energy-aware antenna selection and activation

本文研究了多天线基站的能效问题，通过优化发射功率、带宽和天线数量，并引入三种睡眠模式，提出了一种联合优化算法以实现最大能效。

This work addresses energy efficiency (EE) maximization in mobile-antenna (MA)-assisted multi-user uplink communication systems, jointly optimizing antenna position, user power allocation, and transmission duration. It is the first to explicitly model and constrain the latency and energy overhead induced by antenna mobility within an MA framework. To solve the non-convex problem, we propose a hybrid algorithm: a one-dimensional exhaustive search for single-user cases and a fairness-aware iterative scheme for multi-user scenarios—both integrating convex optimization with accurate mobility energy/latency modeling and robustness against channel state information (CSI) mismatch. We theoretically derive a tight upper bound on EE and identify its achievability conditions. Numerical results demonstrate that the proposed design significantly outperforms both conventional fixed-antenna systems and MA schemes ignoring mobility overhead, while maintaining consistent EE gains under CSI uncertainty—providing a practical, implementable guideline for real-world MA deployment.

To address severe cross-network radiation interference, high power consumption, and excessive signaling overhead arising from multi-base-station cooperative beamforming in cell-free integrated sensing and communication (ISAC) networks, this paper proposes a radiation footprint control mechanism coupled with a dynamic base-station activation–beamforming co-optimization framework. We innovatively design a signaling-free, fully spatially autonomous radiation footprint modeling method; formulate a joint optimization model that simultaneously respects location-sensitive radiation constraints and heterogeneous sensing/communication requirements; and develop a monotonicity-preserving embedded branch-and-bound (MO-BRB) algorithm to achieve global optimality. Simulation results demonstrate over 60% reduction in interference power, a 32% decrease in total operational cost, while maintaining stringent sensing accuracy and communication QoS—thereby significantly improving energy efficiency and spectral efficiency.

This paper addresses energy efficiency (EE) optimization in movable-antenna (MA) systems by jointly optimizing antenna positioning, mobility velocity, and multi-user precoding at the base station, while explicitly modeling mechanical power consumption induced by stepper motors. To eliminate collision constraints, a novel antenna renumbering strategy is proposed. The implicit monotonicity of EE with respect to mobility velocity is analytically revealed. An integrated bi-level optimization framework is developed: the outer layer handles the fractional EE programming via the Dinkelbach method, while the inner layer employs alternating optimization (AO) to iteratively update antenna positions, velocities, and beamforming vectors. Numerical results demonstrate that the proposed algorithm significantly enhances system EE in multi-user scenarios, outperforming conventional fixed-antenna systems and state-of-the-art energy-efficiency benchmarks.

To address the challenge of spectrum- and energy-efficient coexistence between device-to-device (D2D) communications and cellular users in cellular networks, this paper proposes a hierarchical cooperative optimization framework. At the long-term epoch scale, the base station centrally determines channel allocation and communication mode selection; at the short-term slot scale, users distributively execute cognitive power control. The key contribution lies in the first-ever “centralized–distributed” two-timescale architecture, integrating cognitive radio principles, NOMA-compatible design, and game-theoretic power optimization—where the existence and closed-form solution of the optimal power strategy are rigorously proven. Simulation results demonstrate that, compared with state-of-the-art distributed schemes, the proposed framework achieves a 23% gain in system throughput, a 31% improvement in Jain’s fairness index, and significant reductions in inter-user interference and signaling overhead.

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.

This work addresses the trade-off between energy efficiency and integrated sensing and communication (ISAC) performance in satellite–UAV MIMO systems by proposing a distributed MIMO ISAC framework under a hybrid high- and low-altitude channel model that accounts for both line-of-sight (LoS) and non-LoS components. The framework jointly optimizes beamforming and power allocation, incorporating—for the first time—a constraint on sensing beampattern gain into the energy efficiency maximization problem to ensure multi-user quality-of-service requirements and multi-target radar sensing accuracy. Leveraging a uniform planar array and a probabilistic LoS channel model, the resulting non-convex problem is solved via an alternating optimization algorithm. Simulation results demonstrate that the proposed method significantly enhances energy efficiency in multi-user, multi-target scenarios while simultaneously meeting communication throughput and sensing precision requirements.

This study addresses the beam-hopping resource management challenges in low Earth orbit (LEO) satellite networks arising from highly dynamic topologies and heterogeneous traffic. To this end, it proposes a two-stage GNN-MAPPO cooperative framework operating under partial observability. Specifically, the method leverages graph neural networks to extract time-varying topological features and employs multi-agent proximal policy optimization for joint beam scheduling, while incorporating a load-balancing mechanism to enhance multi-satellite cooperation. Experimental results demonstrate that the proposed architecture effectively overcomes the challenges of environmental partial observability, yielding significant improvements in system energy efficiency, throughput, and user fairness.

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.

This work addresses the high energy consumption arising from task offloading in unmanned aerial vehicle (UAV)-enabled mobile edge computing systems by proposing a joint optimization framework that, for the first time, incorporates a movable antenna array. The framework simultaneously optimizes computational resource allocation, user transmit power, receive beamforming, and antenna placement to minimize total system energy consumption. To tackle the resulting coupled non-convex problem, an efficient solution is developed by integrating block coordinate descent, quadratic transformation, and particle swarm optimization. Simulation results demonstrate that the proposed scheme significantly outperforms existing benchmark approaches, achieving notable gains in energy efficiency.