Noncoherent MIMO Communications: Theoretical Foundation, Design Approaches, and Future Challenges

TL;DR

This survey addresses the challenge of reliable wireless communication without instantaneous channel state information by analyzing three CSI-free paradigms for MIMO: Grassmannian signaling, energy-based schemes, and differential-detection-based methods. It provides a rigorous theoretical foundation, practical constellation designs for single- and multi-user settings, and a critical assessment of performance, complexity, and hardware robustness. The work also discusses practical integration with OFDM, DC components, and scalability, and it outlines emerging directions including machine learning-driven design, RIS, ISAC, and noncoherent waveform concepts. The findings emphasize that noncoherent approaches enable robust, scalable performance in high-mobility, massive, and energy-constrained scenarios, and offer a clear roadmap for future research and deployment in next-generation networks.

Abstract

Noncoherent communication is a promising paradigm for future wireless systems where acquiring accurate channel state information (CSI) is challenging or infeasible. It provides methods to bypass the need for explicit channel estimation in practical scenarios such as high-mobility networks, massive distributed antenna arrays, energy-constrained Internet-of-Things devices, and unstructured propagation environments. This survey provides a comprehensive overview of noncoherent communication strategies in multiple-input multiple-output (MIMO) systems, focusing on recent advances since the early 2000s. We classify noncoherent communication schemes into three main approaches where CSI-free signal recovery is based on subspace detection (i.e., Grassmannian signaling), differential detection, and energy detection, respectively. For each approach, we review the theoretical foundation and design methodologies. We also provide comparative insights into their suitability across different channel models and system constraints, highlighting application scenarios where noncoherent methods offer performance and scalability advantages over traditional coherent communication. Furthermore, we discuss practical considerations of noncoherent communication, including compatibility with orthogonal frequency division multiplexing (OFDM), resilience to hardware impairments, and scalability with the number of users. Finally, we provide an outlook on future challenges and research directions in designing robust and efficient noncoherent systems for next-generation wireless networks.

Figures (9)

• Figure 1: The overall layout of the survey.
• Figure 2: Best-suited noncoherent techniques for different scenarios.
• Figure 3: Illustration of the symbols and decision regions of the greedy decoder for a section (around the cell boundaries) of the Cube-Split constellation Ngo_cubesplit_journal on ${\mathbb G}(1,{\mathbb R}^3)$ for $M = 1$ antenna and spectral efficiency $\eta = R/M = 5$ bits/s/Hz. Thanks to the regular placement of the symbols, these decision regions well match the Voronoi regions, which are the optimal decision regions.
• Figure 4: Representation of Grass-Lattice constellation for $T = 2$ symbol periods, $M = 1$ antenna and spectral efficiency $\eta = R/M = 4$ bits/s/Hz from Cuevas24constellationsOnTheSphere, where the Hopf map hopfmap is used to represent the points on the Grassmann manifold.
• Figure 5: SER performance of different Grassmannian constellations and a pilot-based scheme for $T = 2$ symbol periods, $M = N = 1$ antenna and spectral efficiency $\eta = R / M \in \{2,3\}$ bits/s/Hz from Cuevas24constellationsOnTheSphere.