Cache Enabled Cellular Network: Algorithm for Cache Placement and Guarantees

Krishnendu, S.; Bharath, B. N.; Bhatia, Vimal

doi:10.1109/lwc.2019.2926726

Cited by 19 publications

(14 citation statements)

References 13 publications

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“…The optimal cache content problem turns out to be a combinatorial problem and hence results in NP-hard problem. In [13], the authors proposed a epsilon close-to-optimal caching solution and also show that the proposed algorithm performs better with respect to average cache hit and average delay.…”

Section: A Small Base Station Cachingmentioning

confidence: 99%

Wireless Edge Caching and Content Popularity Prediction Using Machine Learning

Krishnendu

Bharath

Bhatia

et al. 2024

IEEE Consumer Electron. Mag.

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Section: A Small Base Station Cachingmentioning

confidence: 99%

Wireless Edge Caching and Content Popularity Prediction Using Machine Learning

Krishnendu

Bharath

Bhatia

et al. 2024

IEEE Consumer Electron. Mag.

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“…We use deterministic caching model which is commonly used in cache-enabled networks [31,32]. To take the full advantages of caching capacity, we transform the constraint F of binary variables q k f to equality, which is ∑ F f =1 q k f = C k .…”

Section: Offline Content Placementmentioning

confidence: 99%

Deep Reinforcement Learning-Based Content Placement and Trajectory Design in Urban Cache-Enabled UAV Networks

Shi

et al. 2020

Wireless Communications and Mobile Computing

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Cache-enabled unmanned aerial vehicles (UAVs) have been envisioned as a promising technology for many applications in future urban wireless communication. However, to utilize UAVs properly is challenging due to limited endurance and storage capacity as well as the continuous roam of the mobile users. To meet the diversity of urban communication services, it is essential to exploit UAVs’ potential of mobility and storage resource. Toward this end, we consider an urban cache-enabled communication network where the UAVs serve mobile users with energy and cache capacity constraints. We formulate an optimization problem to maximize the sum achievable throughput in this system. To solve this problem, we propose a deep reinforcement learning-based joint content placement and trajectory design algorithm (DRL-JCT), whose progress can be divided into two stages: offline content placement stage and online user tracking stage. First, we present a link-based scheme to maximize the cache hit rate of all users’ file requirements under cache capacity constraint. The NP-hard problem is solved by approximation and convex optimization. Then, we leverage the Double Deep Q-Network (DDQN) to track mobile users online with their instantaneous two-dimensional coordinate under energy constraint. Numerical results show that our algorithm converges well after a small number of iterations. Compared with several benchmark schemes, our algorithm adapts to the dynamic conditions and provides significant performance in terms of sum achievable throughput.

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“…where the regret is as defined in (7). If the caching strategy used is C * b,t , then, the above implies that…”

Section: Now By Adding and Subtractingmentioning

confidence: 99%

“…A new paradigm to handle this data clogging is through caching in the cellular networks. Caching can reduce the peak traffic by prefetching popular contents into memories at the small-cell Base Stations (sBSs) [5], [6], [7]. Past works in caching include the classical work from the point-of-view of information theory by Neisen et al [8] (also, see [9]), combinatorial optimization approach [10], energy efficient caching of files in a Device-to-Device (D2D) network (see [10] - [12]), and proactive caching strategy, as in [13].…”

Section: Introductionmentioning

confidence: 99%

Learning to Cache: Distributed Coded Caching in a Cellular Network With Correlated Demands

Krishnendu,

Bharath,

Garg

et al. 2020

Preprint

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Design of distributed caching mechanisms is considered as an active area of research due to its promising solution in reducing data load in the backhaul link of a cellular network. In this paper, the problem of distributed content caching in a small-cell Base Stations (sBSs) wireless network that maximizes the cache hit performance is considered. Most of the existing works focus on static demands, however, here, data at each sBS is considered to be correlated across time and sBSs. The caching strategy is assumed to be a weighted combination of past caching strategies. A high probability generalization guarantees on the performance of the proposed caching strategy is derived. The theoretical guarantee provides following insights on obtaining the caching strategy: (i) run regret minimization at each sBS to obtain a sequence of caching strategies across time, and (ii) maximize an estimate of the bound to obtain a set of weights for the caching strategy which depends on the discrepancy. Also, theoretical guarantee on the performance of the LRFU caching strategy is derived. Further, federated learning based heuristic caching algorithm is also proposed. Finally, it is shown through simulations using Movie Lens dataset that the proposed algorithm significantly outperforms LRFU algorithm.

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Cache Enabled Cellular Network: Algorithm for Cache Placement and Guarantees

Cited by 19 publications

References 13 publications

Wireless Edge Caching and Content Popularity Prediction Using Machine Learning

Wireless Edge Caching and Content Popularity Prediction Using Machine Learning

Deep Reinforcement Learning-Based Content Placement and Trajectory Design in Urban Cache-Enabled UAV Networks

Learning to Cache: Distributed Coded Caching in a Cellular Network With Correlated Demands

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