Optimal SIC Ordering and Computation Resource Allocation in MEC-Aware NOMA NB-IoT Networks

Qian, Li Ping; Feng, Ao; Huang, Yupin; Wu, Yuan; Ji, Bo; Shi, Zhiguo

doi:10.1109/jiot.2018.2875046

Cited by 131 publications

(59 citation statements)

References 39 publications

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“…Shahini et al [21] proposed the energy efficiency maximization problem for cognitive radio (CR) based IoT networks by taking into consideration of user buffer occupancy and data rate fairness. Qian et al [22] proposed an optimal SIC ordering to minimize the maximum task execution latency across devices for MEC-aware NOMA NB-IoT network. Zhai et al [23] proposed a joint user scheduling and power allocation for NOMA based wireless networks with massive IoT devices.…”

Section: B Related Workmentioning

confidence: 99%

NOMA Aided Narrowband IoT for Machine Type Communications With User Clustering

Shahini

Ansari

2019

IEEE Internet Things J.

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To support Machine Type Communications (MTC) in next generation mobile networks, NarrowBand-IoT (NB-IoT) has been released by the Third Generation Partnership Project (3GPP) as a promising solution to provide extended coverage and low energy consumption for low cost MTC devices. However, the existing Orthogonal Multiple Access (OMA) scheme in NB-IoT cannot provide connectivity for a massive number of MTC devices. In parallel with the development of NB-IoT, Non-Orthogonal Multiple Access (NOMA), introduced for the fifth generation wireless networks, is deemed to significantly improve the network capacity by providing massive connectivity through sharing the same spectral resources. To leverage NOMA in the context of NB-IoT, we propose a power domain NOMA scheme with user clustering for an NB-IoT system. In particular, the MTC devices are assigned to different ranks within the NOMA clusters where they transmit over the same frequency resources. Then, we formulate an optimization problem to maximize the total throughput of the network by optimizing the resource allocation of MTC devices and NOMA clustering while satisfying the transmission power and quality of service requirements. We prove the NP-hardness of the proposed optimization problem. We further design an efficient heuristic algorithm to solve the proposed optimization problem by jointly optimizing NOMA clustering and resource allocation of MTC devices. Furthermore, we prove that the reduced optimization problem of power control is a convex optimization task. Simulation results are presented to demonstrate the efficiency of the proposed scheme.

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Section: B Related Workmentioning

confidence: 99%

NOMA Aided Narrowband IoT for Machine Type Communications With User Clustering

Shahini

Ansari

2019

IEEE Internet Things J.

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“…Even though MEC and NOMA have been widely studied in recent years, only handful research works have been carried to study the combination of NOMA and multiuser MEC networks [6,[23][24][25][26][27][28][29]. To further enhance the spectrum efficiency and the performance of multiuser MEC networks, NOMA uplink transmission and downlink transmission were proposed to be applied to MEC [23].…”

Section: A Related Literaturementioning

confidence: 99%

“…The energy efficient power allocation, time allocation and task assignment were proposed to minimize the energy consumption for a NOMA MEC network [27]. Besides the computational resource, the successive interference cancellation (SIC) decoding order was optimized to reduce the task delay for NOMA enabled narrowband Internet of Things (NB-IoT) systems [29].…”

Section: A Related Literaturementioning

confidence: 99%

Optimal Task Partition and Power Allocation for Mobile Edge Computing with NOMA

Fang

Ding

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Mobile edge computing (MEC) can enhance the computing capability of mobile devices, and nonorthogonal multiple access (NOMA) can provide high data rates. Combining these two technologies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task completion time minimization in NOMA multiuser MEC networks, where multiple users can offload their tasks simultaneously via the same frequency band. We adopt the partial offloading, in which each user can partition its computation task into offloading computing and locally computing parts. We aim to minimize the maximum task latency among users by optimizing their tasks partition ratios and offloading transmit power. By considering the energy consumption and transmitted power limitation of each user, the formulated problem is quasi-convex. Thus, a bisection search (BSS) iterative algorithm is proposed to obtain the minimum task completion time. To reduce the complexity of the BSS algorithm and evaluate its optimality, we further derive the closed-form expressions of the optimal task partition ratio and offloading power for two-user NOMA MEC networks based on the analysed F. Fang and Z. Ding are with

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“…Applying caching to heterogeneous network nodes can satisfy the request of UEs in the local networks. By proactively predicting and caching the most popular contents in the storage facilities, the distances between the UEs and contents are shortened, which improves the performance of content delivering.From now on, several recent works have investigated computation offloading and resource allocation strategies in the mobile edge systems [15][16][17][18][19][20]. The authors of [15][16][17] focused on the computation offloading strategy.…”

mentioning

confidence: 99%

“…By the optimization of offloading mode (e.g., local computing or edge offloading mode), the service delay and energy consumption would be largely saved. Some researchers investigated the communication resources allocation [18], computational resource allocation [19,20] in MEC-enabled systems to improve the resource efficiency. As wireless channel conditions of UEs have significant differences, the communication resource allocation and computational resource allocation can improve communication and computing efficiency, respectively.…”

mentioning

confidence: 99%

Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems

Lan

Wang

et al. 2019

Electronics

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The hierarchical edge-cloud enabled paradigm has recently been proposed to provide abundant resources for 5G wireless networks. However, the computation and communication capabilities are heterogeneous which makes the potential advantages difficult to be fully explored. Besides, previous works on mobile edge computing (MEC) focused on server caching and offloading, ignoring the computational and caching gains brought by the proximity of user equipments (UEs). In this paper, we investigate the computation offloading in a three-tier cache-assisted hierarchical edge-cloud system. In this system, UEs cache tasks and can offload their workloads to edge servers or adjoining UEs by device-to-device (D2D) for collaborative processing. A cost minimization problem is proposed by the tradeoff between service delay and energy consumption. In this problem, the offloading decision, the computational resources and the offloading ratio are jointly optimized in each offloading mode. Then, we formulate this problem as a mixed-integer nonlinear optimization problem (MINLP) which is non-convex. To solve it, we propose a joint computation offloading and resource allocation optimization (JORA) scheme. Primarily, in this scheme, we decompose the original problem into three independent subproblems and analyze their convexity. After that, we transform them into solvable forms (e.g., convex optimization problem or linear optimization problem). Then, an iteration-based algorithm with the Lagrange multiplier method and a distributed joint optimization algorithm with the adoption of game theory are proposed to solve these problems. Finally, the simulation results show the performance of our proposed scheme compared with other existing benchmark schemes. and transmission techniques applied in the conventional cellular networks may not be efficient to meet UEs' requirements of high throughput and adequate computational power. To improve the delay performance and operational costs of services in fifth-generation (5G) wireless networks, future communication networks not only need to support seamless wireless access but also to offer the provisioning of computational offloading for UEs [3,4].To meet with such a challenge of limited computational capability of UEs, a typical paradigm of mobile edge computing (MEC) is proposed which combines wireless network service and cloud computing at the edge of the small cell networks (SCNs) [5]. In an MEC system, a huge number of heterogeneous ubiquitous and decentralized devices are enabled to communicate, potentially cooperate and perform computation offloading by uploading their computational tasks to the MEC server via access ratio networks [6,7]. UEs no longer need to offload all of their tasks (e.g., high-quality video streaming, mobile gaming, etc.) to the central and remote cloud. Thus their requirement can be satisfied at any time and anywhere. However, the limited computational capabilities of edge servers may not be sufficient when there exists competition for resources by a large number of device...

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Optimal SIC Ordering and Computation Resource Allocation in MEC-Aware NOMA NB-IoT Networks

Cited by 131 publications

References 39 publications

NOMA Aided Narrowband IoT for Machine Type Communications With User Clustering

NOMA Aided Narrowband IoT for Machine Type Communications With User Clustering

Optimal Task Partition and Power Allocation for Mobile Edge Computing with NOMA

Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems

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