A Non-Orthogonal Multiple Access-Based Multicast Scheme in Wireless Content Caching Networks

Zhao, Zhongyuan; Xu, Mingfeng; Li, Yong

doi:10.1109/jsac.2017.2726698

Cited by 64 publications

(36 citation statements)

References 31 publications

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“…The joint problem in (16) has both combinatorial and continuous variables, which is nonconvex. Motivated by (17), this problem can be separated into two subproblems to solve. First, we will study the UAV trajectory optimization in Section III to maximize the sum rate of UAV-served users by jointly optimizing S and W. Then, based on the optimized S and W, we will concentrate on the joint precoding optimization of the NOMA network in Section IV to maximize the sum rate of BS-served users.…”

Section: A System Modelmentioning

confidence: 99%

“…The optimal power allocation and scheduling for NOMA relayassisted network were jointly studied in [16]. In [17], Zhao et al proposed a novel NOMA multicast scheme through pushing and multicasting the caching contents together to enhance the spectrum efficiency. In addition, the NOMA-based heterogeneous networks have been extensively researched in [18]- [21].…”

Section: Introductionmentioning

confidence: 99%

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Joint Trajectory and Precoding Optimization for UAV-Assisted NOMA Networks

Zhao

Pang

et al. 2019

IEEE Trans. Commun.

273

143

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The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. The sum rate is maximized by jointly optimizing the UAV trajectory and the NOMA precoding. To solve the optimization, we decompose it into two steps. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory, with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained using two schemes with different constraints. The first scheme intends to cancel the interference from the BS to the UAV-served user, while the second one restricts the interference to a given threshold. In both schemes, non-convex optimization problems are converted into tractable ones. An iterative algorithm is designed. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.

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Section: A System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Trajectory and Precoding Optimization for UAV-Assisted NOMA Networks

Zhao

Pang

et al. 2019

IEEE Trans. Commun.

273

143

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“…Based on Lemma 1, by adding constraints (46l)-(46q), the binary variablesŷ (48), respectively, which turn (48) into a linear integer form. To cope with the nonlinearity of delay functions in (14) and (16), and average access delay functions in (24), we utilize the transformation method presented in Appendix A. To this end, we first transform (14) and (16) into (36c) and (36d), respectively, where both of them are in integer linear forms.…”

Section: Appendix Cmentioning

confidence: 99%

“…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 parallel.Non-orthogonal multiple access (NOMA) has recently considered as a promising technology to improve the spectral efficiency of 5G wireless networks [15], [16]. Unlike conventional orthogonal multiple access (OMA) techniques, NOMA can significantly improve the system throughput and support the massive connectivity by using successful interference cancellation (SIC) at the receivers and a mixture of multiple messages at the transmitter [15], [16]. The spectral efficiency can be further improved by combining NOMA with multicarrier systems, called multicarrier NOMA (MC-NOMA), which utilizes multicarrier diversity [17].…”

mentioning

confidence: 99%

“…Problem (31) is still MINLP, which is NP-hard, due to non-concavity of objective function (31a) and non-convexity of constraints (14), (16), (21d), (22), (24), and (29b). In order to deal with the aforementioned challenges, we first relax φ v h ,v l b in (21d) to be a non-negative real value [10], [13], which is an acceptable approach in this context since the maximum number of CPU cycles in each processor is on the order of 10 9 [11]- [13].…”

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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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