power allocation

This study addresses the challenge of welfare maximization under budget constraints in electricity markets, where users’ optimization problems are typically non-convex and difficult to solve. The authors propose an explicit piecewise-modified utility function constructed by splicing the original utility with a logarithmic function, thereby transforming the problem into a convex optimization formulation under tight budget constraints. Leveraging this reformulation, they establish the existence and uniqueness of a competitive equilibrium and demonstrate its equivalence to the solution of the modified convex welfare maximization problem. A dual ascent algorithm is employed to compute the equilibrium, and its convergence—along with the validity of the resulting equilibrium—is corroborated through theoretical analysis and numerical experiments using quadratic and square-root utility functions.

This paper studies the student-school assignment problem under capacity constraints and group-level fairness requirements, jointly optimizing individual utilities (e.g., preference rankings), school enrollment caps, and inter-group fairness—such as across ethnicity or geography—formulated either via concave objective functions or explicit group-wise constraints, and supporting arbitrary covering constraints to capture multi-criteria and ordinal optimization needs. We propose, for the first time, a unified algorithmic framework that integrates convex programming modeling with systematic rounding techniques, yielding tunable randomized or deterministic algorithms. These run in polynomial time and provide controlled trade-offs among utility loss, capacity violations, and fairness deviations. Theoretically, our approach achieves provable approximation guarantees and naturally generalizes to covering constraints and ranking-aware settings. It exhibits strong scalability and practical deployability.

This work addresses energy efficiency and latency optimization in integrated sensing and communication (ISAC) systems under imperfect information. The authors jointly optimize time-slot allocation, beamforming adaptation, functionality selection, and user–target pairing to minimize energy consumption while prioritizing time savings, accounting for uncertainties arising from target dynamics, quantization errors, feedback delays, and hardware constraints. The problem is innovatively formulated as a semi-infinite nonconvex mixed-integer nonlinear program. By exploiting hidden convexity, the authors develop a structure-aware exact reformulation that equivalently transforms the problem into a globally solvable mixed-integer semidefinite program (MISDP). Simulations demonstrate that the proposed approach achieves up to 88% resource savings compared to baseline schemes and reveals strong coupling among the various resource management components.

To address the real-time wireless resource allocation (RA) challenge under massive edge device access in 6G networks, this work proposes a feasibility-preserving learning-to-optimize (L2O) framework. Conventional optimization methods struggle to simultaneously satisfy computational timeliness and constraint feasibility; our framework bridges this gap by integrating explicit constraint modeling with end-to-end neural network training, guaranteeing that all outputs strictly comply with physical and protocol constraints. We systematically design feasibility-enforcing mechanisms and empirically evaluate the framework on canonical RA tasks—including weighted sum-rate maximization—under realistic 6G edge scenarios. Results demonstrate that the proposed method reduces inference latency by one to two orders of magnitude compared to traditional solvers while achieving near-optimal performance, with an average optimality gap of less than 3%. This work establishes a verifiable, deployable paradigm for intelligent, real-time, and trustworthy 6G wireless resource management.

This paper addresses the non-convex mixed-integer nonlinear programming (MINLP) problem of jointly optimizing user scheduling, target association, and beamforming in integrated sensing and communication (ISAC) systems. To tackle this challenge, we propose a globally optimal joint design framework. Our key contributions are threefold: (i) we formulate an exact mixed-integer linear programming (MILP) reformulation of the original problem, enabling globally optimal solutions; (ii) we adopt low-resolution, constant-modulus, finite-phase-shift beamforming to ensure hardware feasibility without compromising performance; and (iii) we replace conventional sequential heuristic approaches with end-to-end joint optimization of sensing and communication resources. Simulation results demonstrate that the proposed method significantly outperforms staged designs in localization accuracy, communication rate, and robustness—validating the fundamental advantages of joint optimization for multi-objective trade-offs and cross-scenario generalization.

This work investigates a transmissive reconfigurable intelligent surface (RIS)-aided downlink multi-user MIMO system, aiming to jointly optimize RIS transmissive coefficients, transmit power allocation, and user-side receive beamforming to maximize the weighted sum rate. The problem is highly challenging due to its non-convex objective, strong coupling among variables, and the constant-modulus constraint on RIS coefficients. To address this, we propose a novel alternating optimization framework that decomposes the original problem into three tractable subproblems, solved efficiently via convex approximation, difference-of-convex programming (DCP), and closed-form solutions, respectively. Convergence is theoretically guaranteed. Simulation results demonstrate rapid convergence, substantial improvements in spectral and energy efficiency—particularly under low-power constraints—and significant weighted sum-rate gains over benchmark schemes. These findings validate the practical potential of transmissive RISs for 6G large-scale MIMO systems.

This work addresses the non-convex, multi-objective optimization challenge in wireless communication systems that arises from balancing user fairness against aggregate throughput, a problem whose complexity escalates with network scale. The authors propose an unsupervised learning approach based on the Wireless Transformer (WiT), which integrates fairness constraints into an end-to-end deep learning framework via Lagrange multipliers. By coupling this architecture with a dual ascent algorithm, the method automatically tunes the multipliers to maximize throughput under controllable fairness guarantees. Notably, the approach operates without labeled data and efficiently approximates the Pareto frontier, enabling flexible trade-offs between fairness and system performance in multi-user scenarios. Experimental results demonstrate significant improvements over existing state-of-the-art solutions.

This work addresses the joint wireless resource management challenge in uplink hybrid beamforming systems, where constraints on the number of radio-frequency chains and per-user power-time allocation complicate system optimization. To tackle this, the paper proposes a low-complexity heuristic algorithm that jointly optimizes, for each time slot, analog beam selection, user scheduling, power allocation, modulation and coding scheme, and digital zero-forcing beamforming. Leveraging codebook-based analog beamforming combined with zero-forcing digital processing, the proposed method achieves near-optimal performance while reducing computational complexity by two orders of magnitude and enabling scalability to large numbers of users. Experimental results demonstrate that the online algorithm closely approaches the theoretical performance upper bound and provide insights into the practical impact of key system parameters.

Resource allocation in multi-cell cellular networks faces challenges due to complex, conflicting utility functions that are only accessible via costly black-box evaluations. Method: We formulate inter-base-station power allocation under spectrum sharing as a non-cooperative game and propose PPR-UCB—a novel algorithm integrating Gaussian process regression with martingale theory to construct tight confidence upper bounds for efficient black-box utility approximation and uncertainty quantification. Contribution/Results: Compared to standard Bayesian optimization, PPR-UCB drastically reduces sample complexity, achieving stable convergence to high-quality pure Nash equilibria with only a few utility evaluations in multi-cell, multi-antenna systems. It simultaneously ensures optimization efficiency and system stability, establishing a new paradigm for black-box game-theoretic optimization.

This work addresses the challenges of slow convergence, high computational complexity, and lack of user prioritization in joint signal enhancement and suppression using reconfigurable intelligent surfaces (RIS) in multi-user wireless systems. To overcome these limitations, the authors propose a unified RIS optimization framework that incorporates adaptive gradient scaling for fast, parameter-free convergence, a low-complexity beamforming recovery method that avoids matrix decomposition, and a novel user prioritization mechanism based on RIS subarray allocation, complemented by a modular architecture supporting flexible addition or removal of components. Evaluated across three representative scenarios, the proposed scheme closely approaches theoretical performance bounds, significantly outperforms conventional semidefinite relaxation methods, and demonstrates near-optimality, scalability, and effectiveness in both cooperative and competitive multi-user environments under real-world channel conditions.