Fairness Comparison of Uplink NOMA and OMA

Wei, Zhiqiang; Guo, Jiajia; Ng, Derrick Wing Kwan; Yuan, Jinhong

doi:10.1109/vtcspring.2017.8108680

Cited by 62 publications

(39 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that in the single-BS-antenna case (N = 1) with α 1 = 0.35 under our setup, the OMA-based partial offloading scheme is observed to achieve the same energy-saving performance as the NOMA-based counterpart. This is consistent with the fact that in the single-antenna scenario, the OMA transmission is able to achieve one point at the Pareto boundary of the capacity region for the multiple access channel (that is achievable by NOMA) in a time-sharing manner [30]. Under our setup, this point is achieved by the two users time-sharing the offloading wireless channel with portions α 1 = 0.35 and α 2 = 1 − α 1 = 0.65, respectively.…”

Section: Numerical Resultssupporting

confidence: 83%

Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems

Wang

Ding

2019

IEEE Trans. Commun.

222

160

View full text Add to dashboard Cite

This paper studies a multiuser mobile edge computing (MEC) system, in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancellation (SIC) to efficiently decode all users' offloaded tasks for remote execution. In particular, we pursue energy-efficient MEC designs by considering two cases with partial and binary offloading, respectively. We aim to minimize the weighted sum-energy consumption at all users subject to their computation latency constraints, by jointly optimizing the communication and computation resource allocation as well as the BS's decoding order for SIC. For the case with partial offloading, the weighted sum-energy minimization is a convex optimization problem, for which an efficient algorithm based on the Lagrange duality method is presented to obtain the globally optimal solution. For the case with binary offloading, the weighted sum-energy minimization corresponds to a mixed Boolean convex problem that is generally more difficult to be solved. We first use the branch-and-bound (BnB) method to obtain the globally optimal solution, and then develop two low-complexity algorithms based on the greedy method and the convex relaxation, respectively, to find suboptimal solutions with high quality in practice. Via numerical results, it is shown that the proposed NOMA-based computation offloading design significantly improves the energy efficiency of the multiuser MEC system as compared to other benchmark schemes. It is also shown that Part of this paper has been presented at on the corresponding task models [5]. For example, partial offloading and binary offloading are two widely adopted computation offloading models in the MEC literature [5], in which the tasks at each user are fully partitionable and non-partitionable, respectively. Next, the performance optimization of computation offloading in MEC systems critically relies on the joint design of both communication and computation resource allocations [7]- [11]. For example, consider that one BS serves one single actively-computing user. In order to minimize the energy consumption for task execution, it is crucial for the user to jointly optimize the communication power (for offloading) and the central processing unit (CPU) frequencies for local computing to balance their energy consumption tradeoff. For the case of partial offloading, the user needs to properly partition the computation task into two parts for offloading and local computing, respectively; for the case of binary offloading, the user needs to properly choose the operation mode between offloading and local computing for energy minimization. Furthermore, future wireless networks are expected to consist of massive IoT devices, and each BS generally needs to ...

show abstract

Section: Numerical Resultssupporting

confidence: 83%

Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems

Wang

Ding

2019

IEEE Trans. Commun.

222

160

View full text Add to dashboard Cite

show abstract

“…1. The cell is modeled by a pair of two concentric ring- 1 We restrict ourselves to the uplink NOMA communications [49], as advanced signal detection/decoding algorithms of NOMA are more affordable at the base station. shaped discs.…”

Section: A System Modelmentioning

confidence: 99%

On the Performance Gain of NOMA Over OMA in Uplink Communication Systems

Wei

Yang

et al. 2020

IEEE Trans. Commun.

Self Cite

196

112

View full text Add to dashboard Cite

In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink cellular communication systems. A base station equipped with a single-antenna, with multiple antennas, and with massive antenna arrays is considered both in single-cell and multi-cell deployments. In particular, in single-antenna systems, we identify two types of gains brought about by NOMA: 1) a large-scale near-far gain arising from the distance discrepancy between the base station and users; 2) a small-scale fading gain originating from the multipath channel fading. Furthermore, we reveal that the large-scale near-far gain increases with the normalized cell size, while the small-scale fading gain is a constant, given by γ = 0.57721 nat/s/Hz, in Rayleigh fading channels. When extending single-antenna NOMA to M -antenna NOMA, we prove that both the large-scale near-far gain and small-scale fading gain achieved by single-antenna NOMA can be increased by a factor of M for a large number of users. Moreover, given a massive antenna array at the base station and considering a fixed ratio between the number of antennas, M , and the number of users, K, the ESG of NOMA over OMA increases linearly with both M and K. We then further extend the analysis to a multi-cell scenario. Compared to the single-cell case, the ESG in multicell systems degrades as NOMA faces more severe inter-cell interference due to the non-orthogonal transmissions. Besides, we unveil that a large cell size is always beneficial to the ergodic sum-rate performance of NOMA in both single-cell and multi-cell systems. Numerical results verify the accuracy of the analytical results derived and confirm the insights revealed about the ESG of NOMA over OMA in different scenarios.

show abstract

“…A research aimed to compare the uplink (UL) NOMA system with OMA using resource allocation fairness as a reference and considering only Jain's index was reported in Reference . The authors analyzed fairness for only two users.…”

Section: Introductionmentioning

confidence: 99%

Nonorthogonal multiple access systems optimization to ensure maximum fairness to users

Jacob

Abrão

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

In this contribution, the optimization of power proportion allocated for each user in the downlink (DL) nonorthogonal multiple access (NOMA) systems have been developed. Successive interference cancellation technique recovers users' signal with high difference between channel gains, to find the lowest optimum power proportion required to guarantee an equal data rate for all active users.Following the same approach, the optimum power proportion for each user and also the minimum total power to achieve the same rate for all users (maximum fairness) were obtained as a design goal. Moreover, the same design methodology was developed seeking to maximize the NOMA system energy efficiency (EE). It was possible to find the maximum EE point and the respective power distribution among the users for a certain circuitry power consumption. For all NOMA users, in which the optimal operation point for EE maximization was parameterized, it was possible to find the total power, power ratio, and the equal rate values. As a result, one can find the trade-off point between EE and sum rate for each system scenario, as well as the best resource efficiency operation point. By considering the same rate for all users, the system attains maximum fairness among the users.Abbreviations: BS, base station; DL, downlink; EE, energy efficiency; NOMA, nonorthogonal multiple access; OMA, orthogonal multiple access; QoS, quality of service; RF, radio frequency; SE, spectral efficiency; SIC, successive cancellation of interference; SINR, signal-to-interference-plus-noise power ratio; SQP, sequential quadratic programming.Trans Emerging Tel Tech. 2020;31:e3875.wileyonlinelibrary.com/journal/ett

show abstract

Fairness Comparison of Uplink NOMA and OMA

Cited by 62 publications

References 26 publications

Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems

Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems

On the Performance Gain of NOMA Over OMA in Uplink Communication Systems

Nonorthogonal multiple access systems optimization to ensure maximum fairness to users

Contact Info

Product

Resources

About