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You, Y., Xu, Q. & Fischione, C. (2024). Hierarchical Online Game-Theoretic Framework for Real-Time Energy Trading in Smart Grid. IEEE Transactions on Smart Grid, 15(2), 1634-1645
Open this publication in new window or tab >>Hierarchical Online Game-Theoretic Framework for Real-Time Energy Trading in Smart Grid
2024 (English)In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 15, no 2, p. 1634-1645Article in journal (Refereed) Published
Abstract [en]

In this paper, the real-time energy trading problem between the energy provider and the consumers in a smart grid system is studied. The problem is formulated as a hierarchical game, where the energy provider acts as a leader who determines the pricing strategy that maximizes its profits, while the consumers act as followers who react by adjusting their energy demand to save their energy costs and enhance their energy consumption utility. In particular, the energy provider employs a pricing strategy that depends on the aggregated amount of energy requested by the consumers, which suits a commodity-limited market. With this price setting, the consumers' energy demand response strategies are designed under a non-cooperative game framework, where a unique generalized Nash equilibrium point is shown to exist. As an extension, the consumers are assumed to be unaware of their future energy consumption behaviors due to uncertain personal needs. To address this issue, an online distributed energy trading framework is proposed, where the energy provider and the consumers can design their strategies only based on the historical knowledge of consumers' energy consumption behavior at each bidding stage. Besides, the proposed framework can be implemented in a distributed manner such that the consumers can design their demand responses by only exchanging information with their neighboring consumers, which requires much fewer communication resources and would thus be more suitable for the practical operation of the grid. As a theoretical guarantee, the proposed framework is further proved to asymptotically achieve the same performance as the offline solution for both energy provider and consumers' optimization problems. The performance of practical designs of the proposed online distributed energy trading framework is finally illustrated in numerical experiments.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Demand response, real-time pricing, utility-cost trade-off, non-cooperative game, generalized Nash equilibrium seeking, online learning
National Category
Energy Systems
Identifiers
urn:nbn:se:kth:diva-345543 (URN)10.1109/TSG.2023.3308055 (DOI)001174148100032 ()2-s2.0-85168735178 (Scopus ID)
Note

QC 20240415

Available from: 2024-04-15 Created: 2024-04-15 Last updated: 2024-04-15Bibliographically approved
Timoudas, T. O., Zhang, S., Magnusson, S. & Fischione, C. (2023). A General Framework to Distribute Iterative Algorithms With Localized Information Over Networks. IEEE Transactions on Automatic Control, 68(12), 7358-7373
Open this publication in new window or tab >>A General Framework to Distribute Iterative Algorithms With Localized Information Over Networks
2023 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 68, no 12, p. 7358-7373Article in journal (Refereed) Published
Abstract [en]

Emerging applications in the Internet of Things (IoT) and edge computing/learning have sparked massive renewed interest in developing distributed versions of existing (centralized) iterative algorithms often used for optimization or machine learning purposes. While existing work in the literature exhibits similarities, for the tasks of both algorithm design and theoretical analysis, there is still no unified method or framework for accomplishing these tasks. This article develops such a general framework for distributing the execution of (centralized) iterative algorithms over networks in which the required information or data is partitioned between the nodes in the network. This article furthermore shows that the distributed iterative algorithm, which results from the proposed framework, retains the convergence properties (rate) of the original (centralized) iterative algorithm. In addition, this article applies the proposed general framework to several interesting example applications, obtaining results comparable to the state of the art for each such example, while greatly simplifying and generalizing their convergence analysis. These example applications reveal new results for distributed proximal versions of gradient descent, the heavy ball method, and Newton's method. For example, these results show that the dependence on the condition number for the convergence rate of this distributed heavy ball method is at least as good as that of centralized gradient descent.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Agents and autonomous systems, communication networks, distributed algorithms, optimization algorithms
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-348559 (URN)10.1109/TAC.2023.3279901 (DOI)001122871700040 ()2-s2.0-85161038450 (Scopus ID)
Note

QC 20240626

Available from: 2024-06-26 Created: 2024-06-26 Last updated: 2024-06-26Bibliographically approved
Weinberg, D., Wang, Q., Timoudas, T. O. & Fischione, C. (2023). A Review of Reinforcement Learning for Controlling Building Energy Systems From a Computer Science Perspective. Sustainable cities and society, 89, Article ID 104351.
Open this publication in new window or tab >>A Review of Reinforcement Learning for Controlling Building Energy Systems From a Computer Science Perspective
2023 (English)In: Sustainable cities and society, ISSN 2210-6707, Vol. 89, article id 104351Article, review/survey (Refereed) Published
Abstract [en]

Energy efficient control of energy systems in buildings is a widely recognized challenge due to the use of low temperature heating, renewable electricity sources, and the incorporation of thermal storage. Reinforcement Learning (RL) has been shown to be effective at minimizing the energy usage in buildings with maintained thermal comfort despite the high system complexity. However, RL has certain disadvantages that make it challenging to apply in engineering practices. In this review, we take a computer science approach to identifying three main categories of challenges of using RL for control of Building Energy Systems (BES). The three categories are the following: RL in single buildings, RL in building clusters, and multi-agent aspects. For each topic, we analyse the main challenges, and the state-of-the-art approaches to alleviate them. We also identify several future research directions on subjects such as sample efficiency, transfer learning, and the theoretical properties of RL in building energy systems. In conclusion, our review shows that the work on RL for BES control is still in its initial stages. Although significant progress has been made, more research is needed to realize the goal of RL-based control of BES at scale.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Building Energy System, HVAC, Heating, Cooling, Reinforcement learning, Machine learning, RL, ML
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-323582 (URN)10.1016/j.scs.2022.104351 (DOI)000910896200001 ()2-s2.0-85144402805 (Scopus ID)
Note

QC 20230208

Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-02-08Bibliographically approved
Daei, S., Razavikia, S., Kountouris, M., Skoglund, M., Fodor, G. & Fischione, C. (2023). Blind Asynchronous Goal-Oriented Detection for Massive Connectivity. In: 2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023: . Paper presented at 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023, Singapore, Singapore, Aug 24 2023 - Aug 27 2023 (pp. 167-174). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Blind Asynchronous Goal-Oriented Detection for Massive Connectivity
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2023 (English)In: 2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 167-174Conference paper, Published paper (Refereed)
Abstract [en]

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels’ angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal oriented optimization problem is proposed which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra reliable and low latency communications, making it an important contribution to the emerging 6G networks.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
atomic norm minimization, goal-oriented optimization, Internet of Things, MIMO communications systems, Random access, reconstruction-free inference
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-343751 (URN)10.23919/WiOpt58741.2023.10349818 (DOI)2-s2.0-85184668805 (Scopus ID)
Conference
21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023, Singapore, Singapore, Aug 24 2023 - Aug 27 2023
Note

QC 20240222

Part of ISBN 978-390317655-3

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-02-22Bibliographically approved
Razavikia, S., Da Silva, J. M. & Fischione, C. (2023). ChannelComp: A General Method for Computation by Communications. IEEE Transactions on Communications, 1-1
Open this publication in new window or tab >>ChannelComp: A General Method for Computation by Communications
2023 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, p. 1-1Article in journal (Refereed) Published
Abstract [en]

Over-the-air computation (AirComp) is a well-known technique by which several wireless devices transmit by analog amplitude modulation to achieve a sum of their transmit signals at a common receiver. The underlying physical principle is the superposition property of the radio waves. Since such superposition is analog and in amplitude, it is natural that AirComp uses analog amplitude modulations. Unfortunately, this is impractical because most wireless devices today use digital modulations. It would be highly desirable to use digital communications because of their numerous benefits, such as error correction, synchronization, acquisition of channel state information, and widespread use. However, when we use digital modulations for AirComp, a general belief is that the superposition property of the radio waves returns a meaningless overlapping of the digital signals. In this paper, we break through such beliefs and propose an entirely new digital channel computing method named ChannelComp, which can use digital as well as analog modulations. We propose a feasibility optimization problem that ascertains the optimal modulation for computing arbitrary functions over-the-air. Additionally, we propose pre-coders to adapt existing digital modulation schemes for computing the function over the multiple access channel. The simulation results verify the superior performance of ChannelComp compared to AirComp, particularly for the product functions, with more than 10 dB improvement of the computation error.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-338912 (URN)10.1109/tcomm.2023.3324999 (DOI)001164695100015 ()2-s2.0-85174823953 (Scopus ID)
Note

QC 20231031

Available from: 2023-10-30 Created: 2023-10-30 Last updated: 2024-06-18Bibliographically approved
Razavikia, S., Barros da Silva Jr., J. M. & Fischione, C. (2023). Computing Functions Over-the-Air Using Digital Modulations. In: ICC 2023: IEEE International Conference on Communications: Sustainable Communications for Renaissance. Paper presented at 2023 IEEE International Conference on Communications, ICC 2023, Rome, Italy, May 28 2023 - Jun 1 2023 (pp. 5780-5786). Institute of Electrical and Electronics Engineers Inc., 2023
Open this publication in new window or tab >>Computing Functions Over-the-Air Using Digital Modulations
2023 (English)In: ICC 2023: IEEE International Conference on Communications: Sustainable Communications for Renaissance, Institute of Electrical and Electronics Engineers Inc. , 2023, Vol. 2023, p. 5780-5786Conference paper, Published paper (Refereed)
Abstract [en]

Over-the-air computation (AirComp) is a known technique in which wireless devices transmit values by analog amplitude modulation so that a function of these values is computed over the communication channel at a common receiver. The physical reason is the superposition properties of the electromagnetic waves, which naturally return sums of analog values. Consequently, the applications of AirComp are almost entirely restricted to analog communication systems. However, the use of digital communications for over-the-air computations would have several benefits, such as error correction, synchronization, acquisition of channel state information, and easier adoption by current digital communication systems. Nevertheless, a common belief is that digital modulations are generally unfeasible for computation tasks because the overlapping of digitally modulated signals returns signals that seem to be meaningless for these tasks. This paper breaks through such a belief and proposes a fundamentally new computing method, named ChannelComp, for performing over-the-air computations by any digital modulation. In particular, we propose digital modulation formats that allow us to compute a wider class of functions than AirComp can compute, and we propose a feasibility optimization problem that ascertains the optimal digital modulation for computing functions over-the-air. The simulation results verify the superior performance of ChannelComp in comparison to AirComp, particularly for the product functions, with around 10 dB improvement of the computation error.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Series
IEEE International Conference on Communications, ISSN 1550-3607 ; 2023
Keywords
Digital communication, modulation, nomo-graphic functions, over-the-air computation, symmetric function
National Category
Communication Systems Telecommunications
Identifiers
urn:nbn:se:kth:diva-340806 (URN)10.1109/ICC45041.2023.10279784 (DOI)001094862605145 ()2-s2.0-85178299399 (Scopus ID)
Conference
2023 IEEE International Conference on Communications, ICC 2023, Rome, Italy, May 28 2023 - Jun 1 2023
Note

Part of ISBN 9781538674628

QC 20231214

Available from: 2023-12-14 Created: 2023-12-14 Last updated: 2024-03-12Bibliographically approved
Fischione, C., Chafii, M., Deng, Y. & Erol-Kantarci, M. (2023). Data Sets For Machine Learning In Wireless Communications And Networks. IEEE Communications Magazine, 61(9), 80-81
Open this publication in new window or tab >>Data Sets For Machine Learning In Wireless Communications And Networks
2023 (English)In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 61, no 9, p. 80-81Article in journal, Editorial material (Other academic) Published
Abstract [en]

The articles in this special section focus on the role of data sets for the evolution of the telecommunication industry in the 5G and 6G era. In 5G and 6G, many new services are emerging to accommodate various Internet of Things (IoT) devices, going beyond the traditional provisions of mobile phones and internet connectivity. Examples of these services include extended reality devices, sensors, or ground and aerial robots. The deployment of these advanced services, however, poses challenges for the wireless network, particularly in its ability to support ubiquitous connections while meeting diverse quality-of-service (QoS) requirements. Despite the remarkable success of model-based design and analysis in wireless networks, it has become evident that these conventional approaches may not be fully adequate to address the dynamic and diverse QoS requirements posed by the emerging IoT landscape. The heterogeneity of devices and services necessitates a more adaptive and intelligent approach to ensure efficient network performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Special issues and sections, 5G mobile communication, Data models, Machine learning, Wireless communication, Internet, 6G mobile communication
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-339327 (URN)10.1109/MCOM.2023.10268845 (DOI)001080991100005 ()2-s2.0-85174492111 (Scopus ID)
Note

QC 20231110

Available from: 2023-11-10 Created: 2023-11-10 Last updated: 2023-11-10Bibliographically approved
Xu, Y., Mohammed, T., Di Francesco, M. & Fischione, C. (2023). Distributed Assignment With Load Balancing for DNN Inference at the Edge. IEEE Internet of Things Journal, 10(2), 1053-1065
Open this publication in new window or tab >>Distributed Assignment With Load Balancing for DNN Inference at the Edge
2023 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, no 2, p. 1053-1065Article in journal (Refereed) Published
Abstract [en]

Inference carried out on pretrained deep neural networks (DNNs) is particularly effective as it does not require retraining and entails no loss in accuracy. Unfortunately, resource-constrained devices such as those in the Internet of Things may need to offload the related computation to more powerful servers, particularly, at the network edge. However, edge servers have limited resources compared to those in the cloud; therefore, inference offloading generally requires dividing the original DNN into different pieces that are then assigned to multiple edge servers. Related approaches in the state-of-the-art either make strong assumptions on the system model or fail to provide strict performance guarantees. This article specifically addresses these limitations by applying distributed assignment to DNN inference at the edge. In particular, it devises a detailed model of DNN-based inference, suitable for realistic scenarios involving edge computing. Optimal inference offloading with load balancing is also defined as a multiple assignment problem that maximizes proportional fairness. Moreover, a distributed algorithm for DNN inference offloading is introduced to solve such a problem in polynomial time with strong optimality guarantees. Finally, extensive simulations employing different data sets and DNN architectures establish that the proposed solution significantly improves upon the state-of-the-art in terms of inference time (1.14 to 2.62 times faster), load balance (with Jain's fairness index of 0.9), and convergence (one order of magnitude less iterations). 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Assignment problems, deep neural network (DNN) offloading, distributed inference, edge computing, Combinatorial optimization, computation offloading, Deep neural networks, Inference engines, Internet of things, Job analysis, Network architecture, Polynomial approximation, Computational modelling, Deep neural network offloading, Edge server, Load-Balancing, Network inference, Task analysis, Computer architecture
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-327261 (URN)10.1109/JIOT.2022.3205410 (DOI)001011036600008 ()2-s2.0-85137908524 (Scopus ID)
Note

QC 20230524

Available from: 2023-05-24 Created: 2023-05-24 Last updated: 2023-07-31Bibliographically approved
Hellström, H., Fodor, V. & Fischione, C. (2023). Federated Learning Over-the-Air by Retransmissions. IEEE Transactions on Wireless Communications, 1-1
Open this publication in new window or tab >>Federated Learning Over-the-Air by Retransmissions
2023 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, p. 1-1Article in journal (Refereed) Published
Abstract [en]

Motivated by the increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL) is of particular interest due to its communication efficiency and its ability to deal with the problem of non-IID data. FL training can be accelerated by a wireless communication method called Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous uplink transmissions to efficiently aggregate model updates. However, since AirComp utilizes analog communication, it introduces inevitable estimation errors. In this paper, we study the impact of such estimation errors on the convergence of FL and propose retransmissions as a method to improve FL accuracy over resource-constrained wireless networks. First, we derive the optimal AirComp power control scheme with retransmissions over static channels. Then, we investigate the performance of Over-the-Air FL with retransmissions and find two upper bounds on the FL loss function. Numerical results demonstrate that the power control scheme offers significant reductions in mean squared error. Additionally, we provide simulation results on MNIST classification with a deep neural network that reveals significant improvements in classification accuracy for low-SNR scenarios.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Federated Learning, Over-the-Air Computation, Retransmissions
National Category
Communication Systems Signal Processing Telecommunications
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-327825 (URN)10.1109/twc.2023.3268742 (DOI)001128031700032 ()2-s2.0-85159703045 (Scopus ID)
Note

QC 20230608

Available from: 2023-05-31 Created: 2023-05-31 Last updated: 2024-03-18Bibliographically approved
Kant, S., Barros da Silva Jr., J. M., Fodor, G., Göransson, B., Bengtsson, M. & Fischione, C. (2023). Federated Learning Using Three-Operator ADMM. IEEE Journal on Selected Topics in Signal Processing, 17(1), 205-221
Open this publication in new window or tab >>Federated Learning Using Three-Operator ADMM
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2023 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 17, no 1, p. 205-221Article in journal (Refereed) Published
Abstract [en]

Federated learning (FL) has emerged as an instance of distributed machine learning paradigm that avoids the transmission of data generated on the users' side. Although data are not transmitted, edge devices have to deal with limited communication bandwidths, data heterogeneity, and straggler effects due to the limited computational resources of users' devices. A prominent approach to overcome such difficulties is FedADMM, which is based on the classical two-operator consensus alternating direction method of multipliers (ADMM). The common assumption of FL algorithms, including FedADMM, is that they learn a global model using data only on the users' side and not on the edge server. However, in edge learning, the server is expected to be near the base station and have direct access to rich datasets. In this paper, we argue that leveraging the rich data on the edge server is much more beneficial than utilizing only user datasets. Specifically, we show that the mere application of FL with an additional virtual user node representing the data on the edge server is inefficient. We propose FedTOP-ADMM, which generalizes FedADMM and is based on a three-operator ADMM-type technique that exploits a smooth cost function on the edge server to learn a global model parallel to the edge devices. Our numerical experiments indicate that FedTOP-ADMM has substantial gain up to 33% in communication efficiency to reach a desired test accuracy with respect to FedADMM, including a virtual user on the edge server.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-323513 (URN)10.1109/jstsp.2022.3221681 (DOI)000937190500014 ()2-s2.0-85142775857 (Scopus ID)
Note

QC 20230426

Available from: 2023-01-31 Created: 2023-01-31 Last updated: 2023-10-16Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-9810-3478

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