Reconfigurable intelligent surfaces (RISs) constitute a promising performance enhancement for next-generation (NG) wireless networks in terms of enhancing both their spectral efficiency (SE) and energy efficiency (EE). We conceive a system for serving paired power-domain non-orthogonal multiple access (NOMA) users by designing the passive beamforming weights at the RISs. In an effort to evaluate the network performance, we first derive the best-case and worst-case of new channel statistics for characterizing the effective channel gains. Then, we derive the best-case and worst-case of our closed-form expressions derived both for the outage probability and for the ergodic rate of the prioritized user. For gleaning further insights, we investigate both the diversity orders of the outage probability and the high-signalto-noise (SNR) slopes of the ergodic rate. We also derive both the SE and EE of the proposed network. Our analytical results demonstrate that the base station (BS)-user links have almost no impact on the diversity orders attained when the number of RISs is high enough. Numerical results are provided for confirming that: i) the high-SNR slope of the RIS-aided network is one; ii) the proposed RIS-aided NOMA network has superior network performance compared to its orthogonal counterpart.

This article investigates the multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) assisted unmanned aerial vehicles (UAVs) networks. By utilizing a stochastic geometry model, a new 3-Dimension UAV framework for providing wireless service to randomly roaming NOMA users has been proposed. In an effort to evaluate the performance of the proposed framework, we derive analytical expressions for the outage probability and the ergodic rate of MIMO-NOMA enhanced UAV networks. We examine tractable upper bounds for the whole proposed framework, with deriving asymptotic results for scenarios that transmit power of interference sources being proportional or being fixed to the UAV. For obtaining more insights for the proposed framework, we investigate the diversity order and high signal-to-noise (SNR) slope of MIMO-NOMA assisted UAV networks. Our results confirm that: i) The outage probability of NOMA enhanced UAV networks is affected to a large extent by the targeted transmission rates and power allocation factors of NOMA users; and ii) For the case that the interference power is proportional to the UAV power, there are error floors for the outage probabilities. Index TermsMIMO, non-orthogonal multiple access, signal alignment, stochastic geometry, unmanned aerial vehicles.

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This paper advocates a pair of strategies in non-orthogonal multiple access (NOMA) in unmanned aerial vehicles (UAVs) communications, where multiple UAVs play as new aerial communications platforms for serving terrestrial NOMA users. A new multiple UAVs framework with invoking stochastic geometry technique is proposed, in which a pair of practical strategies are considered: 1) the UAV-centric strategy for offloading actions and 2) the user-centric strategy for providing emergency communications.In order to provide practical insights for the proposed NOMA assisted UAV framework, an imperfect successive interference cancelation (ipSIC) scenario is taken into account. For both UAV-centric strategy and user-centric strategy, we derive new exact expressions for the coverage probability. We also derive new analytical results for orthogonal multiple access (OMA) for providing a benchmark scheme. The derived analytical results in both user-centric strategy and UAV-centric strategy explicitly indicate that the ipSIC coefficient is a dominant component in terms of coverage probability. Numerical results are provided to confirm that i) for both user-centric strategy and UAV-centric strategy, NOMA assisted UAV cellular networks is capable of outperforming OMA by setting power allocation factors and targeted rate properly; and ii) the coverage probability of NOMA assisted UAV cellular framework is affected to a large extent by ipSIC coefficient, target rates and power allocations factors of paired NOMA users. Index TermsNon-orthogonal multiple access, stochastic geometry, unmanned aerial vehicles. Regarding the literature of UAV networks, early research contributions have studied the performance of single UAV or multiple UAVs networks. Mozaffari et al. [3] proposed a UAV assisted underlaid D2D network with LoS probability, which depends on the height of the UAV, the horizontal distance between the UAV and users, the carrier frequency and type of environment. In the case that LoS exists, a fixed LoS coefficient, e.g., an extra 20dB attenuation, is the dominant component of small-scale fading channels. Note that the proposed model in [3], [4] is a practical model for implementation. For mathematically tractable, the distinctive channel characteristics for UAV networks were investigated in [13], where different types of small-scale fading channels, i.e., Loo model, Rayleigh model, Nakagami-m model, Rician model and Werbull model, were summarized to demonstrate the channel propagation of UAV networks. The air-to-air channel characterization in [14], studied the influence of the altitude-dependent Rician K factor. This work indicated that the impact of the ground reflected multi-path fading reduces with increasing UAV altitude. Jiang et al. [15] proposed a UAV assisted ground-to-air network, where Rician channels are used for evaluating strong LoS components between UAV and ground users. It is also worth noting that Rayleigh fading channel, which is a well-known model in scattering environment, can be also used to model the UAV ch...

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In resource constraint wireless systems, achieving higher spectral efficiency (SE) and energy efficiency (EE), and greater rate fairness are conflicting objectives. Here a general framework is presented to analyze the tradeoff among these three performance metrics in cooperative OFDMA systems with decode-and-forward (DF) relaying, where subcarrier pairing and allocation, relay selection, choice of transmission strategy, and power allocation are jointly considered. In our analytical framework, rate fairness is represented utilizing α-fairness model and the resource allocation problem is formulated as a multiobjective optimization (MOO) problem. We then propose a cross-layer resource allocation algorithm across application and physical layers, and further devise a heuristic algorithm to tackle the computational complexity issue. The SE-EE tradeoff is characterized as a Pareto optimal set, and the efficiency and fairness tradeoff is investigated through the price of fairness (PoF). Simulations indicate that higher fairness results in a worse SE-EE tradeoff. It is also shown imposing fairness helps to reduce the outage probability. For a fixed number of relays, by increasing circuit power, the performance of SE-EE tradeoff is degraded. Interestingly, by increasing the number of relays, although the total circuit power is increased, the SE-EE tradeoff is not necessarily degraded. This is thanks to the extra degree of freedom provided in relay selection.

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BackgroundUnlike influenza viruses, little is known about the prevalence and seasonality of other respiratory viruses because laboratory surveillance for non-influenza respiratory viruses is not well developed or supported in China and other resource-limited countries. We studied the interference between seasonal epidemics of influenza viruses and five other common viruses that cause respiratory illnesses in Hong Kong from 2014 to 2017.MethodsThe weekly laboratory-confirmed positive rates of each virus were analyzed from 2014 to 2017 in Hong Kong to describe the epidemiological trends and interference between influenza viruses, respiratory syncytial virus (RSV), parainfluenza virus (PIV), adenovirus, enterovirus and rhinovirus. A sinusoidal model was established to estimate the peak timing of each virus by phase angle parameters.ResultsSeasonal features of the influenza viruses, PIV, enterovirus and adenovirus were obvious, whereas annual peaks of RSV and rhinovirus were not observed. The incidence of the influenza viruses usually peaked in February and July, and the summer peaks in July were generally caused by the H3 subtype of influenza A alone. When influenza viruses were active, other viruses tended to have a low level of activity. The peaks of the influenza viruses were not synchronized. An epidemic of rhinovirus tended to shift the subsequent epidemics of the other viruses.ConclusionThe evidence from recent surveillance data in Hong Kong suggests that viral interference during the epidemics of influenza viruses and other common respiratory viruses might affect the timing and duration of subsequent epidemics of a certain or several viruses.