Resource Allocation in Cache-Enabled CRAN with Non-Orthogonal Multiple Access

Zhao, Jingjing; Liu, Yuanwei; Mahmoodi, Toktam; Chai, Kok Keong; Chen, Yue; Han, Zhu

doi:10.1109/icc.2018.8422358

Cited by 23 publications

(29 citation statements)

References 12 publications

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“…Here, we discuss the convergence of our proposed Algorithm 1 for solving (29). This discussion is presented in the format of the following two propositions.…”

Section: B Convergence Of the Proposed Algorithmsmentioning

confidence: 98%

“…The cache placement optimization problem in the HD scheme is The mixed-integer nonlinear programming (MINLP) problems (21), (29), and (30) are completely NP-hard, which are mathematically proved in [38] in the transmit power and subcarrier allocation problem for MC-NOMA in order to maximize the downlink data rate of users. In addition, in [39], it is mentioned that data rate maximization optimization problems with interference are completely NP-hard for OFDMA-based wireless networks and it is nonachievable to find the global optimum joint transmit power and subcarrier allocation policy by any existing method.…”

Section: Phasementioning

confidence: 99%

“…The main structure of optimization problems (21) and (29) are the same for a fixed cache placement variable ρ. Therefore, we provide a local optimum resource allocation algorithm that can be adopted for both problems (21) and (29). However, (30) and (29) differ in objective function.…”

Section: Phasementioning

confidence: 99%

“…However, (30) and (29) differ in objective function. Accordingly, we modify the solution algorithm proposed for (29) to be applied to (30).…”

Section: Phasementioning

confidence: 99%

“…The objective function (29a) is upper-bounded by the total average reward of slices which is obtained by u∈Um b∈Br Ac b,u ψ m that is a nonnegative finite term, due to limited access bandwidth and constraint (7). Therefore, for a feasible problem (29), the proposed alternate Algorithm 1 will converge to a locally optimal solution.…”

Section: Propositionmentioning

confidence: 99%

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Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks

et al. 2019

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Cooperative video caching and transcoding in mobile edge computing (MEC) networks is a new paradigm for future wireless networks, e.g., 5G and 5G beyond, to reduce scarce and expensive backhaul resource usage by prefetching video files within radio access networks (RANs). Integration of this technique with other advent technologies, such as wireless network virtualization and multicarrier nonorthogonal multiple access (MC-NOMA), provides more flexible video delivery opportunities, which leads to enhancements both for the network's revenue and for the end-users' service experience. In this regard, we propose a two-phase RAF for a parallel cooperative joint multi-bitrate video caching and transcoding in heterogeneous virtualized MEC networks. In the cache placement phase, we propose novel proactive delivery-aware cache placement strategies (DACPSs) by jointly allocating physical and radio resources based on network stochastic information to exploit flexible delivery opportunities. Then, for the delivery phase, we propose a delivery policy based on the user requests and network channel conditions. The optimization problems corresponding to both phases aim to maximize the total revenue of network slices, i.e., virtual networks. Both problems are non-convex and suffer from high-computational Sepehr Rezvani and Nader Mokari are with the Recently, mobile edge computing (MEC) networks have emerged as a promising technology for next generation wireless networks, providing cloud caching and computing capabilities within the RAN [8], [10]-[13]. Thanks to this paradigm, video files could be prefetched and/or transcoded in close proximity to end-users, leading to enormous latency and backhaul traffic reductions in wireless networks. One problem with this, however, is that duplicated video caching and transcoding in multiple resource-constrained MEC servers wastes both storage and processing resources. To tackle this issue, cooperative joint multi-bitrate video caching and transcoding (CVCT) technology is proposed where each MEC server is able to receive the requested video files from neighboring MEC servers via fronthaul links [7]. In this architecture, each MEC server is deployed side-by-side with each base station (BS) using the generic computing platforms which provides the caching and computation capabilities in heterogeneous networks (HetNets) [5]-[8].3 By sharing both the storage and processing resources among multiple MEC servers, more video files can be prefetched within RANs which results increasing the cache hit ratio [7], [8]. However, non-simultaneous transferring and transcoding video files wastes more time and physical resources in the CVCT system, which is not beneficial for delay-sensitive services. To cope with this challenge, parallel video transmission and transcoding capability [9], [14] can be deployed.In the parallel CVCT system, video transcoding runs in parallel with video transmission, and all the multi-hop video transmissions (between backhaul, fronthaul, and wireless access links) also run in parall...

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“…Here, we discuss the convergence of our proposed Algorithm 1 for solving (29). This discussion is presented in the format of the following two propositions.…”

Section: B Convergence Of the Proposed Algorithmsmentioning

confidence: 98%