Intelligent Reflecting Surface (IRS) can offer unprecedented channel capacity gains since it can reconfigure the signal propagation environment. We decide to maximize the channel capacity by jointly optimizing the tra...Intelligent Reflecting Surface (IRS) can offer unprecedented channel capacity gains since it can reconfigure the signal propagation environment. We decide to maximize the channel capacity by jointly optimizing the transmit-power-constrained precoding matrix at the base station and the unit-modulus-constrained phase shift vector at the IRS in IRS-assisted multi-user downlink communication. We first convert the resulting non-convex problem into an equivalent problem, then use the alternate optimization algorithm. While fixing the phase shift vector, we can obtain the optimal precoding matrix directly by adopting standard optimization packages. While fixing the precoding matrix, we propose the Riemannian Trust-Region (RTR) algorithm to solve this optimization problem. And the key of the RTR algorithm is the solution of the trust-region sub-problem. We first adopt the accurate solution based on Newton's (ASNT) method to solve this sub-problem, which can obtain the global solution but cannot guarantee that the solution is optimal since the initial iteration point is difficult to choose. Then, we propose the Improved-Polyline (IPL) method, which can avoid the difficulty of the ASNT method and improve convergence speed and calculation efficiency. The numerical results show that the RTR algorithm has more significant performance gains and faster convergence speed compared with the existing approaches.展开更多
Information-theoretic principles provide a rigorous foundation for adaptive radar waveform design in contested and dynamically varying environments.This paper addresses the joint optimization of constant modulus wavef...Information-theoretic principles provide a rigorous foundation for adaptive radar waveform design in contested and dynamically varying environments.This paper addresses the joint optimization of constant modulus waveforms to enhance both target detection and parameter estimation concurrently.A unified design framework is developed by maximizing a mutual information upper bound(MIUB),which intrinsically reconciles the tradeoff between detection sensitivity and estimation accuracy without heuristic weighting.Realistic,potentially non-Gaussian statistics of target and clutter returns are modeled using Gaussian mixture distributions(GMDs),enabling tractable closed-form approximations of the MIUB’s Kullback–Leibler divergence and mutual information components.To tackle the ensuing non-convex optimization,a tailored metaheuristic phase-coded dream optimization algorithm(PC-DOA)is proposed,incorporating hybrid initialization and adaptive exploration–exploitation mechanisms for efficient phase-space search.Numerical results substantiate the proposed approach’s superiority in achieving favorable detection estimation trade-offs over existing benchmarks.展开更多
基金supported by the General Program of Natural Science Foudation of Chongqing Province of China(cstc2021jcyj-msxmX0454)
文摘Intelligent Reflecting Surface (IRS) can offer unprecedented channel capacity gains since it can reconfigure the signal propagation environment. We decide to maximize the channel capacity by jointly optimizing the transmit-power-constrained precoding matrix at the base station and the unit-modulus-constrained phase shift vector at the IRS in IRS-assisted multi-user downlink communication. We first convert the resulting non-convex problem into an equivalent problem, then use the alternate optimization algorithm. While fixing the phase shift vector, we can obtain the optimal precoding matrix directly by adopting standard optimization packages. While fixing the precoding matrix, we propose the Riemannian Trust-Region (RTR) algorithm to solve this optimization problem. And the key of the RTR algorithm is the solution of the trust-region sub-problem. We first adopt the accurate solution based on Newton's (ASNT) method to solve this sub-problem, which can obtain the global solution but cannot guarantee that the solution is optimal since the initial iteration point is difficult to choose. Then, we propose the Improved-Polyline (IPL) method, which can avoid the difficulty of the ASNT method and improve convergence speed and calculation efficiency. The numerical results show that the RTR algorithm has more significant performance gains and faster convergence speed compared with the existing approaches.
基金Project supported by the National Natural Science Foundation of China(No.61871384)the Science Fund for Distinguished Young Scholars of Hunan Province(No.2024JJ2066)the Science and Technology Innovation Program of Hunan Province(No.2022RC1092)。
文摘Information-theoretic principles provide a rigorous foundation for adaptive radar waveform design in contested and dynamically varying environments.This paper addresses the joint optimization of constant modulus waveforms to enhance both target detection and parameter estimation concurrently.A unified design framework is developed by maximizing a mutual information upper bound(MIUB),which intrinsically reconciles the tradeoff between detection sensitivity and estimation accuracy without heuristic weighting.Realistic,potentially non-Gaussian statistics of target and clutter returns are modeled using Gaussian mixture distributions(GMDs),enabling tractable closed-form approximations of the MIUB’s Kullback–Leibler divergence and mutual information components.To tackle the ensuing non-convex optimization,a tailored metaheuristic phase-coded dream optimization algorithm(PC-DOA)is proposed,incorporating hybrid initialization and adaptive exploration–exploitation mechanisms for efficient phase-space search.Numerical results substantiate the proposed approach’s superiority in achieving favorable detection estimation trade-offs over existing benchmarks.
