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Cumulative distribution function for average total interference to the Base station, for maximum interference limit of I max = 0.25.
Source publication
In this paper, cache placement problem in cellular networks with device to device communications (D2D) is addressed. D2D transmission allows cellular bandwidth reuse which necessitates interference management. We propose a distributed algorithm, where the base station rewards the user terminals for caching content and helping other nearby terminals...
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Citations
... Generally, caching schemes may be divided into centralized or distributed. In the centralized approach, a central device manages resource allocation and caching decisions [13], [14], while in distributed caching each device independently decides which file to cache [15]- [17]. Golrezaei et al. [18], [19], [13] proposed a caching strategy where the cell is split into smaller square clusters. ...
... repeat 8: Update P dt d = P dt d − ; 9: Calculate the D2D pair new rate R d using (10); 10: Calculate the CU new rate R c using (10); 11: 13: repeat 14: Update P dt d = P dt d − ; 15: Calculate the D2D pair new rate R d using (10); 16: Calculate the CU new rate R c using (10); 17: ...
Device-to-Device (D2D) communication is a novel approach that has been proposed to enhance the performance of traditional cellular networks. This work focuses on video streaming using D2D communications underlaying a Long Term Evolution (LTE) network where resources are allocated to each D2D pair in three modes of operation; cellular, dedicated and reuse. The objective is to maximize the sum-rate of the D2D pairs while maintaining continuous video playback with an acceptable quality level. Unlike previous works, which assign one channel to each user or divide the available resources equally between them or assume constant bit rate streaming (CBR), we estimate the number of Resource Blocks (RBs) needed for all users according to their rate and Signal-to-Noise Ratio (SNR) requirements and consider Scalable Video Coding (SVC). A complete Quality of Experience (QoE)-aware framework for D2D-based video streaming that includes resource allocation, power control, mode selection and SVC layer selection is then proposed while taking into consideration buffer occupancies and the video peak signal-to-noise ratio (PSNR). The efficacy of the proposed scheme is evaluated through extensive simulations and compared to the scenario where only one mode of operation is assigned to all D2D pairs. Furthermore, the effect of the number of D2D users as well as their mobility is also investigated. Simulation results show that the proposed framework improves the throughput of the D2D pairs when compared to a single mode of operation while at the same time providing better video quality and continuity.
... In probabilistic caching, on the other hand, each user decides independently on which files to be cached [26]- [28]. This caching scheme is suitable for users with high mobility and it reduces the signaling overhead, however, it could lower the caching efficiency and leads to overlap and duplication of cache contents [30]- [32]. In the following subsection, we discuss and compare both probabilistic and deterministic caching schemes. ...
... They also proposed two algorithms to match between sellers and buyers with the objective of reducing the energy consumption at the BS. Incentive-based distributed cache in which users are rewarded for caching and helping in video distribution is also presented in [32] where users can elect to cache video files that increase their incentives. A major difference from the incentive-based mechanisms mentioned earlier is that the effect of interference is also considered. ...
... Incentive-based D2D [32] Distributed cache placement that maximizes the BS offloading ...
In recent years, the growing interest in video-based applications has resulted in a rapid increase in wireless data traffic and meeting the stringent quality-of-experience (QoE) requirements for such type of traffic poses a great challenge given the scarce spectrum. As a consequence, new approaches have been investigated to tackle this problem and one promising solution proposed by researchers is to exploit device-to-device (D2D) communication in video transmission. D2D communication has been presented as an innovation that can improve the cellular network performance by exploiting the proximity-based service between closely-located devices. It enhances the spectral and energy efficiencies, improves the capacity of the network and reduces the communication delay as well. Despite the above-mentioned advantages, there are some challenges in video transmission over D2D networks that need to be addressed. These issues include proposing methods for improving the quality of video streaming, management of the possible resulting interference between the D2D links and regular cellular links, resource allocation as well as appropriate selection of the mode of operation. Besides, issues related to D2D-based video caching such as clustering, energy consumption and the use of incentive-based schemes have also been discussed. In this paper, we examine the challenges of video streaming over D2D networks and comprehensively review the available solutions proposed in the literature.
... Game theoretical approaches that give caching decisions in a decentralized 82 manner also take place in literature. Works in [11], [12], [13] and [14] propose 83 cache placement algorithms using Stackelberg game model. In [11] and [13] ...
In this work we consider the problem of optimal cache placement in a D2D enabled cellular network. There are a number of helper devices in the area, which use their cached contents to help other users and offload traffic from the base station. The goal of cache placement is maximizing the offloaded traffic. We first formulate and optimally solve the cache placement problem as a mixed integer linear program. Then we propose a distributively implementable algorithm that clusters helpers. Helpers in each cluster collectively decide the contents to be cached, based on the content popularity. Numerical evaluations show that the proposed cache placement scheme always performs within 5% of the optimal result and it is robust to popularity profile and cache capacity.
