A Systematic Review on NOMA Variants for 5G and Beyond

Budhiraja, Ishan; Kumar, Neeraj; Tyagi, Sudhanshu; Tanwar, Sudeep; Han, Zhu; Piran, Md. Jalil; Suh, Doug Young

doi:10.1109/access.2021.3081601

Cited by 101 publications

(32 citation statements)

References 357 publications

(510 reference statements)

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“…However, the connection scale and transmission performance in NOMA systems largely depends on the resource allocation scheme [18]. Moreover, heterogeneous network (HetNet) [19], as a promising network infrastructure in 5G, can not only enhance the network capacity and user experience of the edge cell, but also reduce coverage blind spots and improve the transmission performance of communication systems. The traditional HetNet is mainly composed of two types of base stations (BSs), namely macro base station (MBS) and small base station (SBS) [20].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Resource Allocation for NOMA-Based Heterogeneous 5G Mine Internet of Things

2022

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The Mine Internet of Things (MIoT), as an application paradigm of the IoT in mine environment, realizes the state perception and information interaction by connecting massive smart sensing devices deployed in mine, plays an important role in coal mine production. The fifth-generation (5G) is a key enabling technology to provide an efficient and reliable communication link guarantee for MIoT networks. To meet the increasing demand for huge amounts of data transmission, a NOMA-based heterogeneous MIoT communication system is proposed. In this paper, different types of mine smart devices can occupy the same subchannel resources for data transmission by the NOMA technology, which improves the device access and spectrum utilization of the MIoT system. We aim to maximize the energy efficiency (EE) of all small cell networks via power allocation and subchannel assignment. Under the imperfect channel state information (CSI), a joint power allocation and subchannel assignment iterative algorithm is proposed. Specifically, firstly, by considering the cross-layer interference power constraints, maximum power constraints and QoS constraints, the EE optimization problem is formulated as a mixed integer nonlinear fractional programming problem. Secondly, the uncertain CSI is modeled as an elliptical uncertainty set, and the original problem is transformed into an equivalent convex optimization form by using the Dinkelbach method. Finally, an approximate solution is obtained by using the Lagrangian dual approach. The numerical results validate the effectiveness of the proposed algorithm and significantly show its superior performance as compared with the baseline algorithms.INDEX TERMS Coal mine, 5G, internet of things (IoT), non-orthogonal multiple access (NOMA), heterogeneous network (HetNet), energy efficient, imperfect channel state information

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Section: Introductionmentioning

confidence: 99%

Energy-Efficient Resource Allocation for NOMA-Based Heterogeneous 5G Mine Internet of Things

2022

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“…Channel resource sharing techniques have been gaining importance due to the necessity of achieving higher spectral efficiency in the next generations of wireless communications systems [1]. Among them, power domain non-orthogonal multiple access (NOMA) is an important technique in accomplishing this task [2], which is also frequently linked to 5G technologies [3]. In this scheme, signals from multiple users with different power levels share the same channel resources, and the successive interference cancellation (SIC) is the usual decoding method, in which the decoding at each user is performed sequentially according to the ordering of their power levels.…”

Section: Introductionmentioning

confidence: 99%

Successive Interference Cancellation Decoding With Adaptable Decision Regions for NOMA Schemes

et al. 2022

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In this work, we propose adaptable decision regions in the successive interference cancellation (SIC) decoder for the three-user uplink/downlink non-orthogonal multiple access (NOMA) scheme by exploiting the knowledge of the channel gains. We present scenarios in which the modified SIC decoder outperforms the traditional one and is able to replicate the performance achieved by a deep learning-based decoder. We present analytical symbol error rate (SER) expressions for the three users, compare them to simulation results, and discuss possible consequences of our analysis, such as the optimal power allocation. We also employ heatmaps to analyze the joint influence on the SER of some system parameters, such as signal-to-noise ratio and channel gains.INDEX TERMS Non-orthogonal multiple access, successive interference cancellation, symbol error rate, decision regions.

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“…The massive deployment of fifth-generation (5G) broadband wireless cellular networks has enabled the transition towards advanced features and services, such as ultra-reliable low latency communications (URLLC), enhanced mobile broadband as well as massive machine type communications (mMTC) [1], [2]. In this context, various novel technologies have been developed in the physical layer, such as nonorthogonal multiple access (NOMA) [3], that can leverage spectral efficiency (SE), milimmiter wave (mmWave) trans-mission [4], as well as massive multiple input multiple output (m-MIMO) configurations [5]. In the latter case, a very large number of transmitting antennas is deployed over various access points (APs) of the wireless orientation in order to support high data rate transmission in multiple mobile stations (MSs) as well as seamless end-to-end connectivity.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

et al. 2022

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The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fifth-generation millimeter-wave multicellular networks, where massive multiple-input multiple-output configurations are employed in all active base stations of the considered orientations. In this context, beamforming is performed with the help of a predefined set of configurations that can deal with various traffic scenarios by properly generating highly directional beams on demand. In parallel, a machine learning (ML) beamforming approach based on the k-nearest neighbors (k-NN) approximation has been considered as well, which is trained in order to generate the appropriate beamforming configurations according to the spatial distribution of throughput demand. Performance is evaluated statistically, via a developed system level simulator that executes Monte Carlo simulations in parallel. Results indicate that the achievable spectral efficiency (SE) and energy efficiency (EE) values are aligned with other state of the art approaches, with reduced hardware and algorithmic complexity, since per user beamforming calculations are omitted. In particular, considering a two-tier cellular orientation, then in the non-ML approach EE and SE can reach up to 5 Mbits/J and 36 bps/Hz, respectively. Both metrics attain the aforementioned values when the MLassisted beamforming framework is considered. However, beamforming complexity is further reduced, since the ML approach provides a direct mapping among the considered throughput demand and appropriate beamforming configuration.

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A Systematic Review on NOMA Variants for 5G and Beyond

Cited by 101 publications

References 357 publications

Energy-Efficient Resource Allocation for NOMA-Based Heterogeneous 5G Mine Internet of Things

Energy-Efficient Resource Allocation for NOMA-Based Heterogeneous 5G Mine Internet of Things

Successive Interference Cancellation Decoding With Adaptable Decision Regions for NOMA Schemes

A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

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