Virtual Resource Allocation in Software-Defined Information-Centric Cellular Networks With Device-to-Device Communications and Imperfect CSI

Wang, Kan; Li, Hongyan; Yu, F. Richard; Wei, Wenchao

doi:10.1109/tvt.2016.2529660

Cited by 61 publications

(55 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this phase, we assume a time-slotted system where at the beginning of each time slot, each user requests one video file. Similar to related works [3]- [6], [18], [19], [30], [32]- [34], the CSI and requests of users remain fixed through a time slot and are completely independent from other time slots 2 . All requests of users at each time slot should also be served within the time slot [3], [18], [33].…”

Section: B Phasementioning

confidence: 82%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

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...

show abstract

Section: B Phasementioning

confidence: 82%

“…The binary parameter θ b,u ∈ {0, 1} determines the user association indicator where if user u is associated with RRS b, θ b,u = 1 and otherwise, θ b,u = 0. In this system, we assume that each user can be connected to at most one RRS, which is represented by [3], [6], [7], [18], [30]…”

Section: B Phasementioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Moreover, they study the coordinated multi point (CoMP) technique in the considered system model. The authors of [24] investigate an information-centric network architecture for device-to-device communications, in which with the software defined radio (SDN) controller, a dynamic centralized radio resource allocation is applied. In [25], the authors develop an algorithm to find both the user association and bandwidth allocation in an SD-based virtualized information-centric architecture network.…”

Section: A Related Workmentioning

confidence: 99%

Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks

Moltafet

Parsaeefard

Javan

et al. 2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

In this paper, by considering multiple slices, a downlink transmission of a sparse code multiple access (SCMA) based cloud-radio access network (C-RAN) is investigated. In this regard, by supposing multiple input and single output (MISO) transmission technology, a novel robust radio resource allocation is proposed where considering uncertain channel state information (CSI), the worst case approach is applied. The main goal of the proposed radio resource allocation is to, maximize the system sum rate with maximum available power at radio remote head (RRH), minimum rate requirement of each slice, maximum frounthaul capacity of each RRH, user association, and SCMA constraints. To solve the proposed optimization problem in an efficient manner, an iterative method is deployed where in each iteration, beamforming and joint codebook allocation and user association subproblem are solved separately. By introducing some auxiliary variables, the joint codebook allocation and user association subproblem is transformed into an integer linear programming, and to solve the beamforming optimization problem, minorization-maximization algorithm (MMA) is applied. Via numerical results, the performance of the proposed system model versus different system parameters and for different channel models are investigated. Index Terms-5G, Sparse code multiple access (SCMA), C-RAN, robust resource allocation.

show abstract

“…. In practice, the available CSI in the SDN controller is imperfect due to inaccurate channel estimation and measurements [45]. Another factor can be the transmission delay (lag) during the CSI delivery.…”

Section: A Downlink Transmissionmentioning

confidence: 99%

SDN-Enabled MIMO Heterogeneous Cooperative Networks With Flexible Cell Association

Papazafeiropoulos

Kourtessis

Renzo

et al. 2019

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Small-cell densification is a strategy enabling the offloading of users from macro base stations (MBSs), in order to alleviate their load and increase the coverage, especially, for celledge users. In parallel, as the network increases in density, the BS cooperation emerges as an efficient design method towards the demands for drastic improvement of the system performance against the detrimental overall interference. We, therefore, model and scrutinize a heterogeneous network (HetNet) of two tiers (macro and small cells) with multiple-antenna BSs serving a multitude of users, which differ with respect to their basic design parameters, e.g., the deployment density, the number of transmit antennas, and transmit power. In addition, the tiers are enhanced with cell association policies by introducing the concept of the association probability. Above this and motivated by the advantages of cooperation among BSs, the small base stations (SBSs) are enriched with this property in their design. SBS cooperation allows shedding light into its impact on the cell selection rules in multi-antenna HetNets. Under these settings, software-defined networking (SDN) is introduced smoothly to play the leading role in the orchestration of the network. In particular, heavy operations such as the coordination and the cell association are undertaken by virtue of an SDN controller performing and managing efficiently the corresponding computations due to its centralized adaptability and dynamicity towards the enhancement and potential scalability of the network. In this context, we derive the coverage probability and the mean achievable rate. Not only we show the outperformance of BS cooperation over uncoordinated BSs, but we also demonstrate that the SBS cooperation enables the admittance of more users from the macro-cell BSs (MBSs). Furthermore, we show that by increasing the number of BS antennas, the system performance is improved as the metrics under study reveal. Moreover, we investigate the performance of different transmission techniques, and we identify the optimal bias in each case when SBSs cooperate. Finally, we depict that the SBS densification is beneficial until a specific density value since a further increase does not increase the coverage probability.

show abstract

Virtual Resource Allocation in Software-Defined Information-Centric Cellular Networks With Device-to-Device Communications and Imperfect CSI

Cited by 61 publications

References 35 publications

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

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

Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks

SDN-Enabled MIMO Heterogeneous Cooperative Networks With Flexible Cell Association

Contact Info

Product

Resources

About