On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm

Fu, Yaru; Doan, Khai Nguyen; Quek, Tony Q. S.

doi:10.1016/j.dcan.2020.06.005

Cited by 19 publications

(10 citation statements)

References 14 publications

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“…Fu et al [8] introduced an AI-based framework of 6G Recommendation-aware Content Caching (RCC). On the other hand, the authors introduced a general framework without studying the performance of a specific optimization algorithm.…”

Section: A the Literature Of 6g Optimizationmentioning

confidence: 99%

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

et al. 2021

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The next generation 6G communication network is typically characterized by the full connectivity and coverage of Users Equipment (UEs). This leads to the need for moving beyond the traditional twodimensional (2D) coverage service to the three-dimensional (3D) full-service one. The 6G 3D architecture leverages different types of non-terrestrial or aerial nodes that can act as mobile Base Stations (BSs) such as Unmanned Aerial Vehicles (UAVs), Low Altitude Platforms (LAPs), High-Altitude Platform Stations (HAPSs), or even Low Earth Orbit (LEO) satellites. Moreover, aided technologies have been added to the 6G architecture to dynamically increase its coverage efficiency such as the Reconfigurable Intelligent Surfaces (RIS). In this paper, an enhanced Computational Intelligence (CI) algorithm is introduced for optimizing the coverage of UAV-BSs with respect to their location from RIS in the 3D space of 6G architecture. The regarded problem is formulated as a constrained 3D coverage optimization problem. In order to increase the convergence of the proposed algorithm, it is hybridized with a crossover operator. For the validation of the proposed method, it is tested on different scenarios with large-scale coordinates and compared with many recent and hybrid CI algorithms, as Slime Mould Algorithm (SMA), Lévy Flight Distribution (LFD), hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and hybrid Grey Wolf Optimizer and Cuckoo Search (GWOCS). The experiment and the statistical analysis show the significant efficiency of the proposed algorithm in achieving complete coverage with a lower number of UAV-BSs and without constraints violation.INDEX TERMS 6G technology, computational intelligence, non-terrestrial base stations, reconfigurable intelligent surfaces, three-dimensional coverage optimization problem.

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Section: A the Literature Of 6g Optimizationmentioning

confidence: 99%

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

et al. 2021

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“…In general, the works that studied content caching and recommendation can be classified into two categories. In the first category, content recommendation is utilized to shape the requests and steer the content demand toward the contents that are both stored in the cache and interesting to the users [4], [5], [19], [20], [21], [22], [23], [24], [25]. In [4], [5] a preference "distortion" tolerance measure is used to quantify how much the engineered recommendations distort the original user content preferences.…”

Section: Related Workmentioning

confidence: 99%

Optimal Content Caching and Recommendation with Age of Information

Ahani¹,

Yuan²

2021

Preprint

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Content caching at the network edge has been considered an effective way of mitigating backhaul load and improving user experience. Caching efficiency can be enhanced by content recommendation and by keeping the information fresh. To the best of our knowledge, there is no work that jointly takes into account these aspects. By content recommendation, a requested content that is not in the cache can be alternatively satisfied by a related cached content recommended by the system. Information freshness can be quantified by age of information (AoI). We address, optimal scheduling of cache updates for a time-slotted system accounting for content recommendation and AoI. For each content, there are requests that need to be satisfied, and there is a cost function capturing the freshness of information. We present the following contributions. First, we prove that the problem is NP-hard. Second, we derive an integer linear formulation, by which the optimal solution can be obtained for small-scale scenarios. Third, we develop an algorithm based on Lagrangian decomposition. Fourth, we develop efficient algorithms for solving the resulting subproblems. Our algorithm computes a bound that can be used to evaluate the performance of any suboptimal solution. Finally, we conduct simulations to show the effectiveness of our algorithm in comparison to a greedy schedule.

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“…The complete information and awareness of the environment comes at the cost of a high volume of data, variety of sources and signi icant processing [80,82]. This necessitates the use of big-data processing techniques [122].…”

Section: Integrated Sensing and Communicationsmentioning

confidence: 99%

6G vision: An ultra-flexible perspective

Arslan¹,

Doğan-Tusha²,

Yazar³

2020

ITU J-FET

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The upcoming sixth generation (6G) communications systems are expected to support an unprecedented variety of applications, pervading every aspect of human life. It is clearly not possible to fulfill the service requirements without actualizing a plethora of flexible options pertaining to the key enabler technologies themselves. At that point, this work presents an overview of the potential 6G key enablers from the flexibility perspective, categorizes them, and provides a general framework to incorporate them in the future networks. Furthermore, the role of artificial intelligence and integrated sensing and communications as key enablers of the presented framework is also discussed.

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On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm

Cited by 19 publications

References 14 publications

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

Optimal Content Caching and Recommendation with Age of Information

6G vision: An ultra-flexible perspective

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