Robust User Association for Ultra Dense Networks

Liakopoulos, Nikolaos; Paschos, Georgios S.; Spyropoulos, Thrasyvoulos

doi:10.1109/infocom.2018.8485896

Cited by 15 publications

(12 citation statements)

References 17 publications

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“…In [6] the dynamic load balancing is studied by capturing the system state with fluid equations, and an asymptotically optimal simple myopic strategy is presented. The authors in [7] predict future traffic based on the traffic history by using robust optimization tools and propose an iterative UA technique that minimizes costs.…”

Section: Related Work User Association (Ua)mentioning

confidence: 99%

“…A simple three-step scheme that sequentially performs a preference-aware UA with service guarantees, a recommendationaware cache placement, and an adjustment of content recommendations, reveals the gains that can be achieved when UA is considered jointly with content caching and recommendations (as introduced in [13]). Despite their interesting results, works [2]- [5], [8]- [12] consider only static UA instances, work [6] focuses on load balancing, and work [7] performs complex computations on the historical traffic.…”

Section: Related Work User Association (Ua)mentioning

confidence: 99%

“…Our network architecture consists of 40 BS, most of them being close to the city center, where the load is higher. We follow the setup of [7], and consider Macro-and Micro-BSs transmitting at P M = 43dBm and P m = 33dBm, respectively. The system bandwidth is W = 10MHz, while the noise density is N 0 = −174dBm/MHz.…”

Section: Numerical Evaluation a System Architecture And Traffic Demandmentioning

confidence: 99%

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Blind Optimal User Association in Small-Cell Networks

Chatzieleftheriou¹,

Destounis²,

Paschos³

et al. 2021

Preprint

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We learn optimal user association policies for traffic from different locations to Access Points(APs), in the presence of unknown dynamic traffic demand. We aim at minimizing a broad family of α-fair cost functions that express various objectives in load assignment in the wireless downlink, such as total load or total delay minimization. Finding an optimal user association policy in dynamic environments is challenging because traffic demand fluctuations over time are non-stationary and difficult to characterize statistically, which obstructs the computation of costefficient associations. Assuming arbitrary traffic patterns over time, we formulate the problem of online learning of optimal user association policies using the Online Convex Optimization (OCO) framework. We introduce a periodic benchmark for OCO problems that generalizes state-of-the-art benchmarks. We exploit inherent properties of the online user association problem and propose PerOnE, a simple online learning scheme that dynamically adapts the association policy to arbitrary traffic demand variations. We compare PerOnE against our periodic benchmark and prove that it enjoys the no-regret property, with additional sublinear dependence of the network size. To the best of our knowledge, this is the first work that introduces a periodic benchmark for OCO problems and a no-regret algorithm for the online user association problem. Our theoretical findings are validated through results on a real-trace dataset.

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Section: Related Work User Association (Ua)mentioning

confidence: 99%

Section: Related Work User Association (Ua)mentioning

confidence: 99%

Section: Numerical Evaluation a System Architecture And Traffic Demandmentioning

confidence: 99%

See 1 more Smart Citation

Blind Optimal User Association in Small-Cell Networks

Chatzieleftheriou¹,

Destounis²,

Paschos³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This model has been extensively used in the literature, cf. [7]- [9]. Here we use it only as an example; our framework only requires that R ij are independent of the association variables.…”

Section: A Downlink Modelmentioning

confidence: 99%

“…In the context of continuous device association, past work has considered the optimization of α-optimal functions [7], [8] or general convex functions [9]. None of the past approaches can be used to solve (1), which is non-convex.…”

Section: Related Workmentioning

confidence: 99%

Computational Optimal Transport for 5G Massive C-RAN Device Association

Paschos

Liakopoulos

Debbah

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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The massive scale of future wireless networks will create computational bottlenecks in performance optimization. In this paper, we study the problem of connecting mobile traffic to Cloud RAN (C-RAN) stations. To balance station load, we steer the traffic by designing device association rules. The baseline association rule connects each device to the station with the strongest signal, which does not account for interference or traffic hot spots, and leads to load imbalances and performance deterioration. Instead, we can formulate an optimization problem to decide centrally the best association rule at each time instance. However, in practice this optimization has such high dimensions, that even linear programming solvers fail to solve. To address the challenge of massive connectivity, we propose an approach based on the theory of optimal transport, which studies the economical transfer of probability between two distributions. Our proposed methodology can further inspire scalable algorithms for massive optimization problems in wireless networks.

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