A joint resource allocation policy for an underlay multi-user secure cognitive radio network (CRN) adopting the time-division multiple-access scheme with the guarantee of the statistical delay quality-of-service is proposed in this paper. Through the problem formulation of the resource allocation for the multi-user secure CRN with perfect channel state information (CSI), the energy-efficient joint power and time optimization scheme is proposed, which aims at minimizing the β-fair cost functions subject to fairness, delay, and security constraints. Furthermore, considering the multi-user secure CRN with imperfect CSI, we also propose a robust resource allocation scheme and the tradeoff between the robustness and the achievable sum-power is analyzed. Numerical results show that the optimal resource allocation scheme with perfect CSI can effectively balance the tradeoff between the energy efficiency and the fairness and is superior to the other conventional resource allocation strategies. Moreover, the robust resource allocation policy with imperfect CSI makes the theoretical research much closer to the practical applications.

How to improve the doctor-patient relationship has been a continuing topic in academia and management and there have been several attempts to utilize online communications to facilitate healthcare. The application of information technology to create an "Internet + medical care" platform has upended the traditional medical service model in China. As an example of the application of the Internet by the healthcare system, this paper investigates a mobile online appointment system used by hospitals. Data on system use came from questionnaires submitted by 225 patients and analyzed by the structural equation model method. The results showed that patients perceived the hospital's online appointment system as an attempt at transparency to which they reacted positively. The patients' perception of transparency promoted trust in the hospital and the doctors and positively affected their feelings of satisfaction, which, of course, improved the doctor-patient relationship. Patients' perceptions of transparency, trust in the hospitals and doctors, and feelings of satisfaction played a partial mediator role between the availability of an online appointment system and better doctor-patient relationships. There were significant gender differences among patients in terms of their feelings of trust and satisfaction with the new appointment method and whether it really improved the doctor-patient relationship.

This paper studies the robust resource allocation to improve the robustness against the channel state information (CSI) uncertainties for energy harvesting (EH)-aided device-to-device (D2D) communication underlaying cellular network. In this network, EH-aided D2D transmitters can reuse the uplink cellular sub-channels to perform transmission to the desired D2D receivers. Assuming the link gains in the interference channels are uncertain, we formulate the robust resource allocation problem with a view to maximizing the sum-rate of the D2D links while guaranteeing the power outage Quality-of-Service (QoS) requirements and interference constraints. In doing so, both deterministic and statistical uncertainty model are adopted to specify the CSI uncertainties. This resulting robust problem is essentially non-convex and intractable in its original form. Therefore, we firstly utilize the worst-case approach and chance constraint approach to find its tractable and convex forms. Then, via employing the Lagrangian dual method, we derive the semi-closed expression for the power allocation and two rules for assigning sub-channels. Based on the theoretical results, a distributed subgradient-based iterative algorithm is developed to achieve the optimally robust resource allocation. Finally, an in-depth simulation is conducted to verify the effectiveness of the proposed algorithm and evaluate the effect of CSI uncertainties and power outage QoS requirement on the sum-rate of the D2D links.

Given that an increasing number of online healthcare channels play an essential role as a supply method in the healthcare service supply chain (HSSC), this paper studies the price decision-making problem for a dual-channel HSSC considering the channel acceptance, price ceiling, and public welfare. In this HSSC, a healthcare institution establishes both a traditional offline channel and an online channel to provide healthcare services for some health conditions. Considering the public welfare of healthcare institutions, we employ a sum formula of economic revenue and patient surplus to describe the total revenue of both healthcare service channels. Based on the Stackelberg game, we develop a decentralized supply chain model to maximize supply chain members’ revenue. By employing the $Karush$–$Kuhn$–$Tucker$ optimality condition, we derive an analytical expression for the optimal service price, which includes the functions of the public welfare coefficient and channel acceptance. Finally, we conduct extensive numerical analyses under various system parameters to verify the optimal price decision-making strategies. Our analytical results indicate that: (1) the healthcare service price is closely related to the patients’ channel acceptance, the public welfare coefficient, and the government price ceiling policy; (2) the public welfare coefficient strongly influences the service price and total revenue, and its increase can decrease the economic revenue of the HSSC; (3) the acceptance of online channels is an essential factor that should be carefully considered in the construction of a dual-channel HSSC. Improving patient acceptance of online channels is conducive to developing and improving a sustainable dual-channel HSSC.

We propose a sustainability-driven resource allocation algorithm for energy harvesting powered device-to-device (D2D) communication underlaying cellular networks. In this setup, D2D transmitters harvest energy from ambient energy sources and reuse the uplink cellular channels to perform transmission to the desired D2D receivers. Considering the time-varying and unpredictable nature of the harvested energy, the proposed algorithm ensures the energy sustainability-related quality-of-service with improved system capacity. To reach this goal, a statistical model based on the effective bandwidth/capacity is adopted to formulate the energy-harvesting/-consuming processes and evaluates the sustainability assurance in terms of the statistical outage exponent. Subsequently, we formulate the resource allocation problem with a view to maximizing the sum-rate of the D2D links by jointly optimizing the power allocation and spectrum resource matching, meanwhile guaranteeing the energy sustainability requirements. By employing the Lagrangian dual method, we derive an analytical expression for the transmission power allocation, and present two rules to match the cellular users with D2D links for spectrum resource reuse. Our results reveal that both the power allocation and spectrum resource matching closely depend on the statistical outage exponent. With these theoretical analyses, we put forward a distributed subgradient-based iterative resource allocation algorithm with polynomial complexity. Finally, simulation results demonstrate that the proposed resource allocation algorithm can acquire a substantial sum-rate improvement.