🤖 AI Summary
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.
📝 Abstract
Coordinated beamforming across distributed base stations (BSs) in cell-free architectures can efficiently support integrated sensing and communication (ISAC) users by improving resource sharing and reducing conflicts in the spatial domain. However, coordinating numerous BSs within the ISAC network poses risks of generating substantial interference for other networks sharing the spectrum, while also increasing operational costs from power consumption and signaling overhead. Therefore, in this paper, we propose an interference-suppressed and cost-optimized cell-free ISAC network by opportunistically and cooperatively orchestrating distributed radio resources to address competing sensing and communication (S&C) demands. Specifically, we conceive a radiation footprint control mechanism that autonomously suppresses interference across the entire signal propagation space to safeguard other networks without exchanging signaling. Then, we propose joint BS activation and beamforming coordination to dynamically activate appropriate BSs and orchestrate their spatial beams for service provisioning. Building upon this framework, we formulate a cost-efficient utility maximization problem that considers individual S&C demands and location-dependent radiation footprint constraints. Since this results in a non-convex optimization problem, we develop a monotonic optimization embedded branch-and-bound (MO-BRB) algorithm to find the optimal solution. Additionally, we apply a low-complexity iterative method to obtain near-optimal solutions. Finally, simulation results validate the effectiveness of the proposed algorithms.