joint user grouping and power allocation

This work addresses the inherent unfairness in uplink NOMA-ISAC systems, where conventional resource allocation strategies favor strong users at the expense of weak users, resulting in persistently low throughput and poor fairness for the latter. To overcome this limitation, the authors propose a novel Proportional Fairness-based Joint User Grouping and Power Allocation (PF-JUGPA) method, which uniquely incorporates users’ historical service rates into both scheduling and resource allocation. By jointly optimizing user grouping and power allocation under a proportional fairness criterion, the proposed approach achieves an effective trade-off between system throughput and user fairness while preserving sensing performance. Experimental results demonstrate that PF-JUGPA significantly improves the Jain fairness index and the average rate of weak users, with only a marginal reduction in total system throughput.

This paper addresses the lack of a unified analytical framework for characterizing utility regions—specifically SINR and achievable rate regions—in wireless networks, particularly under emerging architectures such as cell-free and ultra-massive MIMO. Focusing on their distinct interference characteristics, we derive the first sufficient theoretical condition guaranteeing convexity of the utility region. Under this condition, the weighted sum-rate maximization problem is inherently convex, thereby enabling rigorous rate-based modeling without relying on the conventional “favorable propagation” assumption. By integrating convex optimization, utility boundary analysis, and beamforming modeling, we establish a cross-architecture unified analytical framework. Our results show that time-sharing cannot simultaneously improve all users’ performance along the weak Pareto boundary. Moreover, the proposed condition facilitates the design of efficient optimal solvers, bridging theoretical rigor with practical implementation. (149 words)

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 addresses the trade-off between communication and sensing performance in multi-user MIMO joint communication and sensing (JCAS) systems. It proposes a joint beamforming design that balances mutual information (for communication) and Fisher information (for sensing) via multi-objective optimization, under a scenario where a base station simultaneously serves multiple users and senses a single target. The study establishes the Pareto boundary of this system for the first time, demonstrating the superiority of joint design over separate optimizations, and includes an analytical characterization for the single-user case under equivalent isotropically radiated power (EIRP) constraints. Leveraging uplink–downlink duality, the solution is efficiently obtained through a combination of Lagrangian optimization, block coordinate ascent, line search, and projected gradient descent. Numerical results validate the optimality of the proposed scheme and systematically reveal the impacts of antenna count, number of users, and EIRP limitations on the Pareto boundary.

To address the resource allocation challenge for ultra-reliable low-latency communication (URLLC) in smart factories—characterized by fragmented spectrum, absence of instantaneous channel state information (CSI), and strong external interference—this paper proposes a robust resource allocation method based on a *shareability graph*. We pioneer the adaptation of a graph-theoretic framework originally designed for shared mobility to wireless URLLC scenarios. Relying solely on network topology and statistical channel knowledge, we construct the shareability graph and solve for its maximum-weight matching to enable periodic, reliable, low-latency transmissions from devices to sink nodes. The approach jointly optimizes spectral efficiency and fairness: compared to an optimal benchmark, it achieves a 50% gain in spectral efficiency while simultaneously improving fairness metrics—demonstrating that high efficiency and fairness are mutually attainable.

This study addresses the challenges of high computational complexity and fairness trade-offs in MU-MIMO scheduling by proposing a User Satisfaction-based Scheduling Algorithm (US-SA). The method transforms high-dimensional combinatorial optimization into efficient sub-problems through the construction of low-dimensional subgrouping matrices and a satisfied-user elimination mechanism. Experimental results demonstrate that US-SA achieves performance comparable to optimal exhaustive search while significantly reducing computational overhead. Furthermore, it outperforms existing mainstream schemes in throughput, spectral efficiency, and fairness, effectively balancing system performance with user experience.

本文提出了一种基于干扰驱动的聚类优化框架，用于大规模FM频谱协调，通过识别主要干扰源并利用GPU加速计算来减少优化复杂性和运行时间。

This work addresses energy efficiency optimization in user-centric cell-free massive MIMO networks by proposing a unified framework that jointly performs access point clustering and power allocation, integrating graph structure search with fractional programming techniques. The core contribution is the development of a Graph-Based Steepest Ascent (GBSA) algorithm, which efficiently solves the original mixed-integer fractional programming problem with linear per-iteration complexity while achieving near-global-optimal energy efficiency. Experimental results demonstrate that GBSA significantly outperforms existing methods in terms of energy efficiency, closely approaching the exhaustive-search optimum, and exhibits strong scalability across network sizes.

This work addresses the coexistence challenge among heterogeneous services—including cellular communications, RF sensing, radio navigation, and radar localization—in the sub-6 GHz licensed shared access band under high congestion. It proposes the first unified, centrally coordinated framework enabling dynamic sharing of a common physical resource block pool across all four service types. The design jointly optimizes resource allocation by maximizing a weighted sum cellular rate subject to stringent QoS constraints on duty cycle, orthogonality, sensing signal-to-noise ratio, and Cramér–Rao lower bound for localization accuracy. The solution integrates mixed-integer nonlinear programming, alternating optimization, successive convex approximation, and a low-complexity QoS-aware greedy algorithm. Validated via ray-tracing simulations on the BostonTwin urban digital twin platform, the architecture significantly enhances spectral efficiency and cellular throughput while satisfying diverse QoS requirements, demonstrating the feasibility of spectrum-efficient reuse in civil-military integrated scenarios.

研究通过使用大规模天线阵列和不同级别的合作来解决频谱资源稀缺问题，相比传统方法，该方法在中高频段共享频谱可显著提高用户速率。