A Survey of Non-Orthogonal Multiple Access for 5G

Dai, Linglong; Wang, Bichai; Ding, Zhiguo; Hanzo, Lajos; Chen, Sheng

doi:10.1109/comst.2018.2835558

Cited by 1,127 publications

(748 citation statements)

References 144 publications

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“…1) Power Allocation for EH-powered NOMA: The power allocation policy for NOMA determines the interference cancellation capability of the receivers, and directly affects the throughput and user fairness of NOMA [242]. For EHpowered NOMA, power constraints need to be involved in the development of power allocation schemes.…”

Section: B Multipoint-to-point Systemsmentioning

confidence: 99%

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Chen

et al. 2020

IEEE Trans. on Green Commun. Netw.

110

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The advancements in smart power grid and the advocation of "green communications" have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient manner for economic and ecological benefits. This article presents a contemporary review of recent breakthroughs on the utilization, redistribution, trading and planning of energy harvested in future wireless networks interoperating with smart grids. This article starts with classical models of renewable energy harvesting technologies. We embark on constrained operation and optimization of different energy harvesting wireless systems, such as point-to-point, multipoint-to-point, multipoint-to-multipoint, multi-hop, and multi-cell systems. We also review wireless power and information transfer technologies which provide a special implementation of energy harvesting wireless communications. A significant part of the article is devoted to the redistribution of redundant (unused) energy harvested within cellular networks, the energy planning under dynamic pricing when smart grids are in place, and two-way energy trading between cellular networks and smart grids. Applications of different optimization tools, such as convex optimization, Lagrangian dual-based method, subgradient method, and Lyapunov-based online optimization, are compared. This article also collates the potential applications of energy harvesting techniques in emerging (or upcoming) 5G/B5G communication systems. It is revealed that an effective redistribution and two-way trading of energy can significantly reduce the electricity bills of wireless service providers and decrease the consumption of brown energy. A list of interesting research directions are provided, requiring further investigation.Index Terms-5G/B5G communication networks, energy har-). The first two authors contributed equally to this work. vesting, smart grid, energy redistribution and trading, optimization techniques. arXiv:1912.13203v1 [eess.SY] 31 Dec 2019 EH and smart gridpowered wireless networks Types and models for RES (Sec. II) Smart grid-powered wireless networks (Sec. VIII) Wireless power and information transfer (Sec. VII) 5G/B5G applications of EH and smart gridpowered wireless networks (Sec. XI) Energy redistribution Energy planning under dynamic pricing (Sec. IX) Two-way energy trading and cooperation (Sec. X) EH-based wireless networks Point-to-point link (Sec. III) Multipoint-to-point system (Sec. IV) Multipoint-to-multipoint system (Sec. V) Multi-hop link (Sec. VI) Lessons learnt and future directions (Sec. XII) Fig. 1. The main structure of this article. from the aspects of network deployment, energy/spectrum efficiency, and delay/bandwidth versus power. Featuring energyharvesting wireless networks, beamforming techniques are reviewed in [22]. Energy scheduling, optimization, and application are presented in [23]. Circuit design, hardware implementation, EH techniques, and communication protocols are reviewed in [24]-[27] with specific emphases on radio frequen...

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Section: B Multipoint-to-point Systemsmentioning

confidence: 99%

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Chen

et al. 2020

IEEE Trans. on Green Commun. Netw.

110

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“…To estimate the source signal θ with recovery vector x, a best-linear-unbiased-estimator is used at the fusion center [9, Lesson 9]. To be specific, the fusion center estimates θ in each slot throughθ = f T x, (11) in which f = [f 1 , f 2 , · · · , f K ] is a positive weighting vector satisfying f T 1 K = 1. By optimizing the MSE of the estimator over all possible weighting vectors and using Assumption A4, the minimum achievable MSE is given by [9, Lesson 9,…”

Section: Estimation Modelmentioning

confidence: 99%

Distributed Sensing With Orthogonal Multiple Access: To Code or not to Code?

Dong

2020

IEEE Trans. Signal Process.

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We consider the estimation distortion of a distributed sensing system with finite number of sensor nodes, in which the nodes observe a common phenomenon and transmit their observations to a fusion center over orthogonal channels. In particular, we investigate whether the coded scheme (separate source-channel coding) outperforms the uncoded scheme (joint source-channel coding) or not. To this end, we explicitly derive the estimation distortion of a coded heterogeneous sensing system with diverse node and channel configurations. Based on this result, we show that in a homogeneous sensing system with identical node and channel configurations, the coded scheme outperforms the uncoded scheme if the number of nodes is K = 1 or K = 2. For homogenous sensing systems with K ≥ 3 nodes and general heterogeneous sensing systems, we also present explicit conditions for the coded scheme to perform better than the uncoded scheme. Furthermore, we propose to minimize the estimation distortion of heterogeneous sensing systems with hybrid coding, i.e., some nodes use the coded scheme and other nodes use the uncoded scheme. To determine the optimal hybrid coding policy, we develop three greedy algorithms, in which the pure greedy algorithm minimizes distortion greedily, the group greedy algorithm improves performance by using a group of potential sub-polices, and the sorted greedy algorithm reduces computational complexity by using a pre-solved iteration order. Our numerical and Monte Carlo results show that the proposed algorithms closely approach the optimal policy in terms average estimation distortion.

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“…Although the transformed problem is not equivalent to the original problem, the gap caused by this transformation is acceptable. According to (11) and (12), we transformed the interference from other users in the achievable rates of the PU m and SU m i (2)-(3) to (13)- (14). The interference of the SU m i depends on the transmit power and the channel gains of other weak users.…”

Section: Problem Transformationmentioning

confidence: 99%

“…Unlike the orthogonal MA (OMA) technology where radio resources are allocated orthogonally to multiple users, NOMA allows multiple users with different channel state information to simultaneously occupy the same spectrum resource and distinguishes the signals of users in the power domain . At the receiver, the received superimposed signal is decoded by multiuser detection techniques, such as successive interference cancelation (SIC) technology . Although NOMA increases the complexity of the receiver, it has the advantages of improved spectral efficiency, massive connectivity, low transmission latency, and low signaling cost …”

Section: Introductionmentioning

confidence: 99%

“…10 At the receiver, the received superimposed signal is decoded by multiuser detection techniques, such as successive interference cancelation (SIC) technology. 11 Although NOMA increases the complexity of the receiver, it has the advantages of improved spectral efficiency, massive connectivity, low transmission latency, and low signaling cost. 12 From the aspect of network deployment, mobile edge computing (MEC) is a key technology for achieving high data rate and computational capability as it brings the computing functions from local terminals and remote clouds to the edge of the network.…”

Section: Introductionmentioning

confidence: 99%

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Edge computing and power control in NOMA‐enabled cognitive radio networks

Cheng

Chen

Liang

2019

Trans Emerging Tel Tech

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Due to the limited computation resources of mobile devices in cognitive radio networks, the secondary users in the network can suffer from long executing time, which is not acceptable for latency‐sensitive and computation‐intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user and within the available computation resources of the MEC server, the primary user and secondary users both can offload tasks to the MEC server through nonorthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the nonconvexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near‐optimal solution. Simulation results show that under the same parameters, such as the number of primary users, maximum transmit power, computational capability of the MEC server and the computational capability of the secondary users, the proposed NOMA‐enabled computation offloading scheme can effectively reduce the overall computing latency for the secondary network and improve the percentage of offloading secondary users than those of OMA‐enabled.

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A Survey of Non-Orthogonal Multiple Access for 5G

Cited by 1,127 publications

References 144 publications

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Distributed Sensing With Orthogonal Multiple Access: To Code or not to Code?

Edge computing and power control in NOMA‐enabled cognitive radio networks

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