Blind Optimal User Association in Small-Cell Networks

Chatzieleftheriou, Livia Elena; Destounis, Apostolos; Paschos, Georgios S.; Koutsopoulos, Iordanis

doi:10.1109/infocom42981.2021.9488867

Cited by 2 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [23], a proposal was presented to alter user association in overlapped ultra-dense networks. Furthermore, [24] learned user association policies to direct traffic from different locations to transmitters, minimizing total delay and load on the wireless downlink in the presence of unknown dynamic traffic demand. Network slicing is a key innovation to meet diverse needs.…”

Section: B Traffic Steering In Traditional Ran Architecturementioning

confidence: 99%

Empowering Traffic Steering in 6G Open RAN with Deep Reinforcement Learning

Kavehmadavani,

Nguyen,

et al. 2024

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

The sixth-generation (6G) wireless network landscape is evolving toward enhanced programmability, virtualization, and intelligence to support heterogeneous use cases. The O-RAN Alliance is pivotal in this transition, introducing a disaggregated architecture and open interfaces within the 6G network. Our paper explores an intelligent traffic steering (TS) scheme within the Open radio access network (RAN) architecture, aimed at improving overall system performance. Our novel TS algorithm efficiently manages diverse services, improving shared infrastructure performance amid unpredictable demand fluctuations. To address challenges like varying channel conditions, dynamic traffic demands, we propose a multi-layer optimization framework tailored to different timescales. Techniques such as long-short-term memory (LSTM), heuristics, and multi-agent deep reinforcement learning (MADRL) are employed within the non-real-time (non-RT) RAN intelligent controller (RIC). These techniques collaborate to make decisions on a larger timescale, defining custom control applications such as the intelligent TS-xAPP deployed at the near-real-time (near-RT) RIC. Meanwhile, optimization on a smaller timescale occurs at the RAN layer after receiving inferences/policies from RICs to address dynamic environments. The simulation results confirm the system's effectiveness in intelligently steering traffic through a slice-aware scheme, improving eMBB throughput by an average of 99.42% over slice isolation.

show abstract

Section: B Traffic Steering In Traditional Ran Architecturementioning

confidence: 99%

Empowering Traffic Steering in 6G Open RAN with Deep Reinforcement Learning

Kavehmadavani,

Nguyen,

et al. 2024

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…Moreover, in order to avoid the projection step of ( 2), the regularization function can be used by exploiting the geometry of the feasible space. For example, the work [10] aimed to find solutions that lie in sets that are the combinations of unit simplices. A possible regularization function R(•) for each simplex in the feasible space is the Gibbs-Shannon entropy.…”

Section: B Online Mirror Descent 1) Online Mirror Descent Algorithmmentioning

confidence: 99%

“…The work [10] introduced a novel periodic benchmark, which is particularly useful for performance comparison in the case of conjectured periodicity in λ(t). The benchmark is called Optimal Periodic Static (OPS).…”

Section: Metrics and Benchmarksmentioning

confidence: 99%

“…Among the online approaches, OCO is a powerful one for solving the problems, e.g., resource allocation, computation offloading, and user association, without knowing the current network status [7]- [10]. More specifically, relying only on the most recent historical values of the system parameters, the OCO framework decides the action for the current time instant before the actual parameter values are revealed and incorporated with the action to obtain a cost or utility.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online Learning for Industrial IoT: The Online Convex Optimization Perspective

Chatzieleftheriou

Liu

Koutsopoulos

et al. 2022

2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)

View full text Add to dashboard Cite

Industrial Internet of things (IIoT), one enabler for Industry 4.0 Smart Factories, is a mission-critical and latencysensitive application of 5G networks. Due to the stringent latency requirements in IIoT, coordinating the simultaneous transmissions of massive entities and knowing the interference they create to each other is not feasible. Additionally, due to the mobility feature of mobile robots and automated guided vehicles, the experienced channel fading may differ from the estimated one. Therefore, some uncertainties exist in IIoT networks while we decide the communication and control mechanisms. Within the context of IIoT, this paper discusses some resource allocation solutions from the perspective of Online Convex Optimization (OCO). OCO is a computationally lightweight and memory-efficient mathematical tool which tackles the optimization problems, given that the network environment is arbitrary and unknown. We first introduce the key performance indicators in IIoT networks and highlight the uncertain factors, which we may encounter while allocating the communication resources in IIoT. Then we provide an overview of main principles of OCO and present the comparison benchmarks and related metrics for performance evaluation. Moreover, we discuss the kind of resource allocation problems in IIoT that can be tackled by OCO. Finally, we summarize the advantages of applying OCO to IIoT networks.

show abstract

Blind Optimal User Association in Small-Cell Networks

Cited by 2 publications

References 15 publications

Empowering Traffic Steering in 6G Open RAN with Deep Reinforcement Learning

Empowering Traffic Steering in 6G Open RAN with Deep Reinforcement Learning

Online Learning for Industrial IoT: The Online Convex Optimization Perspective

Contact Info

Product

Resources

About