Energy-efficient cooperative coded caching for heterogeneous small cell networks

Jia, Qingmin; Xie, Renchao; Huang, Tao; Liu, Jiang; Liu, Yunjie

doi:10.1109/infcomw.2017.8116421

Cited by 15 publications

(10 citation statements)

References 14 publications

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“…where η k represents the computation energy efficiency coefficient related to the processor's chip. On the other hand, based on the energy-proportional model the local caching energy consumed for processing (1 − l k [n])s k [n] bits data at kth GN can be written as where ω ca k is the coefficient of caching power efficiency related to the GN caching hardware drives [27][28][29]. With the help of (5), the transmission energy consumption at the kth IoT device is given as (5)…”

Section: Energy Consumption At the Iot Devicementioning

confidence: 99%

Collaborative offloading for UAV-enabled time-sensitive MEC networks

Zhao

et al. 2021

J Wireless Com Network

127

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Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($$\mathbf {P}_{\mathbf {T}}$$ P T ), resource allocation at UAV ($$\mathbf {P}_{\mathbf {R}}$$ P R ) and offloading decisions at IoT devices ($$\mathbf {P}_{\mathbf {O}}$$ P O ) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

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Section: Energy Consumption At the Iot Devicementioning

confidence: 99%

Collaborative offloading for UAV-enabled time-sensitive MEC networks

Zhao

et al. 2021

J Wireless Com Network

127

View full text Add to dashboard Cite

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“…where ω ca k is the caching energy coefficient related to the device storage drive [23,24,25]. With the help of (5), the transmission energy consumption at the k-th IoT device is given as…”

Section: Energy Consumption At the Iot Devicementioning

confidence: 99%

Collaborative Offloading for UAV-enabled Time-Sensitive MEC Networks

Zhao

et al. 2020

Preprint

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Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV is rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($\mathbf{P_T}$), resource allocation at UAV ($\mathbf{P_R}$) and offloading decisions at IoT devices ($\mathbf{P_O}$), then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

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“…Since SBSs are closer to users and usually provide larger bandwidth than the MBS, caching at the SBSs has attracted significant attention in HetNets. Compared to the uncoded caching, the careful placement of coded content in caches leads to a more significant decrease of the end-to-end delay and backhaul pressure for the HetNets [11]- [14]. For example, [11] and [12] have found the optimal content placement in all the SBSs by restructuring the contents with MDS codes to reduce the backhaul rate.…”

Section: Related Workmentioning

confidence: 99%

“…Focusing on the content placement phase in HetNets, [13] investigates the tradeoff between expected backhaul rate and energy consumption. Authors in [14] jointly consider the coded caching and cooperative caching issues, and propose a cooperative coded caching scheme to optimize energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Actually, coded caching is well suited to emerging HetNets which consist of a dense deployment of local-coverage SBSs with high data rates, along with sparsely distributed, large-coverage MBS. For example, authors in [13], [14] put forward a maximum-distance separable (MDS) encoded caching scheme to achieve energy-saving edge computing in HetNets.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Aware Joint User Association and Resource Allocation for Coded Cache-Enabled HetNets

et al. 2019

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Cache-enabled heterogeneous networks (HetNets) have recently emerged as an attractive solution to meet the exponentially increasing demand on mobile data traffic. However, the power consumption and the backhaul limitation of small base stations (SBSs) have become bottlenecks to deploy HetNets. How to relieve the burden of backhauls via wireless caching and enable the HetNets to operate in an energy-efficient way are still open issues. Aiming to minimize the power consumption while guaranteeing QoS requirements of users, in this paper, we address the problem of joint user association (UA) and resource allocation (RA) for coded cache-enabled HetNets. First, based on the many-to-many matching game between the virtual SBSs and users (VSU), we propose a low-complexity joint UA and power allocation (PA) algorithm (JUPVA). Then, considering the unequal BA, we design a three-phase optimization algorithm (JURVA), which makes a joint decision on UA, PA, and BA iteratively. The simulation results demonstrate that the proposed algorithms yield significant performance improvement in terms of power consumption.

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Energy-efficient cooperative coded caching for heterogeneous small cell networks

Cited by 15 publications

References 14 publications

Collaborative offloading for UAV-enabled time-sensitive MEC networks

Collaborative offloading for UAV-enabled time-sensitive MEC networks

Collaborative Offloading for UAV-enabled Time-Sensitive MEC Networks

Energy-Aware Joint User Association and Resource Allocation for Coded Cache-Enabled HetNets

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