A Survey of NOMA: Current Status and Open Research Challenges

Makki, Behrooz; Chitti, Krishna; Behravan, Ali; Alouini, Mohamed‐Slim

doi:10.1109/ojcoms.2020.2969899

Cited by 365 publications

(189 citation statements)

References 32 publications

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“…Step 4 Performing NOMA-MRT Beamform with w m , w n expressed in (6), and transmit x with p m,n = [P − p n , p n ], where p n =ρ n N 0 ,ρ n ∈ (ρ n,j ,ρ n,j+1 ) andρ n,j is calculated by (33) (replacing subscript k with subscript j).…”

Section: Algorithm 1 Adaptive Switching Methodsmentioning

confidence: 99%

“…Recently, Non-orthogonal multiple access (NOMA) has been considered as a promising multiple access scheme for 5th Generation (5G) wireless communication systems, owning to its higher spectrum efficiency compared with the conventional orthogonal multiple access schemes [3][4][5]. Note that Third Generation Partnership Project (3GPP) has considered NOMA as a study-item for 5G new radio (NR) in Release 15 and decided to leave it for possible use in Beyond 5G (B5G) [6]. NOMA enables multiple users to share the same time-frequency resource block on the same spatial layer.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive transmission mode switching in downlink MISO-NOMA systems: sum rate maximization and the achievable rate region

Zhao¹,

Wang²,

Zhang³

et al. 2020

Preprint

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In this paper, the design of adaptive transmission mode switching (TMS) to maximize the sum rate for the downlink multiple-input-single-output based non-orthogonal multiple access (MISO-NOMA) systems is investigated. Firstly, the colsed-form expressions of the boundary of achievable rate region of two candidate transmission mode, i.e., NOMA-based maximum ratio transmission (NOMA-MRT) and minimum mean square error beamforming (MMSE-BF), are obtained. By obtaining the outer boundary of the union of the achievable rate regions of the two transmission mode, an adaptive switching method is developed to achieve a larger rate region. Secondly, based on the idea that the solution to the problem of weighted sum rate (WSR) optimization must be on the boundary of achievable rate region, the optimal solutions to the problem of WSR optimization for NOMA-MRT and MMSE-BF are obtained, respectively. Subsequently, by exploiting the optimal solutions aforementioned for two transmission modes and the high efficiency of TMS, a low-complexity Joint User pairing and Power Allocation algorithm (JUPA) is proposed to further improve sum-rate performance for the multi-user case. Compared with the Exhaustive Search based user Pairing and Power Allocation algorithm (ES-PPA), the proposed JUPA can enjoy a much lower computation complexity and only suffer a slight sum-rate performance loss, whereas outperforms other traditional schemes. Finally, numerical results are provided to validate the analyses and the proposed algorithms.

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Section: Algorithm 1 Adaptive Switching Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive transmission mode switching in downlink MISO-NOMA systems: sum rate maximization and the achievable rate region

Zhao¹,

Wang²,

Zhang³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Low-complexity and adaptive NOMA-MEC: It is widely known that the NOMA computational complexity (e.g., user clustering, signal decoding, and CSI acquisition) is the main barrier against the NOMA practicality [94]. Most of the existing studies consider using convex optimization and game theory approaches for solving resource management problems in NOMA MEC systems; however, they typically have high complexity.…”

Section: Learned Lessons and Potential Workmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

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“…Notice that in the present work we don't consider the impact of path loss, but it could be easily taken into account by inserting a multiplicative coefficient on the user received SNR or SINR, accounting for the loss in received power due to distance. For the sake of simplicity, we do not account for this effect in the present, as common in related published work [1], [2], [33], [38]. Next, let θ i be the statistical delay exponent of the i-th user, i.e., θ i captures how strict the delay constraint of the user i is, and assume that the service process satisfies the Gärtner-Ellis theorem [30].…”

Section: Effective Capacity In Uplink Nomamentioning

confidence: 99%

Asymptotic Performance Analysis of NOMA Uplink Networks Under Statistical QoS Delay Constraints

Bello

Chorti

Fijalkow

et al. 2020

IEEE Open J. Commun. Soc.

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In this paper, we study the performance of an uplink non-orthogonal multiple access (NOMA) network under statistical quality of service (QoS) delay constraints, captured through each user's effective capacity (EC). We first propose novel closed-form expressions for the EC in a two-user NOMA network and show that in the high signal-to-noise ratio (SNR) region, the "strong" NOMA user, referred to as U2, has a limited EC, assuming the same delay constraint as the "weak" user, referred to as U1. We demonstrate that for the weak user U1, OMA and NOMA have comparable performance at low transmit SNRs, while NOMA outperforms OMA in terms of EC at high SNRs. On the other hand, for the strong user U2, NOMA achieves higher EC than OMA at small SNRs, while OMA becomes more beneficial at high SNRs. Furthermore, we show that at high transmit SNRs, irrespective of whether the application is delay tolerant, or not, the performance gains of NOMA over OMA for U1, and OMA over NOMA for U2 remain unchanged. When the delay QoS of one user is fixed, the performance gap between NOMA and OMA in terms of total EC increases with decreasing statistical delay QoS constraints for the other user. Next, by introducing pairing, we show that NOMA with user-pairing outperforms OMA, in terms of total uplink EC. The best pairing strategies are given in the cases of four and six users NOMA, raising once again the importance of power allocation in the optimization of NOMA's performance. Index Terms-Beyond 5G (B5G), effective capacity, low latency, non-orthogonal multiple access (NOMA), quality of service (QoS), user-pairing.

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A Survey of NOMA: Current Status and Open Research Challenges

Cited by 365 publications

References 32 publications

Adaptive transmission mode switching in downlink MISO-NOMA systems: sum rate maximization and the achievable rate region

Adaptive transmission mode switching in downlink MISO-NOMA systems: sum rate maximization and the achievable rate region

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Asymptotic Performance Analysis of NOMA Uplink Networks Under Statistical QoS Delay Constraints

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