面向感知的OFDM-ISAC系统自适应资源分配设计
Sensing-Oriented Adaptive Resource Allocation Designs for OFDM-ISAC Systems
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中文总结 AI 辅助
本文针对OFDM-ISAC系统提出面向感知的自适应资源分配策略，通过推导感知性能闭式表达式并基于Dinkelbach变换和MM算法求解非凸优化问题，在保障通信QoS的同时实现接近纯雷达方案的感知性能。
中文摘要 AI 辅助
正交频分复用-集成感知与通信（OFDM-ISAC）已成为未来无线网络的关键使能技术，利用广泛采用的OFDM波形在统一框架内无缝集成无线通信与雷达感知。本文提出了OFDM-ISAC系统的自适应资源分配策略，以在不同感知需求与通信服务质量（QoS）之间实现最优权衡。我们首先为OFDM-ISAC系统开发了一个全面的资源分配框架，推导了关键感知性能指标的闭式表达式，包括时延分辨率、多普勒分辨率、时延-多普勒峰值旁瓣电平（PSL）以及接收信噪比（SNR）。在这一理论基础上，我们提出了两种针对不同感知目标的新型资源分配算法。面向分辨率的算法旨在最大化加权时延-多普勒分辨率，同时满足PSL、感知SNR、通信总速率和发射功率的约束。面向旁瓣的算法侧重于最小化时延-多普勒PSL，同时满足分辨率、SNR和通信约束。为了高效求解由此产生的非凸优化问题，我们基于Dinkelbach变换和优化最小化（MM）开发了两种自适应资源分配算法。大量仿真验证了所提出的面向感知的自适应资源分配策略在提升分辨率和旁瓣抑制方面的有效性。值得注意的是，这些策略实现的感知性能几乎与将所有资源专用于感知的纯雷达方案相同。这些结果凸显了所提方法在优化OFDM-ISAC系统内感知与通信目标之间权衡方面的优越性能。
英文摘要
Orthogonal frequency division multiplexing - integrated sensing and communication (OFDM-ISAC) has emerged as a key enabler for future wireless networks, leveraging the widely adopted OFDM waveform to seamlessly integrate wireless communication and radar sensing within a unified framework. In this paper, we propose adaptive resource allocation strategies for OFDM-ISAC systems to achieve optimal trade-offs between diverse sensing requirements and communication quality-of-service (QoS). We first develop a comprehensive resource allocation framework for OFDM-ISAC systems, deriving closed-form expressions for key sensing performance metrics, including delay resolution, Doppler resolution, delay-Doppler peak sidelobe level (PSL), and received signal-to-noise ratio (SNR). Building on this theoretical foundation, we introduce two novel resource allocation algorithms tailored to distinct sensing objectives. The resolution-oriented algorithm aims to maximize the weighted delay-Doppler resolution while satisfying constraints on PSL, sensing SNR, communication sum-rate, and transmit power. The sidelobe-oriented algorithm focuses on minimizing delay-Doppler PSL while satisfying resolution, SNR, and communication constraints. To efficiently solve the resulting non-convex optimization problems, we develop two adaptive resource allocation algorithms based on Dinkelbach's transform and majorization-minimization (MM). Extensive simulations validate the effectiveness of the proposed sensing-oriented adaptive resource allocation strategies in enhancing resolution and sidelobe suppression. Remarkably, these strategies achieve sensing performance nearly identical to that of a radar-only scheme, which dedicates all resources to sensing. These results highlight the superior performance of the proposed methods in optimizing the trade-off between sensing and communication objectives within OFDM-ISAC systems.
