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1. Study of Energy Efficiency for Satellite Communication Subsystems by Differential Game

   https://doi.org/10.11159/cdsr23.206  

   Wan, Wei; Cioffi, John M; Peng, Yuanyuan; Howard, Brenay S (June 2023, INTERNATIONAL ASET INC.)

   This paper studies a satellite transponder’s communication channel, in which there exist multiple-user terminals, who compete for limited radio resources to meet their own data rate needs. Because inter-user interference limits on the satellite transponder’s performance, the transponder’s power-control system needs to coordinate all its users to reduce interference and maximizes overall performance. A non-cooperative Differential Game (DG) is set up to model the users’ competition in a transponder’s communication channel. Each user’s utility function is a trade-off between transmission data rate and power consumption. Nash Equilibrium (NE) is defined to be the solution of the DG model. The optimality condition of NE is derived to be a set of Differential Algebraic Equations (DAE). An algorithm based on minimizing Hamiltonians is developed to solve the DAE system. The numerical solution of the DG model provides the transponder’s power control system with each user’s power-control strategy at the equilibrium. 

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2. Stackelberg Differential Game Analysis of Energy Efficiency for Satellite Communication Subsystems

   https://doi.org/10.11159/cdsr24.121  

   Wan, Wei; Peng, Yuanyuan; Cioffi, John M; Glover, Kalia T (June 2024, INTERNATIONAL ASET INC.)

   The multiple-user terminals in a satellite transponder’s communication channel compete for limited radio resources to meet their own data rate needs. Because inter-user interference limits on the satellite transponder’s performance, the transponder’s power-control system needs to coordinate all its users to reduce interference and maximizes overall performance of this channel. This paper studies Stackelberg competition among the asymmetrical users in a transponder’s channel, where some users called leader have priority to choose their power control strategy, but other users called followers have to optimize their power control strategy with given leader’s controls. A Stackelberg Differential Game (SDG) is set up to model the Stackelberg competition in a transponder’s communication channel. Each user’s utility function is a trade-off between transmission data rate and power consumption. The dynamics of the system is the changing of channel gain. The optimality condition of Stackelberg equilibrium of leaders and followers is a set of Differential Algebraic Equations (DAE) with an imbedded control strategies from its counterpart. In order to solve for Stackelberg equilibrium, an algorithm based on optimizing leaders’ and followers’ Hamiltonians iteratively is developed. The numerical solution of the SDG model provides the transponder’s power control system with each user’s power-control strategy at the Stackelberg equilibrium. 

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3. Deep Autoencoder-Based Z-Interference Channels With Perfect and Imperfect CSI

   https://doi.org/10.1109/TCOMM.2023.3328026  

   Zhang, Xinliang; Vaezi, Mojtaba (February 2024, IEEE Transactions on Communications)

   A deep autoencoder (DAE)-based structure for end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The proposed structure jointly optimizes the two encoder/decoder pairs and generates interference-aware constellations that dynamically adapt their shape based on interference intensity to minimize the bit error rate (BER). An in-phase/quadrature-phase (I/Q) power allocation layer is introduced in the DAE to guarantee an average power constraint and enable the architecture to generate constellations with nonuniform shapes. This brings further gain compared to standard uniform constellations such as quadrature amplitude modulation. The proposed structure is then extended to work with imperfect channel state information (CSI). The CSI imperfection due to both the estimation and quantization errors are examined. The performance of the DAE-ZIC is compared with two baseline methods, i.e., standard and rotated constellations. The proposed structure significantly enhances the performance of the ZIC both for the perfect and imperfect CSI. Simulation results show that the improvement is achieved in all interference regimes (weak, moderate, and strong) and consistently increases with the signal-to-noise ratio (SNR). For instance, more than an order of magnitude BER reduction is obtained with respect to the most competitive conventional method at weak interference when SNR>15dB and two bits per symbol are transmitted. The improvements reach about two orders of magnitude when quantization error exists, indicating that the DAE-ZIC is more robust to the interference compared to the conventional methods. 

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4. Risk-Aware Resource Management in Public Safety Networks

   https://doi.org/10.3390/s19183853  

   Vamvakas, Panagiotis; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon (September 2019, Sensors)

   null (Ed.)

   Modern Public Safety Networks (PSNs) are assisted by Unmanned Aerial Vehicles (UAVs) to provide a resilient communication paradigm during catastrophic events. In this context, we propose a distributed user-centric risk-aware resource management framework in UAV-assisted PSNs supported by both a static UAV and a mobile UAV. The mobile UAV is entitled to a larger portion of the available spectrum due to its capability and flexibility to re-position itself, and therefore establish better communication channel conditions to the users, compared to the static UAV. However, the potential over-exploitation of the mobile UAV-based communication by the users may lead to the mobile UAV’s failure to serve the users due to the increased levels of interference, consequently introducing risk in the user decisions. To capture this uncertainty, we follow the principles of Prospect Theory and design a user’s prospect-theoretic utility function that reflects user’s risk-aware behavior regarding its transmission power investment to the static and/or mobile UAV-based communication option. A non-cooperative game among the users is formulated, where each user determines its power investment strategy to the two available communication choices in order to maximize its expected prospect-theoretic utility. The existence and uniqueness of a Pure Nash Equilibrium (PNE) is proven and the convergence of the users’ strategies to it is shown. An iterative distributed and low-complexity algorithm is introduced to determine the PNE. The performance of the proposed user-centric risk-aware resource management framework in terms of users’ achievable data rate and spectrum utilization, is achieved via modeling and simulation. Furthermore, its superiority and benefits are demonstrated, by comparing its performance against other existing approaches with regards to UAV selection and spectrum utilization. 

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5. A new nonsmooth optimal control framework for wind turbine power systems

   https://doi.org/10.1016/j.jfranklin.2024.107498  

   Abdelfattah, Hesham; Eisa, Sameh A; Stechlinski, Peter (January 2025, Journal of the Franklin Institute)

   Optimal control theory extending from the calculus of variations has not been used to study the wind turbine power system (WTPS) control problem, which aims at achieving two targets: (i) maximizing power generation in lower wind speed conditions; and (ii) maintaining the output power at the rated level in high wind speed conditions. A lack of an optimal control framework for the WTPS (i.e., no access to actual optimal control trajectories) reduces optimal control design potential and prevents competing control methods of WTPSs to have a reference control solution for comparison. In fact, the WTPS control literature often relies on reduced and linearized models of WTPSs, and avoids the nonsmoothness present in the system during transitions between different conditions of operation. In this paper, we introduce a novel optimal control framework for the WTPS control problem. We use in our formulation a recent accurate, nonlinear differential–algebraic equation (DAE) model of WTPSs, which we then generalize over all wind speed ranges using nonsmooth functions. We also use developments in nonsmooth optimal control theory to take into account nonsmoothness present in the system. We implement this new WTPS optimal control approach to solve the problem numerically, including (i) different wind speed profiles for testing the system response; (ii) real-world wind data; and (iii) a comparison with smoothing and naive approaches. Results show the effectiveness of the proposed approach. 

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