From Specialist to Large Models: A Paradigm Evolution Towards Semantic-Aware MIMO

TL;DR

The ``semantic-aware MIMO'' paradigms are proposed, which leverages specialist models and large models to perceive, utilize, and fuse the inherent semantics of channels and sources for improved performance.

Abstract

The sixth generation (6G) network is expected to deploy larger multiple-input multiple-output (MIMO) arrays to support massive connectivity, which will increase overhead and latency at the physical layer. Meanwhile, emerging 6G demands such as immersive communications and environmental sensing pose challenges to traditional signal processing. To address these issues, we propose the ``semantic-aware MIMO'' paradigm, which leverages specialist models and large models to perceive, utilize, and fuse the inherent semantics of channels and sources for improved performance. Moreover, for representative MIMO physical-layer tasks, e.g., random access activity detection, channel feedback, and precoding, we design specialist models that exploit channel and source semantics for better performance. Additionally, in view of the more diversified functions of 6G MIMO, we further explore large models as a scalable solution for multi-task semantic-aware MIMO and review recent advances along with their advantages and limitations. Finally, we discuss the challenges, insights, and prospects of the evolution of specialist models and large models empowered semantic-aware MIMO paradigms.

Figures (5)

• Figure 1: System architecture of a semantic communication framework incorporating key functionalities including activity detection, CSI feedback, and downlink precoding. The communication process involves three semantic stages: (i) semantic perception, which extracts local features such as user activity and channel characteristics during activity detection and semantic CSI compression; (ii) semantic utilization, where side information is leveraged to enhance CSI reconstruction quality; and (iii) semantic fusion, involving the fusion of semantic data and downlink CSI for precoding design.
• Figure 2: (a) System diagram of semantic-aware massive random access with variable preamble length transmission; and (b) Performance of $P_e$ relative to testing preamble lengths $L_{\text{test}}$ in various UAD schemes. Notes: During simulation, we randomly generated QPSK preambles of maximum length $L_{\text{max}}=28$, with $K=128$ potential UEs and a BS equipped with 64 antennas. The activity of each UE follows a Bernoulli distribution with an active probability of 0.1, and the channels between UEs and the BS follow independent and identically distributed Rayleigh fading.
• Figure 3: (a) The SA-RCA-MUNet-based multi-user semantic-aware CSI feedback architecture; and (b) NMSE performance comparison of different CSI feedback schemes as a function of compression ratiozhw-wcl. Notes: For performance evaluation, simulations used a dataset generated by Quadriga under the 3GPP TR 38.901 UMi scenario. The BS has a 32-antenna linear array, while the UE has a single antenna. The downlink channel operates at 6.7 GHz with 30 kHz subcarrier spacing across 1024 subcarriers. After angle-delay transformation, only the first $N_c=32$ delay-domain rows, where multipath energy concentrates, are fed back. The feedback uses $k$ subcarriers, yielding a compression ratio $r = k / (32 \times 32) = k/1024$.
• Figure 4: (a) Source-channel semantic-aware precoding for multi-user image transmission network; and (b) LPIPS vs. SNR for different transmission schemes. Notes: It is assumed that a BS equipped with 64 antennas simultaneously transmits different image data to 4 UEs over the broadband multipath channel. Due to space limitations, we include only the key results here, while more complete analyses and full experimental settings can be found in WMH.
• Figure 5: A comparison of various LM applications in communication physical-layer tasks.