Deep Learning for Radio Resource Allocation With Diverse Quality-of-Service Requirements in 5G

Dong, Rui; She, Changyang; Hardjawana, Wibowo; Li, Yonghui; Vucetic, Branka

doi:10.1109/twc.2020.3041319

Cited by 78 publications

(55 citation statements)

References 52 publications

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“…Based on the universal approximation theorem of artificial deep neural networks (DNN) (Hornik et al, 1989), a promising approach to solve cross-layer optimization problems is to use DNNs to approximate the optimal policies. Such a concept has been validated in the existing literature and has the potential to be implemented in real-world systems (Dong et al, 2021;Gu et al, 2021).…”

Section: Cross-layer Optimizationmentioning

confidence: 99%

Grand Challenges in Signal Processing for Communications

She

Cheng

et al. 2021

Front. Signal Process.

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Section: Cross-layer Optimizationmentioning

confidence: 99%

Grand Challenges in Signal Processing for Communications

She

Cheng

et al. 2021

Front. Signal Process.

Self Cite

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“…They leveraged a multi-armed-bandit method to allocate resources to slices to meet end-to-end latency requirements. Current networks are non-stationary in general and therefore, pre-trained networking models require fine-tuning to correct the model-mismatch problem, as highlighted in [19].…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Cooperative Learning for Disaggregated Delay Modeling in Multidomain Networks

Tabatabaeimehr

Ruiz

Liu

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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Accurate delay estimation is one of the enablers of future network connectivity services, as it facilitates the application layer to anticipate network performance. If such connectivity services require isolation (slicing), such delay estimation should not be limited to a maximum value defined in the Service Level Agreement, but to a finer-grained description of the expected delay in the form of, e.g., a continuous function of the load. Obtaining accurate end-to-end (e2e) delay modeling is even more challenging in a multi-operator (Multi-AS) scenario, where the provisioning of e2e connectivity services is provided across heterogeneous multi-operator (Multi-AS or just domains) networks. In this work, we propose a collaborative environment, where each domain Software Defined Networking (SDN) controller models intra-domain delay components of inter-domain paths and share those models with a broker system providing the e2e connectivity services. The broker, in turn, models the delay of inter-domain links based on e2e monitoring and the received intra-domain models. Exhaustive simulation results show that composing e2e models as the summation of intra-domain network and inter-domain link delay models provides many benefits and increasing performance over the models obtained from e2e measurements.

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“…У зв'язку зі збільшенням кількості нових пристроїв, наприклад пристроїв за технологіями D2D [15][16][17][18], M2M [19][20][21], IoT [22,23], OFDM [24][25][26][27], поляризаційне рознесення та просторове рознесення [50][51][52][53], у мережі, спостерігається зростання трафіка, що призводить до погіршення якості [54][55][56]. Для покращення якості обслуговування (QoS) було розроблено різноманітні механізми для провідних та безпровідних мереж.…”

Section: вступunclassified

Контроль Трафіку У Гетерогенних Мережа 5g

Piltyay¹

2021

Preprint

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Deep Learning for Radio Resource Allocation With Diverse Quality-of-Service Requirements in 5G

Cited by 78 publications

References 52 publications

Grand Challenges in Signal Processing for Communications

Grand Challenges in Signal Processing for Communications

Cooperative Learning for Disaggregated Delay Modeling in Multidomain Networks

Контроль Трафіку У Гетерогенних Мережа 5g

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