Dynamic Power–Latency Tradeoff for Mobile Edge Computation Offloading in NOMA-Based Networks

Nouri, Nima; Entezari, Ahmadreza; Abouei, Jamshid; Jaseemuddin, Muhammad; Anpalagan, Alagan

doi:10.1109/jiot.2019.2957313

Cited by 41 publications

(8 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The long-term average network utility maximization problem was investigated for a buffer-aided cooperative NOMA system in [34], where a source was connected to multiple destinations via a buffer-aided NOMA relay. An average weighted-sum power consumption minimization problem was investigated for a stochastic NOMA-based MEC network in [35]. However, the offloading scheduling in the above works were optimized with the assumption of complete knowledge of the overall users, such as channel state information, computational task arrivals and memory management status, which may not be practical in realistic situations.…”

Section: B Related Workmentioning

confidence: 99%

Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge

Hua,

Tian,

Lyu

et al. 2021

Preprint

View full text Add to dashboard Cite

By exploiting the superiority of non-orthogonal multiple access (NOMA), NOMA-aided mobile edge computing (MEC) can provide scalable and low-latency computing services for the Internet of Things. However, given the prevalent stochasticity of wireless networks and sophisticated signal processing of NOMA, it is critical but challenging to design an efficient task offloading algorithm for NOMA-aided MEC, especially under a large number of devices. This paper presents an online algorithm that jointly optimizes offloading decisions and resource allocation to maximize the long-term system utility (i.e., a measure of throughput and fairness). Since the optimization variables are temporary coupled, we first apply Lyapunov technique to decouple the long-term stochastic optimization into a series of per-slot deterministic subproblems, which does not require any prior knowledge of network dynamics. Second, we propose to transform the non-convex per-slot subproblem of optimizing NOMA power allocation equivalently to a convex form by introducing a set of auxiliary variables, whereby the time-complexity is reduced from the exponential complexity to O(M 3/2 ). The proposed algorithm is proved to be asymptotically optimal, even under partial knowledge of the device states at the base station. Simulation results validate the superiority of the proposed algorithm in terms of system utility, stability improvement, and the overhead reduction.

show abstract

Section: B Related Workmentioning

confidence: 99%

Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge

Hua,

Tian,

Lyu

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For dynamic NOMA-based computation offloading, both the channel fluctuations and dynamic task arrivals are considered. The authors of [18] and [30] investigated in the multi-user NOMA uplink computation offloading and multi-server aided downlink NOMA computation offloading, respectively. The classic Lyapunov optimization method was used for converting the long-term utility optimization problem into an online optimization problem in both these studies.…”

Section: Related Contributionsmentioning

confidence: 99%

“…In vehicular communications, spectrum shortage is another issue when considering data transmission among vehicles requesting computation offloading and those assisting in offloaded processing. In this context, non-orthogonal multiple access (NOMA) can serve several users within the same time and frequency resource block, which further improves the bandwidth efficiency [14], as evidenced in [15]- [18].…”

Section: Introductionmentioning

confidence: 99%

Dynamic NOMA-Based Computation Offloading in Vehicular Platoons

Zheng¹,

Chen²,

Wei³

et al. 2021

Preprint

View full text Add to dashboard Cite

Both the Mobile edge computing (MEC)-based and fog computing (FC)-aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMAbased computation offloading scheme for vehicular platoons on highways, where the vehicles can offload their computing tasks to other platoon members. To cope with the rapidly fluctuating channel quality, we divide the timeline into successive time slots according to the channel's coherence time. Robust computing and offloading decisions are made for each time slot after taking the channel estimation errors into account. Considering a certain time slot, we first analytically characterize both the locally computed source data and the offloaded source data as well as the energy consumption of every vehicle in the platoons. We then formulate the problem of minimizing the long-term energy consumption by optimizing the allocation of both the communication and computing resources. To solve the problem formulated, we design an online algorithm based on the classic Lyapunov optimization method and block successive upper bound minimization (BSUM) method. Finally, the numerical simulation results characterize the performance of our algorithm and demonstrate its advantages both over the local computing scheme and the orthogonal multiple access (OMA)-based offloading scheme.

show abstract

“…The authors in [15] jointly optimized the power and time distribution to cut down computing and offloading energy consumption and then obtained the closed-form expression of optimal power and time allocation solution. In [16], the partial offloading scheme based on NOMA minimized the average power consumption and realized the dynamic power delay trade-off for MEC offloading by using the Lyapunov method. To improve the offloading efficiency of edge users, a NOMA-assisted MEC system was introduced in [17], where a joint communication and computation resource iterative optimization algorithm with low complexity was proposed to minimize the energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of MEC Based on NOMA under Imperfect CSI with Eavesdropper

Pei

Jiang

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Mobile edge computing (MEC) is becoming more and more popular because of improving computing power in virtual reality, augmented reality, unmanned driving, and other fields. This paper investigates a nonorthogonal multiple access- (NOMA-) based MEC system, which is under imperfect channel state information (ipCSI). In this system model, a pair of users offloads their tasks to the MEC server with the existence of an eavesdropper (Eve). To evaluate the impact of Eve on the performance of the NOMA-MEC system, the secrecy outage probability (SOP) expressions for two users with the conditions of imperfect CSI and perfect channel state information (pCSI) are derived. In addition, both throughput and energy efficiency are discussed in the delay-limited transmission mode. Simulation results reveal that (1) due to the influence of channel estimation errors, the secrecy outage behaviors of two users under ipCSI conditions are worse than those of users with pCSI; (2) the secrecy performance of NOMA-MEC is superior to orthogonal multiple access- (OMA-) aided MEC systems; and (3) the NOMA-MEC systems have the ability to attain better system throughput and energy efficiency compared with OMA-MEC.

show abstract

Dynamic Power–Latency Tradeoff for Mobile Edge Computation Offloading in NOMA-Based Networks

Cited by 41 publications

References 45 publications

Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge

Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge

Dynamic NOMA-Based Computation Offloading in Vehicular Platoons

Performance Analysis of MEC Based on NOMA under Imperfect CSI with Eavesdropper

Contact Info

Product

Resources

About