CSI-tuples-based 3D Channel Fingerprints Construction Assisted by MultiModal Learning

Low-altitude communications can promote the integration of aerial and terrestrial wireless resources, expand network coverage, and enhance transmission quality, thereby empowering the development of sixth-generation (6G) mobile communications. As an enabler for low-altitude transmission, 3D channel fingerprints (3D-CF), also referred to as the 3D radio map or 3D channel knowledge map, are expected to enhance the understanding of communication environments and assist in the acquisition of channel state information (CSI), thereby avoiding repeated estimations and reducing computational complexity. In this paper, we propose a modularized multimodal framework to construct 3D-CF. Specifically, we first establish the 3D-CF model as a collection of CSI-tuples based on Rician fading channels, with each tuple comprising the low-altitude vehicle's (LAV) positions and its corresponding statistical CSI. In consideration of the heterogeneous structures of different prior data, we formulate the 3D-CF construction problem as a multimodal regression task, where the target channel information in the CSI-tuple can be estimated directly by its corresponding LAV positions, together with communication measurements and geographic environment maps. Then, a high-efficiency multimodal framework is proposed accordingly, which includes a correlation-based multimodal fusion (Corr-MMF) module, a multimodal representation (MMR) module, and a CSI regression (CSI-R) module. Numerical results show that our proposed framework can efficiently construct 3D-CF and achieve at least 27.5% higher accuracy than the state-of-the-art algorithms under different communication scenarios, demonstrating its competitive performance and excellent generalization ability. We also analyze the computational complexity and illustrate its superiority in terms of the inference time.

Adaptive Importance Sampling and Quasi-Monte Carlo Methods for 6G URLLC Systems

In this paper, we propose an efficient simulation method based on adaptive importance sampling, which can automatically find the optimal proposal within the Gaussian family based on previous samples, to evaluate the probability of bit error rate (BER) or word error rate (WER). These two measures, which involve high-dimensional black-box integration and rare-event sampling, can characterize the performance of coded modulation. We further integrate the quasi-Monte Carlo method within our framework to improve the convergence speed. The proposed importance sampling algorithm is demonstrated to have much higher efficiency than the standard Monte Carlo method in the AWGN scenario.

A High-Level Comparison of Recent Technologies for Massive MIMO Architectures

Since the introduction of massive MIMO (mMIMO), the design of a transceiver with feasible complexity has been a challenging problem. Initially, it was believed that the main issue in this respect is the overall RF-cost. However, as mMIMO is becoming more and more a key technology for future wireless networks, it is realized, that the RF-cost is only one of many implementational challenges and design trade-offs. In this paper, we present, analyze and compare various novel mMIMO architectures, considering recent emerging technologies such as intelligent surface-assisted and Rotman lens based architectures. These are compared to the conventional fully digital (FD) and hybrid analog-digital beamforming (HADB) approaches. To enable a fair comparison, we account for various hardware imperfections and losses and utilize a novel, universal algorithm for signal precoding. Based on our thorough investigations, we draw a generic efficiency to quality trade-off for various mMIMO architectures. We find that in a typical cellular communication setting the reflect/transmit array based architectures sketch the best overall trade-off. Further, we show that in a qualitative ranking the power efficiency of the considered architectures is independent of the frequency range.