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Non-orthogonal multiple access (NOMA) is an essential enabling technology for the fifth-generation (5G) wireless networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The key idea behind NOMA is to serve multiple users in the same resource block, such as a time slot, subcarrier, or spreading code. The NOMA principle is a general framework, and several recently proposed 5G multiple access schemes can be viewed as special cases. This survey provides an overview of the latest NOMA research and innovations as well as their applications. Thereby, the papers published in this special issue are put into the context of the existing literature. Future research challenges regarding NOMA in 5G and beyond are also discussed.

A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends

Non-orthogonal multiple access (NOMA) is an essential enabling technology for the fifth generation (5G) wireless networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The key idea behind NOMA is to serve multiple users in the same resource block, such as a time slot, subcarrier, or spreading code. The NOMA principle is a general framework, and several recently proposed 5G multiple access schemes can be viewed as special cases. This survey provides an overview of the latest NOMA research and innovations as well as their applications. Thereby, the papers published in this special issue are put into the content of the existing literature. Future research challenges regarding NOMA in 5G and beyond are also discussed.

In the fifth 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 paper, 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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A Survey of Non-Orthogonal Multiple Access for 5G

Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.

UAV Communications for 5G and Beyond: Recent Advances and Future Trends

In the existing work of unmanned aerial vehicle base station (UAV-BS) deployment, where serving radius (SR) is fixed regardless of resource waste and overlapping interference, dynamic tunability of SR has not been considered. In this letter, a dynamic tunable model where SR can be properly adjusted is proposed, based on which a semi-progressive offloading deployment scheme devoted to UAV number and overlapping interference optimization is raised. Specifically, UAV is deployed around macro base stations (MBSs), where a certain proportion ( $\gamma $ ) of the ground terminals (GTs) are covered first with the principle of minimum overlap and maximum coverage, then through tunability of SR, the rest GTs are covered without increasing UAV number. Moreover, the base station expands its SR to cover the closest uncovered GT through fine adjustment of power (MBS) or antenna down-tilt (UAV), after which surrounding UAVs are also adjusted to reduce overlapping interference. The results show our proposed scheme reduces 17.26% UAV quantity compared with the cluster method and decreases 71.75% interference compared with the core-set method.

Deployment Optimization of UAV-Aided Networks Through a Dynamic Tunable Model

To enhance the network coverage and capacity, unmanned aerial vehicle (UAV) communication has become a promising technology thanks to the high mobility and the flexible deployment of UAVs. However, the consistency of the service is not guaranteed due to the limitation single UAV base station (BS), leading to the research for the user-centric UAV swarm networks. This paper derives the semi-closed-form expressions of coverage probability and average achievable rate for the user-centric UAV swarm networks to characterize the performance gain obtained from the increased UAV swarm diversity. Especially, the user-centric UAV swarm networks organize a dynamic UAV swarm for each user consisting of potential serving UAVs in user's vicinity, which gives the design of boundaryless cells to serve each user seamlessly without user's involvement. By modeling the locations of the UAVs as Poisson point process (PPP) and modeling the channel fading as Nakagami-$m$ distribution, the analytical results are calculated under two transmission modes, depending on whether cell coordination is considered or not. The results show that the coverage performance can be improved by 30% without cell coordination and 50% with cell coordination in comparison with traditional cell-centric networks when the UAV altitude is 100 m and SINR threshold is 0 dB.

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User-Centric Intelligent UAV Swarm Networks: Performance Analysis and Design Insight

As densification and heterogeneity are the promising trends of future mobile networks, deployment of base stations (BSs) becomes increasingly difficult due to the laborious procedures of alternating optimization between field measurements and coverage evaluation via propagation models. In this paper, we present a coverage evaluation tool based on machine learning (ML) which is free from propagation model. In particular, received signal strengths (RSSs) reported by users and factors that affect the transmitted signal, such as distance, geography and configuration parameters of BS, are collected and used to train a classifier which allows to predict the RSS at the user side. In this way, the complicated procedures to obtain propagation model can be skipped, which reduces the great cost in coverage evaluation. Moreover, in order to acquire better prediction performance, hyper-parameters optimization is performed via exhaustive grid search methods in each learning algorithm. Simulations are conducted by using vast real-world data for a better training result. We also compare the performance by means of different machine learning algorithms and the numerical results show that Support Vector Machine (SVM) outperforms other classifiers in terms of prediction accuracy, which is up to 86.7%.

Propagation-model-free Coverage Evaluation via Machine Learning for Future 5G Networks

Ultra-Dense Networks (UDNs) is a clear trend for future 5G while dense deployment of small cells (SCs) would result in unnecessary handovers (UHOs) due to a shorter sojourn time in the target SC and thus user experience is degraded. The main reason for this problem is that existing small cell selection (SCS) schemes ignore the importance of sojourn time. Therefore, in this paper, a SCS scheme based on sojourn time estimation is proposed. The Cumulative Distribution Function of the sojourn time depending on the user velocity and SC intensity is presented firstly. Furthermore, the instantaneous sojourn time is calculated by estimating the chord length of user trajectory in the coverage of candidate SC, in which user direction is obtained by applying a direction prediction scheme meanwhile the coverage of SC is determined by Poisson-Voronoi Tessellations (PVTs). In addition, the optimal sojourn time threshold is determined for the purpose of lower handover failures and those candidate SCs whose sojourn time are shorter than the predefined threshold would be skipped. Simulation results show that compared with a typical parameters setting (TTT=320ms, Offset=3dB) in LtE, the proposed scheme can reduce the Ping Pong (PP) rate and UHO rate by up to 50% and 60%, respectively. Moreover, the handover failures can be reduced by up to 30%.

Sojourn time estimation-based small cell selection in Ultra-Dense Networks

As densification is the promising trend of future mobile networks, deployment of base stations (BSs) becomes increasingly difficult due to the laborious procedures in network planning; besides, unreasonable layout may lead to poor coverage performance. Hence, this paper firstly trains a propagation-model-free received signal strength (RSS) predictor based on machine learning (ML) models, and then optimizes coverage performance of BS deployment via multi-objective heuristic methods. Specifically, many practical features that affect signal propagation like geographical types and operating parameters of BS, are fed into ML models to predict RSS in a rasterized area; then based on the trained model, a well-designed multi-objective genetic algorithm (GA) is proposed to minimize the number of deployed BSs with coverage constraint. For the practical considerations of fast convergence and output-consistence, greedy algorithm with fixed initial solution and searching direction is also carried out. Moreover, the typical scenarios of incremental deployment (the mobile operator needs to deploy more BSs on the basis of the existing deployment) and BS outage compensation (one BS fails and other BSs need to adjust their configurations to fill the coverage gap), are also investigated for practical needs. Simulations show that multi-layer perceptron outperforms other ML algorithms in terms of RSS prediction with mean absolute error (MAE) yielded to 3.78 dB; and numerical results verify the convergence and availability of the proposed algorithms, which shows 18.5% gain than the real-world deployment in terms of coverage rate.

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Hongtao Zhang

Papers

Deployment Optimization of UAV-Aided Networks Through a Dynamic Tunable Model

User-Centric Intelligent UAV Swarm Networks: Performance Analysis and Design Insight

Propagation-model-free Coverage Evaluation via Machine Learning for Future 5G Networks

Sojourn time estimation-based small cell selection in Ultra-Dense Networks

Propagation-Model-Free Base Station Deployment for Mobile Networks: Integrating Machine Learning and Heuristic Methods