Autonomic and Distributed Joint Routing and Power Control for Delay-Sensitive Applications in Multi-Hop Wireless Networks
Abstract
Multi-hop wireless networks can provide flexible network infrastructures at a low cost. However, most existing wireless networking solutions are designed for delay-insensitive applications, thereby resulting in poor performance when handling delay-sensitive applications. Traditionally, network design problems are formulated as static optimizations, by assuming the network characteristics remain static. However, these solutions are not optimal when the environments are dynamic. Recently, several research works apply machine learning to maximize the performance of multi-hop wireless networks in dynamic environments, but they either only focus on determining policies at the network layer, without considering the lower-layers' actions, or use centralized learning approaches, which are inefficient for delay-sensitive applications, due to the large delay when propagating messages throughout the network. We propose in this paper a new solution that enables the nodes to autonomously determine their routing and transmission power to maximize the network utility, in a dynamic environment. We formulate the problem as a Markov Decision Process, and propose a distributed computation of the optimal policy. Moreover, we use reinforcement-learning to find the optimized policy when the dynamics are unknown. We explicitly consider the impact of the information overhead on the network performance, and propose several novel algorithms to reduce the information overhead.
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Lin and Schaar rely on an n-step temporal difference learning method and aim at reducing the frequency of message exchange and thus the communication overhead without jeopardizing convergence speed.
“They rely on a nstep temporal difference (TD) [433] learning method, and aim at reducing the frequency of message exchange, and thus the communication overhead without jeopardizing the convergence speed.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [277]
Lin and Schaar address the problem of routing delay-sensitive applications in the more general context of multi-hop wireless ad hoc networks.
“In [277] Lin and Schaar address the problem of routing delay-sensitive applications in the more general context of multi-hop wireless ad hoc networks.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [277]
Utility functions such as QoS provisioning, load balancing and network lifetime maximization require more information to be disseminated at the cost of increased complexity and communication overhead.
“utility functions such as QoS provisioning, load balancing and network lifetime maximization, as in Q-PR [24], QELAR [197,277], require more information to be disseminated at the cost of an increased complexity and communication overhead.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [277]
Routing in wireless ad hoc networks calls for decentralized RL where the learning capability is distributed among the routing nodes.
“routing in wireless ad hoc networks calls for decentralized RL [59,277] where the learning capability is distributed among the routing nodes.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [277]
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
(1995) · Cited by 10,558 · see citing works
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972 · see citing works
Cross-Layer Combining of Adaptive Modulation and Coding With Truncated ARQ Over Wireless Links
Liu, Q., Zhou, S., & Giannakis, G. B. (2004) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2004.833474 · Cited by 927 · see citing works
Finite-state Markov model for Rayleigh fading channels
Zhang, Q., & Kassam, S. A. (1999) · IEEE Transactions on Communications · doi:10.1109/26.803503 · Cited by 833 · see citing works
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach
Boyan, J. A., & Littman, M. L. (1994) · Cited by 694 · see citing works
Selfish Routing and the Price of Anarchy
Roughgarden, T. (2006) · doi:10.21236/ada637949 · Cited by 678 · see citing works
Kühn, F., Wattenhofer, R. P., Zhang, Y., & Zollinger, A. (2003) · ACM Symposium on Principles of Distributed Computing (PODC) · doi:10.1145/872035.872044 · Cited by 660 · see citing works
Selfish Routing and the Price of Anarchy
Roughgarden, T. (2016) · Cambridge University Press eBooks · doi:10.1017/cbo9781316779309.012 · Cited by 656 · see citing works
Dynamic Power Allocation and Routing for Time Varying Wireless Networks
Neely, M. J., Modiano, E., Member, S., & Rohrs, C. E. (2003) · Cited by 653 · see citing works
Optimal routing, link scheduling and power control in multihop wireless networks
Cruz, R. L., & Santhanam, A. (2004) · doi:10.1109/infcom.2003.1208720 · Cited by 615 · see citing works
Dynamic power allocation and routing for time-varying wireless networks
Neely, M. J., Modiano, E., & Rohrs, C. E. (2005) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2004.837349 · Cited by 599 · see citing works
Queuing with adaptive modulation and coding over wireless links: cross-Layer analysis and design
Liu, Q., Zhou, S., & Giannakis, G. B. (2005) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2005.847005 · Cited by 548 · see citing works
Shiang, H.-P., & van der Schaar, M. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.922851 · Cited by 194 · see citing works
End-to-End QoS for Video Delivery Over Wireless Internet
Zhang, Q., Zhu, W., & Zhang, Y. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2004.839603 · Cited by 161 · see citing works
A Decision-Theoretic Framework for Opportunistic Spectrum Access
Zhao, Q., & Swami, A. (2007) · IEEE Wireless Communications · doi:10.1109/mwc.2007.4300978 · Cited by 156 · see citing works
Using Feedback in Collaborative Reinforcement Learning to Adaptively Optimize MANET Routing
Dowling, J., Curran, E., Cunningham, R., & Cahill, V. (2005) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2005.846390 · Cited by 148 · see citing works
Analyzing and Optimizing Adaptive Modulation Coding Jointly With ARQ for QoS-Guaranteed Traffic
Wang, X., Liu, Q., & Giannakis, G. B. (2007) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2007.891465 · Cited by 124 · see citing works
Shiang, H.-P., & van der Schaar, M. (2007) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2007.070513 · Cited by 95 · see citing works
A joint scheduling, power control, and routing algorithm for ad hoc wireless networks
Li, Y., & Ephremides, A. (2006) · Ad Hoc Networks · doi:10.1016/j.adhoc.2006.04.005 · Cited by 90 · see citing works
Integrated voice/data call admission control for wireless DS-CDMA systems
Singh, S. S., Krishnamurthy, V., & Poor, H. V. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.1003071 · Cited by 88 · see citing works
Applications of Markov Decision Processes in Communication Networks
Altman, E. (2002) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-1-4615-0805-2_16 · Cited by 74 · see citing works
Network-assisted link adaptation with power control and channel reassignment in wireless networks
Krishnaswamy, D. (2002) · Medical Entomology and Zoology · Cited by 62 · see citing works
Cross-layer QoS Analysis of Opportunistic OFDM-TDMA and OFDMA Networks
Chang, Y.-J., Chien, F., & Kuo, C. ‐. J. (2007) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2007.070503 · Cited by 58 · see citing works
Applications of Markov Decision Processes in Communication Networks : a Survey
Altman, E. (2000) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 44 · see citing works
TPOT-RL Applied to Network Routing
Stone, P. (2000) · Cited by 29 · see citing works
A Feedback-Enhanced Learning Approach for Routing in WSN
Egorova-Foerster, A., & Murphy, A. L. (2011) · reroDoc Digital Library · Cited by 17 · see citing works
Implementation of IEEE 802.11e block acknowledgement policies based on the buffer size
Cabral, O., Segarra, A., & Velez, F. J. D. S. (2008) · European Wireless Conference · doi:10.1109/ew.2008.4623924 · Cited by 8 · see citing works
Optimal rate control in wireless networks with fading channels
Razavilar, J., Liu, K. J. R., & Marcus, S. I. (2003) · IEEE Vehicular Technology Conference · doi:10.1109/vetec.1999.778311 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches
Mammeri, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2913776 · Cited by 435 · see citing works
Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey
Alsheikh, M. A., Hoang, D. T., Niyato, D., Tan, H.-P., & Lin, S. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2420686 · Cited by 196 · see citing works
Markov decision processes with applications in wireless sensor networks: A survey
ALSHEIKH, A. M., Hoang, D. T., Niyato, D. T., & Tan, H.-P. (2015) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 189 · see citing works
Aouini, Z., & Pekár, A. (2022) · Computer Networks · doi:10.1016/j.comnet.2021.108719 · Cited by 122 · see citing works
Al‐Anbagi, I., Erol‐Kantarci, M., & Mouftah, H. T. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2363950 · Cited by 111 · see citing works
Toward Packet Routing With Fully Distributed Multiagent Deep Reinforcement Learning
You, X., Li, X., Xu, Y., Feng, H., Zhao, J., & Yan, H. (2020) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.3012832 · Cited by 109 · see citing works
Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing
Guo, W., Yan, C., & Lu, T. (2019) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147719833541 · Cited by 104 · see citing works
Application of reinforcement learning to routing in distributed wireless networks: a review
Al-Rawi, H. A. A., Ng, M. A., & Yau, K. A. (2013) · Artificial Intelligence Review · doi:10.1007/s10462-012-9383-6 · Cited by 103 · see citing works
HiveMind: Towards Cellular Native Machine Learning Model Splitting
Wang, S., Zhang, X., Uchiyama, H., & Matsuda, H. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3118403 · Cited by 90 · see citing works
Zhu, J., Song, Y., Jiang, D., & Song, H. H. (2016) · IEEE Access · doi:10.1109/access.2016.2600633 · Cited by 80 · see citing works
Reinforcement Learning for Self Organization and Power Control of Two-Tier Heterogeneous Networks
Amiri, R., Almasi, M. A., Andrews, J. G., & Mehrpouyan, H. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2919611 · Cited by 77 · see citing works
Self-Learning Power Control in Wireless Sensor Networks
Chincoli, M., & Liotta, A. (2018) · Sensors · doi:10.3390/s18020375 · Cited by 79 · see citing works
Improving Energy Efficiency and QoS of LPWANs for IoT Using Q-Learning Based Data Routing
Pandey, O. J., Yuvaraj, T., Paul, J. K., Nguyen, H. H., Gundepudi, K., & Shukla, M. K. (2021) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2021.3114147 · Cited by 41 · see citing works
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning
You, X., Li, X., Xu, Y., Feng, H., & Zhao, J. (2019) · International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) · doi:10.23919/wiopt47501.2019.9144110 · Cited by 39 · see citing works
Su, R., Gong, Z., Zhang, D., Li, C., Chen, Y., & Venkatesan, R. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3055065 · Cited by 35 · see citing works
Tang, F., Barolli, L., & Li, J. (2012) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2012.2228873 · Cited by 32 · see citing works
Bhavanasi, S. S., Pappone, L., & Esposito, F. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3287936 · Cited by 35 · see citing works
A Centralized Cross-Layer Protocol for Joint Power Control, Link Scheduling, and Routing in UWSNs
Yuan, Y., Zhuo, X., Liu, M., Wei, Y. D., & Qu, F. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3335849 · Cited by 26 · see citing works
Evaluating and Boosting Reinforcement Learning for Intra-Domain Routing
Xu, Q., Zhang, Y., Wu, K., Wang, J., & Lu, K. (2019) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass.2019.00039 · Cited by 26 · see citing works
Energy-Efficient Deadline-Constrained Maximum Reliability Forwarding in Lossy Networks
Zou, Z., Soldati, P., Zhang, H., & Johansson, M. (2012) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2012.090312.110938 · Cited by 23 · see citing works
Shen, Y., Huang, L., & Wu, X. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1059858 · Cited by 21 · see citing works
Nishanth, N., & Mujeeb, A. (2021) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2021.3055503 · Cited by 18 · see citing works
Zhu, J., Jiang, D., Shaowei, B., & Zhang, Y. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.03.104 · Cited by 17 · see citing works
Optimised cross-layer synchronisation schemes for wireless sensor networks
Nasri, N., Fradj, A. B., & Kachouri, A. (2017) · International Journal of Electronics · doi:10.1080/00207217.2017.1292555 · Cited by 17 · see citing works
Enhancement of Energy Conservation Technologies in Wireless Sensor Network
Thayananthan, V., & Alzranhi, A. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.07.052 · Cited by 15 · see citing works
Basics of coalitional games with applications to communications and networking
Shams, F., & Luise, M. (2013) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/1687-1499-2013-201 · Cited by 15 · see citing works
A Multi-Featured Actor-Critic Relay Selection Scheme for Large-Scale Energy Harvesting WSNs
Wang, T., Wu, S., Wang, Z., Jiang, Y., Ma, T., & Yang, Z. (2020) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2020.3030695 · Cited by 14 · see citing works
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement LearningPreprint
You, X., Li, X., Xu, Y., Feng, H., Zhao, J., & Yan, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.03494 · Cited by 13 · see citing works
A Reinforcement Routing Algorithm with Access Selection in the Multi–Hop Multi–Interface Networks
Jafari, A. H., & Shahhoseini, H. S. (2015) · Journal of Electrical Engineering · doi:10.1515/jee-2015-0011 · Cited by 12 · see citing works
Joint Routing and MAC Layer QoS-Aware Protocol for Wireless Sensor Networks
Arifuzzaman, M., Dobre, O. A., Ahmed, M. H., & Ngatched, T. M. N. (2016) · Globecom · doi:10.1109/glocom.2016.7841933 · Cited by 12 · see citing works
Characterization of Wireless Sensor Networks for Traffic & Delay
Waghole, D. S., & Deshpande, V. (2013) · International Conference on Cloud & Ubiquitous Computing & Emerging Technologies (CUBE) · doi:10.1109/cube.2013.57 · Cited by 11 · see citing works
Transmission Power Control in WSNs: From Deterministic to Cognitive Methods
Chincoli, M., & Liotta, A. (2017) · Internet of things · doi:10.1007/978-3-319-61300-0_3 · Cited by 11 · see citing works
Routing with Graph Convolutional Networks and Multi-Agent Deep Reinforcement Learning
Bhavanasi, S. S., Pappone, L., & Esposito, F. (2022) · IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) · doi:10.1109/nfv-sdn56302.2022.9974607 · Cited by 10 · see citing works
Al-Rawi, H. A. A., & Yau, K. A. (2013) · IEEE Symposium on Computational Intelligence for Communication Systems and Networks (CIComms) · doi:10.1109/cicomms.2013.6582850 · Cited by 10 · see citing works
Energy Efficiency Adaptation for Multihop Routing in Wireless Sensor Networks
Chehri, A., & Mouftah, H. T. (2012) · Journal of Computer Networks and Communications · doi:10.1155/2012/767920 · Cited by 10 · see citing works
Reinforcement learning models for scheduling in wireless networks
Yau, K. A., Kwong, K. H., & Shen, C. (2013) · Frontiers of Computer Science · doi:10.1007/s11704-013-2291-3 · Cited by 10 · see citing works
A Survey of Applying Reinforcement Learning Techniques to Multicast Routing
Ashour, O., St‐Hilaire, M., Kunz, T., & Wang, M. (2019) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon47517.2019.8993014 · Cited by 9 · see citing works
Intelligent Cross-Layer Protocol with traffic-differentiation-based QoS for wireless sensor networks
Haqbeen, J. A., Itō, T., Arifuzzaman, M., & Otsuka, T. (2016) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2016.7848176 · Cited by 7 · see citing works
Joint routing, MAC and physical layer protocol for wireless sensor networks
Haqbeen, J. A., Itō, T., Arifuzzaman, M., & Otsuka, T. (2017) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2017.8227992 · Cited by 6 · see citing works
Packet Routing in Dynamic Network: Deep Reinforcement Learning-Based Approach
Zheng, Z., & Zhou, Y. J. (2023) · International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) · doi:10.1109/iccss58421.2023.10270531 · Cited by 5 · see citing works
On Maximizing the Probability of Achieving Deadlines in Communication Networks
Becker, B., Oberli, C., Meuser, T., & Steinmetz, R. (2024) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan13010009 · Cited by 5 · see citing works
Crosslayer parameter configuration for TCP throughput improvement in cognitive radio networks
Hong, J., Cong-Bin, L., & Wu, C. (2013) · Acta Physica Sinica · doi:10.7498/aps.62.038804 · Cited by 4 · see citing works
Handover Game for Data Transportation over Dynamic UAV Networks with Predictable Channels
Li, B., & Chen, J. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000858 · Cited by 4 · see citing works
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
Yang, K., Shen, C., Yang, J., Yeh, S., & Sydir, J. (2023) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf59524.2023.10477008 · Cited by 4 · see citing works
In-node cognitive power control in Wireless Sensor Networks
Chincoli, M., & Liotta, A. (2017) · IEEE International Conference on Communications workshops · doi:10.1109/iccw.2017.7962805 · Cited by 3 · see citing works
Distributed No-Regret Learning for Multi-Stage Systems with End-to-End Bandit Feedback
Hou, I.-H. (2024) · ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) · doi:10.1145/3641512.3686369 · Cited by 3 · see citing works
Haqbeen, J. A., Itō, T., Arifuzzaman, M., & Otsuka, T. (2017) · The International journal of networked and distributed computing · doi:10.2991/ijndc.2017.5.3.5 · Cited by 3 · see citing works
Brahmi, I. H., Maire, F., Djahel, S., & Murphy, J. (2015) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc.2015.7343638 · Cited by 3 · see citing works
Haqbeen, J. A., Itō, T., Arifuzzaman, M., & Otsuka, T. (2017) · The International journal of networked and distributed computing · doi:10.2991/ijndc.2017.5.2.3 · Cited by 2 · see citing works
Zhang, L., Du, R., Hao, Z., Li, S., & Hu, Z. (2025) · International Journal of Communication Systems · doi:10.1002/dac.70039 · Cited by 2 · see citing works
Lifetime Optimization of Wireless Multi-hop Networks based on Network coding
Zhang, X. (2014) · Journal of Multimedia · doi:10.4304/jmm.9.3.469-475 · Cited by 2 · see citing works
Reliable Multicast Routing Protocol Based on Reinforcement Learning
Ashour, O., Kunz, T., & St‐Hilaire, M. (2023) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc56721.2023.10293864 · Cited by 2 · see citing works
EnRoute: A DQN based Energy Efficient Routing for URLLC in Next Generation Networks
Sharma, A., & Tripathi, S. (2023) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants59832.2023.10469593 · Cited by 1 · see citing works
Huang, J., Liu, G., Huang, T., Jiang, Y., & Wu, Y. (2026) · Ad Hoc Networks · doi:10.1016/j.adhoc.2026.104230 · Cited by 1 · see citing works
Analytical Model of Energy Saving Approach in Wireless Sensor Network
Thayananthan, V., & Bahazaq, F. (2014) · UKSim International Conference on Computer Modelling and Simulation (UKSim) · doi:10.1109/uksim.2014.94 · Cited by 1 · see citing works
Optimal Parameters Configuration for TCP Goodput Improvement in CR Networks
Liu, C., Jiang, H., Yang, Y., & Xu, G. (2012) · Spring Congress on Engineering and Technology · doi:10.1109/scet.2012.6342028 · Cited by 1 · see citing works
Energy Efficiency with Computational Topology and Routing in Wireless Sensors Networks
MohammedBahazaq, F., & Thayananthan, V. (2014) · International Journal of Computer Applications · doi:10.5120/15685-4548 · Cited by 1 · see citing works
ENERGY MANAGEMENT IN WIRELESS SENSOR NETWORKS: CROSS LAYER APPROACH BRIEF REVIEW
Sangeetha, N., & Binu, G. S. (2016) · Cited by 1 · see citing works
Yousefi, H. H. N., Kavian, Y. S., & Mahmoudi, A. (2019) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700855 · Cited by 1 · see citing works
Collaborative Transmission over Intermediate Links in Duty-Cycle WSNs
Chen, Q., Xia, X., Li, Z., & Zheng, Y. (2021) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads53394.2021.00111 · Cited by 1 · see citing works
Sha, Y., Hu, J., Hao, S., & Wang, D. (2022) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2022.09.010 · Cited by 1 · see citing works
Reinforcement Learning in Self Organizing Cellular Networks
Amiri, R. (2020) · doi:10.18122/td/1696/boisestate · Cited by 1 · see citing works
Energy Dissipation Balance Scheme in Dynamic Ad Hoc Networks
Fang, Y., Shi, J., Sun, Z., Kong, S., & Wang, S. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-3-662-49831-6_8
Adaptive Packet Routing on Communication Networks Based on Reinforcement Learning
Deeka, T., Deeka, B., & On-rit, S. (2019) · Lecture notes in networks and systems · doi:10.1007/978-3-030-12385-7_12
Zhang, R. (2022) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct56141.2022.10073446
Distributed Learning for Multi-Stage Bandits: No-Regret Algorithm and Lower Bound
Hou, I.-H. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3648382
- Authors
- Lin, Z., & van der Schaar, M.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2011-01
- Volume
- 10
- Issue
- 1
- Pages
- 102-113
- ISSN
- 1536-1276 (ISSN-L)
- OpenAlex
- W2103724767
- Cited by
- 68
- References
- 30
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 6 · 2022: 6 · 2021: 5 · 2020: 3 · 2019: 9 · 2018: 2 · 2017: 6 · 2016: 6 · 2015: 4 · 2014: 5 · 2013: 6 · 2012: 4
- FWCI
- 2.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Wireless Communications (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Keywords
- multi-hop wireless networks, delay-sensitive applications, distributed reinforcement learning, Markov decision process, network utility maximization, dynamic environments, transmission power control
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