Proactive Caching Strategies in Heterogeneous Networks With Device-to-Device Communications

Quer, Giorgio; Pappalardo, Irene; Rao, Bhaskar D.; Zorzi, Michele

doi:10.1109/twc.2018.2840976

Cited by 34 publications

(32 citation statements)

References 40 publications

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“…By utilizing the normalization condition Π1 = 1, we obtain π 0 as Equation (16). By substituting Equation (16) into Equation (19), the general formula π i is derived as Equation (17).…”

Section: Lemmamentioning

confidence: 99%

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Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks

et al. 2020

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In this paper, we focus on the performance analysis of device-to-device (D2D) underlay communication in cellular networks. First, we develop a spatiotemporal traffic model to model a retransmission mechanism for D2D underlay communication. The D2D users in backlogged statuses are modeled as a thinned Poisson point process (PPP). To capture the characteristics of sporadic wireless data generation and limited buffer, we adopt queuing theory to analyze the performance of dynamic traffic. Furthermore, a feedback queuing model is adopted to analyze the performance with retransmission strategy. With the consideration of interference and channel fading, the service probability of the queue departure process is determined by the received signal-to-interference-plus-noise ratio (SINR). Then, the embedded Markov chain is employed to depict the queuing status in the D2D user buffer. We compute its steady-state distribution and derive the closed-form expressions of performance metrics, namely the average queue length, average throughput, average delay, and dropping probability. Simulation results show the validity and rationality of the theoretical analysis with different channel parameters and D2D densities. In addition, the simulation explores the dropping probability of a D2D user with and without the retransmission strategy for different D2D links in the system. When the arrival rate is comparatively high, the optimal throughput is reached after fewer retransmission attempts as a result of the limited buffer.

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“…By utilizing the normalization condition Π1 = 1, we obtain π 0 as Equation (16). By substituting Equation (16) into Equation (19), the general formula π i is derived as Equation (17).…”

Section: Lemmamentioning

confidence: 99%

“…Several studies on performance analysis of D2D communications using the queuing model have been reported in the literature [14][15][16]. Existing works have also built the spatiotemporal model simultaneously based on the queuing theory and stochastic geometry (e.g., [17][18][19]) .…”

mentioning

confidence: 99%

Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks

et al. 2020

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“…In order to make problem (P1) tractable, we propose to substitute iteratively the products of the binary variables in function G u,f by new single variables [29], [32]. The resulting new problem (P2) can be expressed by:…”

Section: A Optimal Solution Designmentioning

confidence: 99%

“…Also, [29] focused on caching, and ignored the effects of wireless channels and transmit power conditions. In [30], only interference-free channels and probabilistic bandwidth allocation are assumed.…”

mentioning

confidence: 99%

Joint Caching and Resource Allocation in D2D-Assisted Heterogeneous Networks

Jaafar

Ajib

Elbiaze

2018

2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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5G networks are required to provide very fast and reliable communications while dealing with the increase of users traffic. In Heterogeneous Networks (HetNets) assisted with Device-to-Device (D2D) communication, traffic can be offloaded to Small Base Stations or to users to improve the network's successful data delivery rate. In this paper, we aim at maximizing the average number of files that are successfully delivered to users, by jointly optimizing caching placement and channel allocation in cache-enabled D2D-assisted HetNets. At first, an analytical upper-bound on the average content delivery delay is derived. Then, the joint optimization problem is formulated. The non-convexity of the problem is alleviated, and the optimal solution is determined. Due to the high time complexity of the obtained solution, a low-complex sub-optimal approach is proposed. Numerical results illustrate the efficacy of the proposed solutions and compare them to conventional approaches. Finally, by investigating the impact of key parameters, e.g. power, caching capacity, QoS requirements, etc., guidelines to design these networks are obtained. Index TermsHetNet, Device-to-Device (D2D), caching, channel allocation, successful delivery rate (SDR).

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“…They also investigated the gain of optimizing the caching policy by learning user preferences over content popularity. The authors in [4] optimized the caching strategies of the users and small base stations to minimize a system cost in a deviceto-device heterogeneous network. Differently from [3] and [4], we assume that the content popularity is unknown.…”

Section: Introductionmentioning

confidence: 99%

Online Caching Policy with User Preferences and Time-Dependent Requests: A Reinforcement Learning Approach

Hatami

Leinonen

Codreanu

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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Content caching is a promising approach to reduce data traffic in the back-haul links. We consider a system where multiple users request items from a cache-enabled base station that is connected to a cloud. The users request items according to the user preferences in a time-dependent fashion, i.e., a user is likely to request the next chunk (item) of the file requested at a previous time slot. Whenever the requested item is not in the cache, the base station downloads it from the cloud and forwards it to the user. In the meanwhile, the base station decides whether to replace one item in the cache by the fetched item, or to discard it. We model the problem as a Markov decision process (MDP) and propose a novel state space that takes advantage of the dynamics of the users' requests. We use reinforcement learning and propose a Q-learning algorithm to find an optimal cache replacement policy that maximizes the cache hit ratio without knowing the popularity profile distribution, probability distribution of items, and user preference model. Simulation results show that the proposed algorithm improves the cache hit ratio compared to other baseline policies.

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Proactive Caching Strategies in Heterogeneous Networks With Device-to-Device Communications

Cited by 34 publications

References 40 publications

Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks

Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks

Joint Caching and Resource Allocation in D2D-Assisted Heterogeneous Networks

Online Caching Policy with User Preferences and Time-Dependent Requests: A Reinforcement Learning Approach

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