Energy efficiency optimization in cognitive radio inspired non-orthogonal multiple access

Zhang, Yi; Yang, Qian; Zheng, Tong-Xing; Wang, Huiming; Ju, Ying; Meng, Yue

doi:10.1109/pimrc.2016.7794658

Cited by 45 publications

(33 citation statements)

References 19 publications

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“…As a further development, the authors of [162] studied the energy efficiency optimization of MIMO-aided NOMA systems in the face of fading channels in conjunction with statistical CSI at the transmitter, where the energy efficiency was defined in terms of the ergodic capacity attained at unity power consumption. In [163] the energy efficiency optimization problem of a cognitive radio aided multiuser downlink NOMA system was investigated subject to an individual quality of service constraint for each primary user. An efficient algorithm based on the classic sequential convex approximation method was conceived for solving the corresponding non-convex fractional programming problem formulated.…”

Section: User Grouping and Resource Allocationmentioning

confidence: 99%

A Survey of Non-Orthogonal Multiple Access for 5G

Dai

Wang

Ding

et al. 2018

IEEE Commun. Surv. Tutorials

1,127

664

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Abstract-In the 5th generation (5G) of wireless communication systems, hitherto unprecedented requirements are expected to be satisfied. As one of the promising techniques of addressing these challenges, non-orthogonal multiple access (NOMA) has been actively investigated in recent years. In contrast to the family of conventional orthogonal multiple access (OMA) schemes, the key distinguishing feature of NOMA is to support a higher number of users than the number of orthogonal resource slots with the aid of non-orthogonal resource allocation. This may be realized by the sophisticated inter-user interference cancellation at the cost of an increased receiver complexity. In this article, we provide a comprehensive survey of the original birth, the most recent development, and the future research directions of NOMA. Specifically, the basic principle of NOMA will be introduced at first, with the comparison between NOMA and OMA especially from the perspective of information theory. Then, the prominent NOMA schemes are discussed by dividing them into two categories, namely, power-domain and code-domain NOMA. Their design principles and key features will be discussed in detail, and a systematic comparison of these NOMA schemes will be summarized in terms of their spectral efficiency, system performance, receiver complexity, etc. Finally, we will highlight a range of challenging open problems that should be solved for NOMA, along with corresponding opportunities and future research trends to address these challenges.

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Section: User Grouping and Resource Allocationmentioning

confidence: 99%

A Survey of Non-Orthogonal Multiple Access for 5G

Dai

Wang

Ding

et al. 2018

IEEE Commun. Surv. Tutorials

1,127

664

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“…Operation mechanism [7] SISO CR with NOMA Underlay [8] MIMO CR with NOMA Underlay [9] Large-scale CR with NOMA Underlay [10] Cooperative CR with NOMA Overlay [11] Cooperative multicast CR with NOMA Overlay [12] Cooperative CR with NOMA and STBC Overlay [13] MISO CR with NOMA √ Underlay [14] SISO CR with NOMA √ Underlay be inactive, otherwise it continues performing spectrum sensing in order to find available frequency bands.…”

Section: Resource Optimizationmentioning

confidence: 99%

“…In [13], the authors proposed an efficient algorithm to optimize the EE of underlay CRNs with NOMA based on the sequential convex approximation method. The considered CRNs with NOMA are general networks which consider an arbitrary number of PUs.…”

Section: A Resource Optimization In Crns With Nomamentioning

confidence: 99%

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State of the Art, Taxonomy, and Open Issues on Cognitive Radio Networks with NOMA

Zhou

Liang

et al. 2018

IEEE Wireless Commun.

200

124

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The explosive growth of mobile devices and the rapid increase of wideband wireless services call for advanced communication techniques that can achieve high spectral efficiency and meet the massive connectivity requirement. Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are envisioned to be important solutions for the fifth generation wireless networks. Integrating NOMA techniques into CR networks (CRNs) has the tremendous potential to improve spectral efficiency and increase the system capacity. However, there are many technical challenges due to the severe interference caused by using NOMA. Many efforts have been made to facilitate the application of NOMA into CRNs and to investigate the performance of CRNs with NOMA. This article aims to survey the latest research results along this direction. A taxonomy is devised to categorize the literature based on operation paradigms, enabling techniques, design objectives and optimization characteristics. Moreover, the key challenges are outlined to provide guidelines for the domain researchers and designers to realize CRNs with NOMA. Finally, the open issues are discussed.Cognitive radio, non-orthogonal multiple access, spectral efficiency, massive connectivity. I. INTRODUCTIONT HE explosive growth of mobile devices, the rapidly increasing demand on the broadband and highrate communication services, such as augmented reality (AR) and virtual reality (VR), and the fixed spectrum assignment policy result in the increasingly severe spectrum scarcity problem. According to the third Generation Partnership Project (3GPP), compared with the fourth generation (4G) networks, the fifth generation (5G) networks are required to achieve 1000 times higher system capacity, 10 times higher spectral efficiency (SE), and 100 times higher connectivity density [1]. Moreover, among these requirements, meeting the system capacity is the most important but probably the most challenging one due to the limited spectrum resource. Thus, it is imperative to develop advanced communication techniques that can achieve high SE as well as massive wireless connectivity. As a promising technique, cognitive radio (CR) has drawn significant attention in both industry and academia due to its high SE [2]. It can enable the secondary network (or called the unlicensed network) to access the licensed frequency bands of the primary network by using adaptive transmission strategies while protecting the quality-of-service (QoS) of the primary one.Besides CR, non-orthogonal multiple access (NOMA) techniques are promising to improve SE and user connectivity density [4], [5]. Unlike the conventional orthogonal multiple access (OMA) techniques, NOMA techniques allow multiple users simultaneously access the network at the same time and the same frequency band by using non-orthogonal resources, such as different power levels or low-density spreading codes. In [5], the authors have classified the existing dominant NOMA schemes into two categories based on the non-orthogonality resources, namely, power-...

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“…In addition, NOMA can be viewed as a special form of cognitive radio (CR) networks [24], in which the user having stronger channel gains is viewed as a secondary user while the user having poorer channel gains is viewed as a primary one. In [25], the above described CR inspired NOMA framework was generalized by exploiting multiple antenna technologies and QoS guarantees for weaker users.…”

Section: A Related Work and Motivationsmentioning

confidence: 99%

Non-Orthogonal Multiple Access Assisted Multi-Region Geocast

et al. 2018

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This work proposes a novel location-based multigroup multicast framework which is termed as non-orthogonal multiple access (NOMA) assisted multi-region geocast. This novel spectrum sharing framework exploits the NOMA technology to realize the simultaneous delivery of different messages to different user groups characterized by different geographical locations. The essence of the proposed framework is that the geographical information of user groups unites NOMA and multigroup multicast to enhance the spectral efficiency (SE) and the energy efficiency (EE) of wireless transmissions. Specifically, we investigate the downlink beamforming design of the proposed framework in multiple-input single-output (MISO) settings. The decoding strategy for NOMA is designed and guaranteed by users' geographical information and required quality of service (QoS). The majorization and minimization (MM) algorithm is exploited to solve the non-convex and intractable problems therein.Comprehensive numerical experiments are further provided to show that NOMA holds tremendous promise but also limitations in terms of SE and EE, compared with spatial division multiple access (SDMA) and orthogonal multiple access (OMA).Index Terms-Non-orthogonal multiple access, multi-group multicast, geographical information, design of decoding order, quality of service.

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Energy efficiency optimization in cognitive radio inspired non-orthogonal multiple access

Cited by 45 publications

References 19 publications

A Survey of Non-Orthogonal Multiple Access for 5G

A Survey of Non-Orthogonal Multiple Access for 5G

State of the Art, Taxonomy, and Open Issues on Cognitive Radio Networks with NOMA

Non-Orthogonal Multiple Access Assisted Multi-Region Geocast

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