Towards Cooperative Data Rate Prediction for Future Mobile and Vehicular 6G Networks

Sliwa, Benjamin; Falkenberg, Robert; Wietfeld, Christian

doi:10.1109/6gsummit49458.2020.9083767

Cited by 53 publications

(42 citation statements)

References 22 publications

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“…In future work, we want to extend BS-CB with a multi-MNO strategy which allows dynamic network selection for compensating black spots regions. In addition, we plan to further analyze cooperative approacheswhere the network infrastructure actively distributes network load information to the mobile clients [16] -for data rate prediction in order to optimize the resulting accuracy. Moreover, we aim to move another step forward towards zero touch optimization through integration of online learning mechanisms for the data rate prediction.…”

Section: Resultsmentioning

confidence: 99%

Acting selfish for the good of all

Sliwa

Adam

Wietfeld

2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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In this work, we present Black Spot-aware Contextual Bandit (BS-CB) as a novel client-based method for resource-efficient opportunistic transmission of delay-tolerant vehicular sensor data. BS-CB applies a hybrid approach which brings together all major machine learning disciplines-supervised, unsupervised, and reinforcement learning-in order to autonomously schedule vehicular sensor data transmissions with respect to the expected resource efficiency. Within a comprehensive real world performance evaluation in the public cellular networks of three Mobile Network Operators (MNOs), it is found that 1) The average uplink data rate is improved by 125%-195% 2) The apparently selfish goal of data rate optimization reduces the amount of occupied cell resources by 84%-89% 3) The average transmission-related power consumption can be reduced by 53%-75% 4) The price to pay is an additional buffering delay due to the opportunistic medium access strategy.

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Section: Resultsmentioning

confidence: 99%

Acting selfish for the good of all

Sliwa

Adam

Wietfeld

2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

Self Cite

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“…These data rates are still not enough for some applications. Therefore, we require the development of some standard and communication protocols that have data rates in the range of 10-100Gbps [107].…”

Section: Peak Data Ratementioning

confidence: 99%

“…For instance, if we are moving in the airplanes or high-speed bullet trains, the communication should not be disturbed, and data rates should be maintained. The mobility requirements for 6G, as defined by ITU, is >1000Km/hr [107].…”

Section: Mobilitymentioning

confidence: 99%

The shift to 6G communications: vision and requirements

Akhtar

Hassan

Ghaffar

et al. 2020

Hum. Cent. Comput. Inf. Sci.

217

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The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artificial intelligence (AI)/machine learning (ML), quantum communication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realization of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More specifically, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.

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“…RAT selection is based on call quality, cost of making a call, power consumption at UE, and handover rate offered by base stations from each RAT. Work has been done for cooperative network prediction by fusing knowledge from the client and network domains [17]. The paper presents a proof-of-concept for 6G networks, arguing that better data rates could be achieved by cooperatively involving UEs and network devices to make machine learning-based network optimization decisions compared to network-centric approaches.…”

Section: Controller and Clientmentioning

confidence: 99%

Beyond 5G: Hybrid End-to-End Quality of Service Provisioning in Heterogeneous IoT Networks

et al. 2020

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A significant increase is expected in video and multimedia traffic in Beyond 5G networks. The inclusion of a huge number of IoT nodes in Beyond 5G networks further complicates the design of such networks. These futuristic networks are expected to deal with this increased traffic and number of nodes while ensuring that network delays do not exceed a certain threshold. In such networks, Quality of Service (QoS) provisioning has become vital, not only to guarantee certain key performance indicators but also to improve user experience. This paper proposes a hybrid approach for end-to-end QoS provisioning, involving both clients and controllers to address these challenges. Each client tries to satisfy its own access QoS requirements by choosing optimal access device(s) and makes decisions based on locally available view. Controllers are then responsible for finding optimal paths in the core network to satisfy client core QoS requirements. Experimental results show that the proposed approach provides better QoS guarantees than several other access device selection and routing schemes.

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Towards Cooperative Data Rate Prediction for Future Mobile and Vehicular 6G Networks

Cited by 53 publications

References 22 publications

Acting selfish for the good of all

Acting selfish for the good of all

The shift to 6G communications: vision and requirements

Beyond 5G: Hybrid End-to-End Quality of Service Provisioning in Heterogeneous IoT Networks

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