Sensing-Enabled MIMO Noise Communication via Joint Localization and Bit Detection

Wireless systems with integrated communication and sensing capabilities have recently gained significant attention. Whereas most existing approaches rely on structured communication signals, radar waveforms, or pilot-based sensing, noise communication systems employ intentionally noise-like signals that provide a low probability of interception and robust resilience to interference. Nevertheless, sensing and localization for such systems remain largely unexplored. This paper presents a joint localization and communication framework for multiple-input multiple-output (MIMO) noise communication systems, in which multiple transmitters with unknown locations communicate with spatially distributed receivers via on–off keying (OOK) noise signaling. By exploiting received signal strength (RSS) measurements, the proposed framework jointly estimates transmitter locations and detects the transmitted bits. The resulting problem is formulated as a nonlinear least-squares (NLS) optimization task that jointly involves transmitter positions and symbol-dependent transmit powers. A full joint Levenberg–Marquardt (NLS–LM) estimator is developed to jointly recover all unknown parameters. To reduce computational complexity, an alternating least-squares and Levenberg–Marquardt (LS–LM) algorithm is also proposed, alternating between power estimation and localization refinement. In addition, convergence analyses are provided for both algorithms, establishing monotonic objective reduction and convergence to stationary solutions under standard assumptions. Simulation results demonstrate the effectiveness of the proposed methods and highlight the tradeoff between estimation accuracy and computational complexity.