Integrated Communication, Localization, and Sensing in 6G D-MIMO Networks

TL;DR

This work investigates integrating sensing, localization, and communication within distributed MIMO (D-MIMO) networks for 6G, termed ISAC-D-MIMO. It analyzes deployment options, architectural choices, and functional perspectives (communication vs. sensing/localization), and highlights how a unified ISAC framework can leverage D-MIMO's distributed processing and multi-AP perspectives. A case study demonstrates significant uplink spectral efficiency gains when incorporating localization and sensing (up to ~16x at 10 dB SNR) under phase-coherent operation, while a testbed with 1-bit radio-over-fiber fronthaul showcases practical synchronization challenges and the impact of AP geometry on localization. The paper concludes that ISAC in D-MIMO offers substantial performance gains but faces key challenges in synchronization, scalability, and standardization that must be addressed for practical deployment.

Abstract

Future generations of mobile networks call for concurrent sensing and communication functionalities in the same hardware and/or spectrum. Compared to communication, sensing services often suffer from limited coverage, due to the high path loss of the reflected signal and the increased infrastructure requirements. To provide a more uniform quality of service, distributed multiple input multiple output (D-MIMO) systems deploy a large number of distributed nodes and efficiently control them, making distributed integrated sensing and communications (ISAC) possible. In this paper, we investigate ISAC in D-MIMO through the lens of different design architectures and deployments, revealing both conflicts and synergies. In addition, simulation and demonstration results reveal both opportunities and challenges towards the implementation of ISAC in D-MIMO.

Figures (4)

• Figure 1: Illustration of the ISAC functionalities in D-MIMO systems with key network components as well as different architectural options and characteristics of deployment scenarios. Acronyms: user equipment (UE), access point (AP), central unit (CU). Icons designed by Freepik.
• Figure 2: Impact of the number of AP on PEB and positioning RMSE, comparing conventional time-coherent positioning with phase-coherent D-MIMO positioning. The system operates at 28 GHz with 6 MHz of bandwidth, under pure LOS conditions.
• Figure 3: Impact of LAS on the UL SEs in a simulated phase-coherent D-MIMO system. We consider the setup from cell-free2 with $5$ UEs served by nearby AP (in total $200$) according to the dynamic cooperation clustering framework. The AP and UE are uniformly distributed in a $1\times 1$ km square area and the UE are initially served by several AP (default AP) where the links are blocked. Rician fading channel model is used with the same parameter setup as in ozdogan2019massive.
• Figure 4: A D-MIMO testbed used for ISAC demonstrations (right), the geometric configuration of the APs and the UE (upper left), and the experimental results for localization and communications (lower left). Localization RMSE and communication SNR performances are shown for different orders of deployment of APs (Order-1: $1~2~3~ 10~ 9~ 8~ 7~ 6~ 5~ 4$, Order-2: $1~7~4~10~5~2~8~3~9~6$).