In this paper, we investigate the performance of covert communications in different types of a relay system: decode-and-forward (DF), compress-and-forward (CF) and amplify-and-forward (AF). We ...consider a source node that attempts to send both public and covert messages to a destination node through a relay on which a covert message detector is embedded. By taking the minimum detection error probability (DEP) at the relay into account, we optimize the power distribution between the public and covert messages to achieve the maximum covert rate. We further make a delay-aware comparison among DF, CF and AF relay systems with the obtained closed-form covert rates and conduct an extensive examination on the asymptotic behaviors in different limits. Our analyses reveal that CF or AF tend to outperform DF for high source transmit power or low relay transmit power, while various system parameters such as the processing delay, minimum required quality of service for public messages and DEP threshold lead to different performance relationships among DF, CF and AF for high relay transmit power. Numerical results verify our investigation into the performance comparison in various channel models.
Covert communications have arisen as an effective communications security measure that overcomes some of the limitations of cryptography and physical layer security. The main objective is to ...completely conceal from external devices the very existence of the link for exchanging confidential messages. In this paper, we take a step further and consider a scenario in which a covert communications node disguises itself as another functional entity for even more covertness. To be specific, we study a system where a source node communicates with a seemingly receive-only destination node which, in fact, is full-duplex (FD) and covertly delivers critical messages to another hidden receiver while evading the surveillance. Our aim is to identify the achievable covert rate at the hidden receiver by optimizing the public data rate and the transmit power of the FD destination node subject to the worst-case detection error probability (DEP) of the warden. Closed-form solutions are provided, and we investigate the effects of various system parameters on the covert rate through numerical results, one of which reveals that applying more (less) destination transmit power achieves a higher covert rate when the source transmit power is low (high). Since our work provides a performance guideline from the information-theoretic point of view, we conclude this paper with a discussion on possible future research such as analyses with practical modulations and imperfect channel state information.
In this paper, we study covert communications strategies in a compress-and-forward (CF) relay system. Along with a public message to a destination node via a CF relay, a source node attempts to ...transmit a covert message while evading the surveillance of the CF relay. We identify the optimal power distribution between the public and covert messages and the optimal amount of compression that maximize the covert rate subject to the minimum detection error probability requirement. Our provided solutions also reveal that both the power distribution and the achievable covert rate are identical to that of an equivalent amplify-and-forward (AF) relay system if an adequate quantization codebook with the optimal compression is employed. The numerical results verify the effectiveness of the optimal solutions and confirm our analyses.
Pandemics, natural disasters (e.g., hurricanes, droughts, fires), strikes, piracy, and other events can unexpectedly disrupt supply or spike demand, creating shortages. Unlike ordinary stockouts ...caused by store-specific inventory policies, shortages involve the entire supply chain. One tool for managing shortages is imposing purchase limits. Purchase limits restrict the quantity each shopper can purchase of the scarce product (e.g., gasoline, toilet paper, sanitizers, meat, batteries), possibly increasing availability to other shoppers. Although altruistic stores might use purchase limits for egalitarian goals (e.g., reducing hoarding, waste, panic buying, arbitrage, unfair distribution), the authors find that profit-maximizing stores can use purchase limits to increase profits during shortages. These findings suggest that stores’ price-and-limit strategies depend on shortage severity, store size, competition, and seasonality. For moderate shortages, large multiproduct stores, where average shopping basket sizes are large, should maintain low prices and impose limits, whereas small stores should increase prices and not impose limits. For severe shortages, by contrast, large stores should keep low prices but not impose limits, whereas small stores should increase prices and impose limits. Generally, large stores benefit from increased future store traffic when they impose limits. Interestingly, purchase limits can improve both store profits and, with lower prices, consumer surplus.
In this paper, we consider a wireless powered communication network with an energy harvesting (EH) jammer where eavesdroppers try to wiretap the communication between users and a hybrid access-point ...(H-AP). In our system, the H-AP first transmits an energy signal to recharge the batteries of the EH users and the EH jammer in the energy transfer (ET) phase. Then, in the subsequent information transfer (IT) phase, each user sends information to the H-AP in a time division multiple access manner, while the jammer generates jamming signals to interfere the eavesdroppers. We adopt two different secrecy performance measurements according to the level of channel state information (CSI) of the eavesdroppers. First, with a single user, we maximize the secrecy rate by optimizing the time allocation between the ET and the IT phase when perfect CSI of the eavesdroppers is available at all nodes. In contrast, when the instantaneous CSI of the eavesdroppers is not available at legitimate nodes, we analyze and minimize the secrecy outage probability. We also extend the single user analysis to a more general multi-user situation with an additional consideration of the transmit power allocation at the jammer. Finally, we evaluate the performance of our proposed solutions through simulations and demonstrate that a performance gain compared to conventional schemes becomes more pronounced with the increased number of eavesdroppers and users.
This article studies the intersection between the largest U.S. industry—health care—and the $1 trillion nonprofit sector. Using analytical and empirical analyses, the authors reveal the marketing ...strategies helping private nonprofit hospitals achieve higher output, prices, and profits than for-profit hospitals. Nonprofit hospitals, focusing on both profits and output, obtain these outcomes by expanding their service mix with high-priced premium specialty medical services (PSMS), whereas for-profit hospitals can be more profitable with higher prices for basic services. Competition increases the differences between nonprofit and for-profit hospitals in PSMS breadth, output, and prices. Nonprofit hospitals lose their competitive advantage when competing with other nonprofits; that is, presence of a for-profit competitor broadens available nonprofit PSMS. With broader service mixes, nonprofits focus more on national advertising than for-profits because PSMS (e.g., pediatric trauma, neurosurgery, heart transplants, oncology) require larger geographic markets than local basic services (e.g., laboratory, diagnostics, nursing, pharmaceutics). Exogenous, heterogeneous state regulations restricting for-profit hospital entry help econometric identification (i.e., markets prohibiting for-profits act as controls). Service mix may be a key difference between nonprofit and for-profit hospitals.
This paper studies multiple-input multiple-output multiple access wiretap channels (MAC-WT) where an eavesdropper tries to tap the communication between multiple legitimate transmitters and a ...legitimate receiver. In this system, we propose precoder optimization methods at the transmitters in order to maximize the sum secrecy rate performance. Although this problem can be solved by the well-known difference of convex (DC) programming, we present a more efficient algorithm whose computational complexity is much lower than that of the conventional DC approach. By investigating the Karush-Kuhn-Tucker conditions, it is confirmed that the proposed low-complexity algorithm achieves the same performance as the conventional DC method. Our analysis also reveals that the proposed algorithm ensures global optimality for multiple-input single-output MAC-WT cases, while binary power control is optimal for single-input multiple-output scenarios. Simulation results demonstrate the efficacy of the proposed precoding methods.
Federated learning (FL) is a decentralized machine learning (ML) method that enables model training while preserving privacy. FL is gaining attention because it avoids data transfer to the server, ...facilitating the decentralized learning of the traditional ML model. Despite its potential, FL project is significantly more challenging to develop than centralized ML methods owing to decentralized local data. We propose FedOps , federated learning operations for constructing systematic FL project by enhancing machine learning operations (MLOps) to be effectively applied to FL while preserving its core process. To address complexity of FL implementation, we developed FedOps platform , which involves FedOps -based projects to manage the whole lifecycle in FL context. We also investigated methods to identify performance degradation factors in FL and suggest an approach for improvement. FedOps Platform provides an analysis tool for client heterogeneity, called chunk-bench . This tool enables researchers and engineers to gain insights into systems heterogeneity by using only small chunk of the clients' data to execute test in the shortest time possible while tracking the systems heterogeneity across the clients. By addressing systems heterogeneity, FedOps Platform achieved 13%-43% improvement in communication cost-to-accuracy and 20%-68% improvement in time-to-accuracy. We believe that FedOps Platform offers an optimal solution for end-to-end development of FL projects, with significantly improving both computational and communication efficiencies.
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on ...demand with minimal average latency from a time-varying library of popular contents. Uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this letter proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL.
Federated learning (FL) that can train using machine learning methods without moving data have attracted interest owing to the focus on data privacy. Several FL platforms and frameworks are being ...developed with various open datasets. However, FL has not yet been fully utilized in real-world projects; instead, centralized ML models are still being used for AI. Researchers who develop these models should be able to easily and conveniently apply custom data and models developed in a centralized environment to FL environments, deploy and train multiple clients, and manage the lifecycle of the entire FL process. This study proposes FLScalize to enable AI researchers to conveniently apply their own custom data and models to FL environments that can occur in the real world and to deploy and manage the FL lifecycle. FLScalize can be used to simulate system heterogeneity and data heterogeneity, both of which are FL issues that occur in real FL environments. Furthermore, FLScalize provides a manager component that continuously manages the FL client and server required for real-world FL tasks and realizes an FL lifecycle management implementation that enables continuous integration, deployment, and training.
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