective cell radius. With both software-defined radios and programmable COTS systems, and through effective orchestration of these wireless resources with fiber as well as compute resources embedded end-to-end across User Equipment (UE), Base Stations (BS), edge, and cloud, including support for Bring Your Own Device (BYOD), ARA offers programmability, performance, robustness, and heterogeneity at the same time, thus enabling rural-focused co-evolution of wireless and applications while helping advance the frontiers of wireless systems in domains such as Open RAN, NextG, and agriculture applications. The resulting solutions hold the potential of reducing the rural broadband cost by a factor of 10 or more, thus making rural broadband as affordable as urban broadband. Here we present the design principles and implementation strategies of ARA, characterize its performance and heterogeneity, and highlight example wireless and application experiments uniquely enabled by ARA.

Place, publisher, year, edition, pages
Elsevier BV, 2025
Keywords
ARA, NextG, PAWR, Precision agriculture, Rural wireless, xHaul
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-362041 (URN)10.1016/j.comnet.2025.111188 (DOI)001456857200001 ()2-s2.0-105000528356 (Scopus ID)
Note

QC 20250403

Available from: 2025-04-03 Created: 2025-04-03 Last updated: 2025-05-06Bibliographically approved
Bandali, M., Riu, J. R., Lewitzki, A., Roy, D. & Gross, J. (2025). ML-Based Fault Management Automation in Large-Scale Fixed and Mobile Telecommunication Networks. IEEE Transactions on Network and Service Management, 22(2), 1775-1787
Open this publication in new window or tab >>ML-Based Fault Management Automation in Large-Scale Fixed and Mobile Telecommunication Networks
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2025 (English)In: IEEE Transactions on Network and Service Management, E-ISSN 1932-4537, Vol. 22, no 2, p. 1775-1787Article in journal (Refereed) Published
Abstract [en]

Many network faults are flooding the telecommunication companies in the form of Trouble Tickets (TT). Automation in managing these TTs is vital in increasing customer satisfaction. We develop a solution to address two challenges regarding TTs generated from fixed and mobile access network domains: prediction of resolution times and technician dispatch needs. Our study utilizes datasets from Telenor, a Swedish telecommunication operator, encompassing 35,000 access switches and 8,000 base stations. It incorporates 40,000 switch TTs and 22,000 mobile TTs during 2019-2023. None of the previous works studied multiple telecommunication domains or considered the time evolution of TTs. This work comprehensively studies several prediction models for the mentioned use cases and network domains. Our models successfully outperform the company baseline and best proposed state-of-the-art models. Within 1-hour confidence interval, our method can correctly predict shortest ranges of resolution times for 90% of switch TTs and 80% of mobile TTs. We also predict the necessity of dispatching workforce to the place with weighted F1 scores of respectively, 88% and 89% for switch and mobile TTs which shows high average accuracy of our system in prediction across both dispatch and non-dispatch TT classes to assist operation. With these scores, our model is capable of allocating resources automatically, enhancing customer satisfaction. We also studied the TTs evolution, for example, for switch TTs, within 15 minutes of creation time, prediction improves by 57% and 50%, for resolution and dispatch prediction, respectively.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2025
Keywords
Base stations, Automation, Predictive models, Knowledge engineering, Data models, Companies, Neural networks, Measurement, Accuracy, Support vector machines, Trouble tickets, mobile network, fixed network, fault management, resolution time prediction, dispatch-need prediction, machine learning models
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-364232 (URN)10.1109/TNSM.2024.3509636 (DOI)001473161100015 ()2-s2.0-85211352921 (Scopus ID)
Note

QC 20250609

Available from: 2025-06-09 Created: 2025-06-09 Last updated: 2025-06-09Bibliographically approved
Roy, N., Dhullipalla, M. H., Sharma, G. P., Dimarogonas, D. V. & Gross, J. (2025). Quality of Control Based Resource Dimensioning for Collaborative Edge Robotics. In: 2025 IEEE 22nd Consumer Communications and Networking Conference, CCNC 2025: . Paper presented at 22nd IEEE Consumer Communications and Networking Conference, CCNC 2025, Las Vegas, United States of America, Jan 10 2025 - Jan 13 2025. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Quality of Control Based Resource Dimensioning for Collaborative Edge Robotics
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2025 (English)In: 2025 IEEE 22nd Consumer Communications and Networking Conference, CCNC 2025, Institute of Electrical and Electronics Engineers (IEEE) , 2025Conference paper, Published paper (Refereed)
Abstract [en]

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2025
Keywords
collaborative robotics, edge computing, multi-agent systems, quality of control, safety-critical applications
National Category
Robotics and automation Communication Systems Control Engineering
Identifiers
urn:nbn:se:kth:diva-363996 (URN)10.1109/CCNC54725.2025.10976180 (DOI)2-s2.0-105005147175 (Scopus ID)
Conference
22nd IEEE Consumer Communications and Networking Conference, CCNC 2025, Las Vegas, United States of America, Jan 10 2025 - Jan 13 2025
Note

Part of ISBN 9798331508050

QC 20250603

Available from: 2025-06-02 Created: 2025-06-02 Last updated: 2025-06-03Bibliographically approved
Rabet, I., Fotouhi, H., Alves, M., Champati, J. P., Gross, J., Vahabi, M. & Björkman, M. (2024). A Stochastic Network Calculus Model for TSCH Schedulers. In: 2024 IEEE Symposium on Computers and Communications, ISCC 2024: . Paper presented at 29th IEEE Symposium on Computers and Communications, ISCC 2024, Paris, France, Jun 26 2024 - Jun 29 2024. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A Stochastic Network Calculus Model for TSCH Schedulers
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2024 (English)In: 2024 IEEE Symposium on Computers and Communications, ISCC 2024, Institute of Electrical and Electronics Engineers (IEEE) , 2024Conference paper, Published paper (Refereed)
Abstract [en]

Low-power wireless Internet of Things (IoT) devices employ Time Slotted Channel Hopping (TSCH) Medium Access Control to achieve predictable timing behaviour. TSCH aims at collision-free scheduling by exploiting diversity over time (slots) and frequency (channels). However, existing works on performance and worst-case analysis are based on deterministic models, which lead to rather pessimistic non-realistic results, i.e. tools for probabilistic performance analysis of TSCH schedulers are still lacking. In this context, we devised a Stochastic Network Calculus model that enables to calculate end-to-end delays for specific traffic flows and (deadline) violation probability, building on Moment Generating Functions. We instantiate this SNC model and provide bounds for three widely used TSCH schedulers, namely Minimal Scheduling Function, Orchestra, and a custom collision-free scheduler, with different parameters such as radio duty-cycle, radio link quality, and traffic arrival rate. We demonstrate that our proposed model closely follows the simulation results, under different network scenarios.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
6loWPAN, 6TiSCH, Contiki, COOJA, Internet-of-Things (IoT), performance analysis, RPL, schedulers, simulation, stochastic network calculus, wireless sensor networks
National Category
Communication Systems Computer Engineering
Identifiers
urn:nbn:se:kth:diva-356951 (URN)10.1109/ISCC61673.2024.10733626 (DOI)001363176200065 ()2-s2.0-85209205841 (Scopus ID)
Conference
29th IEEE Symposium on Computers and Communications, ISCC 2024, Paris, France, Jun 26 2024 - Jun 29 2024
Note

 Part of ISBN 979-835035423-2

QC 20250122

Available from: 2024-11-28 Created: 2024-11-28 Last updated: 2025-02-06Bibliographically approved
Mostafavi, S. S., Tillner, M., Sharma, G. P. & Gross, J. (2024). EDAF: An End-to-End Delay Analytics Framework for 5G-and-Beyond Networks. In: : . Paper presented at 11th International Workshop on Computer and Networking Experimental Research using Testbeds (CNERT 2024). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>EDAF: An End-to-End Delay Analytics Framework for 5G-and-Beyond Networks
2024 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Supporting applications in emerging domains like cyber-physical systems and human-in-the-loop scenarios typically requires adherence to strict end-to-end delay guarantees. Contributions of many tandem processes unfolding layer by layer within the wireless network result in violations of delay constraints, thereby severely degrading application performance. Meeting the application's stringent requirements necessitates coordinated optimization of the end-to-end delay by fine-tuning all contributing processes. To achieve this task, we designed and implemented EDAF, a framework to decompose packets' end-to-end delays and determine each component's significance for 5G network. We showcase EDAF on OpenAirInterface 5G uplink, modified to report timestamps across the data plane. By applying the obtained insights, we optimized end-to-end uplink delay by eliminating segmentation and frame-alignment delays, decreasing average delay from 12ms to 4ms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-352157 (URN)10.1109/INFOCOMWKSHPS61880.2024.10620853 (DOI)001300418400140 ()2-s2.0-85196346420 (Scopus ID)
Conference
11th International Workshop on Computer and Networking Experimental Research using Testbeds (CNERT 2024)
Note

QC 20240823

Part of ISBN 979-8-3503-8447-5

Available from: 2024-08-22 Created: 2024-08-22 Last updated: 2025-05-09Bibliographically approved
Zhao, Y., Gao, W., Zhu, Y., Hu, Y. & Gross, J. (2024). Energy Efficiency Maximization for Multi-Node IoT Networks Operating with Finite Blocklength Codes. In: 2024 19th International Symposium on Wireless Communication Systems, ISWCS 2024: . Paper presented at 19th International Symposium on Wireless Communication Systems, ISWCS 2024, Rio de Janeiro, Brazil, Jul 14 2024 - Jul 17 2024. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Energy Efficiency Maximization for Multi-Node IoT Networks Operating with Finite Blocklength Codes
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2024 (English)In: 2024 19th International Symposium on Wireless Communication Systems, ISWCS 2024, Institute of Electrical and Electronics Engineers (IEEE) , 2024Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we consider an Internet of Things (IoT) network supporting latency-critical transmissions to multiple nodes in the finite blocklength regime. An energy efficiency maximizing design is provided via efficiently jointly allocating the power and blocklength among the transmissions to different nodes, while guaranteeing per-node constraint of reliability. To address the formulated non-convex problem, we propose a Majorization-Minimization-based approach. Specifically, we tightly approximate the problem at each local point by introducing an auxiliary constant. Then, we rigorously prove the convexity of the conducted local problem. By successively optimally solving each convex local problem, a near-optimal solution is finally achieved. Via simulations, we demonstrate the performance advantages of the proposed joint design.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
6G, energy efficiency, finite blocklength, multi-node, resource allocation
National Category
Communication Systems Telecommunications
Identifiers
urn:nbn:se:kth:diva-353494 (URN)10.1109/ISWCS61526.2024.10639049 (DOI)001304943800051 ()2-s2.0-85203464631 (Scopus ID)
Conference
19th International Symposium on Wireless Communication Systems, ISWCS 2024, Rio de Janeiro, Brazil, Jul 14 2024 - Jul 17 2024
Note

Part of ISBN [9798350362510] QC 20240923

Available from: 2024-09-19 Created: 2024-09-19 Last updated: 2025-01-20Bibliographically approved
Moothedath, V. N., Champati, J. P. & Gross, J. (2024). Getting the Best Out of Both Worlds: Algorithms for Hierarchical Inference at the Edge. IEEE Transactions on Machine Learning in Communications and Networking, 2, 280-297
Open this publication in new window or tab >>Getting the Best Out of Both Worlds: Algorithms for Hierarchical Inference at the Edge
2024 (English)In: IEEE Transactions on Machine Learning in Communications and Networking, E-ISSN 2831-316X, Vol. 2, p. 280-297Article in journal (Refereed) Published
Abstract [en]

We consider a resource-constrained Edge Device (ED), such as an IoT sensor or a microcontroller unit, embedded with a small-size ML model (S-ML) for a generic classification application and an Edge Server (ES) that hosts a large-size ML model (L-ML). Since the inference accuracy of S-ML is lower than that of the L-ML, offloading all the data samples to the ES results in high inference accuracy, but it defeats the purpose of embedding S-ML on the ED and deprives the benefits of reduced latency, bandwidth savings, and energy efficiency of doing local inference. In order to get the best out of both worlds, i.e., the benefits of doing inference on the ED and the benefits of doing inference on ES, we explore the idea of Hierarchical Inference (HI), wherein S-ML inference is only accepted when it is correct, otherwise the data sample is offloaded for L-ML inference. However, the ideal implementation of HI is infeasible as the correctness of the S-ML inference is not known to the ED. We thus propose an online meta-learning framework that the ED can use to predict the correctness of the S-ML inference. In particular, we propose to use the probability corresponding to the maximum probability class output by S-ML for a data sample and decide whether to offload it or not. The resulting online learning problem turns out to be a Prediction with Expert Advice (PEA) problem with continuous expert space. For a full feedback scenario, where the ED receives feedback on the correctness of the S-ML once it accepts the inference, we propose the HIL-F algorithm and prove a sublinear regret bound √ n ln(1/λ min )/2 without any assumption on the smoothness of the loss function, where n is the number of data samples and λ min is the minimum difference between any two distinct maximum probability values across the data samples. For a no-local feedback scenario, where the ED does not receive the ground truth for the classification, we propose the HIL-N algorithm and prove that it has O ( n 2/3 ln 1/3 (1/λ min )) regret bound. We evaluate and benchmark the performance of the proposed algorithms for image classification application using four datasets, namely, Imagenette and Imagewoof [1], MNIST [2], and CIFAR-10 [3].

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-343505 (URN)10.1109/tmlcn.2024.3366501 (DOI)
Funder
Vinnova, 2019-00031Swedish Research Council, 2022-03922
Note

QC 20240216

Available from: 2024-02-15 Created: 2024-02-15 Last updated: 2025-02-20Bibliographically approved
Letsioue, A., Moothedath, V. N., Beherae, A. P., Champatie, J. P. & Gross, J. (2024). Hierarchical Inference at the Edge: A Batch Processing Approach. In: Proceedings - 2024 IEEE/ACM Symposium on Edge Computing, SEC 2024: . Paper presented at 9th Annual IEEE/ACM Symposium on Edge Computing, SEC 2024, Rome, Italy, December 4-7, 2024 (pp. 476-482). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Hierarchical Inference at the Edge: A Batch Processing Approach
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2024 (English)In: Proceedings - 2024 IEEE/ACM Symposium on Edge Computing, SEC 2024, Institute of Electrical and Electronics Engineers (IEEE) , 2024, p. 476-482Conference paper, Published paper (Refereed)
Abstract [en]

Deep learning (DL) applications have rapidly evolved to address increasingly complex tasks by leveraging large-scale, resource-intensive models. However, deploying such models on low-power devices is not practical or economically scalable. While cloud-centric solutions satisfy these computational demands, they present challenges in terms of communication costs and latencies for real-Time applications when every computation task is offloaded. To mitigate these concerns, hierarchical inference (HI) frameworks have been proposed, enabling edge devices equipped with small ML models to collaborate with edge servers by selectively offloading complex tasks. Existing HI approaches depend on immediate offloading of data upon selection, which can lead to inefficiencies due to frequent communication, especially in time-varying wireless environments. In this work, we introduce Batch HI, an approach that offloads samples in batches, thereby reducing communication overhead and improving system efficiency while achieving similar performance as existing HI methods. Additionally, we find the optimal batch size that attains a crucial balance between responsiveness and system time, tailored to specific user requirements. Numerical results confirm the effectiveness of our approach, highlighting the scenarios where batching is particularly beneficial.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
batching, edge computing, Hierarchical inference, offloading decisions, regret bound, responsiveness, tiny ML
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-359857 (URN)10.1109/SEC62691.2024.00055 (DOI)001424939400046 ()2-s2.0-85216793011 (Scopus ID)
Conference
9th Annual IEEE/ACM Symposium on Edge Computing, SEC 2024, Rome, Italy, December 4-7, 2024
Note

Part of ISBN 979-8-3503-7828-3

QC 20250213

Available from: 2025-02-12 Created: 2025-02-12 Last updated: 2025-04-01Bibliographically approved
Imtiaz, S. & Gross, J. (2024). Machine Learning Based Fair Resource Allocation Leveraging User Coordinates in Multi-Antenna Systems.
Open this publication in new window or tab >>Machine Learning Based Fair Resource Allocation Leveraging User Coordinates in Multi-Antenna Systems
2024 (English)Manuscript (preprint) (Other academic)
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-359394 (URN)
Available from: 2025-01-30 Created: 2025-01-30 Last updated: 2025-01-31Bibliographically approved
Rau, E.-P., Gross, J., Coomes, D. A., Swinfield, T., Madhavapeddy, A., Balmford, A. & Keshav, S. (2024). Mitigating risk of credit reversal in nature-based climate solutions by optimally anticipating carbon release. Carbon Management, 15(1), Article ID 2390854.
Open this publication in new window or tab >>Mitigating risk of credit reversal in nature-based climate solutions by optimally anticipating carbon release
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2024 (English)In: Carbon Management, ISSN 1758-3004, E-ISSN 1758-3012, Vol. 15, no 1, article id 2390854Article in journal (Refereed) Published
Abstract [en]

Nature-based climate solutions supply carbon credits generated from net carbon drawdown in exchange for project funding, but their credibility is challenged by the inherent variability and impermanence of drawdown. By evaluating drawdown benefits from a social cost of carbon perspective, project developers can enhance credibility and estimate impermanence by conservatively anticipating drawdowns to be eventually released following a release schedule, issuing additional credits when actual release is less severe than anticipated. We demonstrate how we can use ex post observations of drawdowns to construct optimal release schedules that limit the risk of credit reversals (when net drawdown is negative). We simulate both theoretical and real-life projects to examine how this approach balances the trade-off between generating credits evaluated as more permanent and limiting reversal risk. We discuss how this approach incentivizes project performance and provides a pragmatic solution to challenges facing larger-scale implementation of nature-based climate solutions.

Place, publisher, year, edition, pages
Informa UK Limited, 2024
Keywords
Carbon credits, temporary carbon storage, REDD plus, deforestation, climate action, life on land
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-353131 (URN)10.1080/17583004.2024.2390854 (DOI)001302374700001 ()2-s2.0-85202862058 (Scopus ID)
Note

QC 20240912

Available from: 2024-09-12 Created: 2024-09-12 Last updated: 2024-09-12Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-6682-6559

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