基于导频与未知数据载荷的OFDM分布式天线系统中计算高效的被动定位联合最大似然估计器
A Computationally Efficient Joint Maximum Likelihood Estimator for Passive Localization in OFDM Distributed Antenna Systems with Pilots and Unknown Data Payloads
AI总结：
该研究针对OFDM分布式天线系统，提出无需数据解码的联合最大似然定位估计器，性能优于决策导向基线，计算复杂度低于非数据辅助方法，可高效实现被动定位。
AI中文摘要：
以通信为中心的集成感知与通信（ISAC）是第六代（6G）无线系统中极具前景的范式，可利用已部署的通信基础设施实现新型感知服务。通信信号通常包含已知的确定性导频序列和未知的随机数据载荷。对于定位与感知任务，多静态和分布式ISAC系统中主流方法仅依赖导频符号，完全忽略占每个传输帧大部分的数据载荷所携带的定位信息；而决策导向（DD）方法将数据估计视为额外导频，固有地将定位性能限制在底层通信系统的水平；文献中的非数据辅助（NDA）方法需要数据符号分布的先验知识，且计算成本随星座图大小增长。本文推导了一种联合最大似然（JML）估计器，在被动场景中联合利用导频和数据符号进行定位，无需数据解码，其中分布式感知接收机利用用户设备（UE）的正交频分复用（OFDM）通信信号作为机会信号进行定位。推导得到的最优解对于典型6G参数而言计算上不可行，因此提出两种可处理的近似方案，其定位性能优于DD基线方法，计算复杂度相当，且与星座图无关，计算需求显著低于现有NDA方法。此外，所提出的估计器具有几何解释，可为其内在定位行为提供见解。
英文摘要：
Communication-centric Integrated Sensing and Communications (ISAC) is a promising paradigm for sixth-generation (6G) wireless systems, enabling new sensing services by leveraging the already-deployed communication infrastructure. Communication signals typically comprise both known deterministic pilot sequences and unknown random data payloads. For localization and sensing tasks, the prevailing approach in multistatic and distributed ISAC systems relies exclusively on pilot symbols, entirely overlooking the positioning information carried by data payloads, which constitute the majority of each transmitted frame. Alternatively, Decision-Directed (DD) approaches treat data estimates as additional pilots, inherently limiting localization performance to that of the underlying communication system, while Non-Data-Aided (NDA) methods from the literature require prior knowledge of the data symbol distribution and incur a computational cost that grows with constellation size. In this paper, we derive a Joint Maximum Likelihood (JML) estimator that jointly exploits pilot and data symbols for localization without requiring data decoding, in a passive scenario where a distributed sensing receiver localizes a User Equipment (UE) by exploiting its Orthogonal Frequency-Division Multiplexing (OFDM) communication signal as a signal of opportunity. The optimal solution is derived and shown to be computationally intractable for typical 6G parameters. Two tractable approximations are then proposed, achieving localization performance superior to DD baselines at comparable computational complexity, while remaining constellation-agnostic and yielding substantially lower computational requirements than existing NDA approaches. Furthermore, the proposed estimators are shown to admit a geometric interpretation, providing insight into their intrinsic localization behavior.
