This paper presents a novel two-stage method for constructing channel knowledge maps (CKMs) specifically for A2G (Aerial-to-Ground) channels in the presence of non-cooperative interfering nodes (INs). We first estimate the interfering signal strength (ISS) at sampling locations based on total received signal strength measurements and the desired communication signal strength (DSS) map constructed with environmental topology. Next, an ISS map construction network (IMNet) is proposed, where a negative value correction module is included to enable precise reconstruction. Subsequently, we further execute signal-to-interference-plus-noise ratio map construction and IN localization. Simulation results demonstrate lower construction error of the proposed IMNet compared to baselines in the presence of interference.

Channel knowledge map (CKM) has become a potential technique to enhance communication performance by exploiting the priori actual radio propagation information, especially in communication between unmanned aerial vehicles (UAVs) and ground base stations (GBSs). However, CKM constructed by existing methods cannot obtain differentiable expressions from locations to the channel information, rendering it unsuitable for the traditional communication design. In this work, we propose a site-specific differentiable CKM, based on which jointly designing trajectories and transmit power of UAVs. First, assuming sufficient channel samples to be collected by a GBS, the CKM is constructed for this specific site as a differentiable back propagation neural network (BPNN). For ease of CKM migration towards other GBSs in the proximity of the established site, we adopt the transfer learning mechanism to set up new CKMs that require significantly less training samples. Next, leveraging channel knowledge stored in the CKMs, we investigate the multi-UAV trajectory design and power control strategy, while the UAVs are traversing the network coverage area with designated starting and destination points. Specifically, the minimal average rate between the UAVs and their associated GBSs is maximized along the designed trajectories, which is solved by continuous convex optimization based on the differentiable CKMs. Numerical results show that the BPNN and transfer learning can effectively construct high-accuracy CKMs, while reducing the overall training cost. It is also shown that the proposed joint trajectory and power optimization based on the CKM-assisted architecture achieves improved minimal average rate compared to the alternating optimization method based on distance-dependent path-loss models and existing CKM-based methods with fixed power configurations, since both site-specific environmental information and power optimization are exploited.

Channel estimation in a fading environment can be regarded as a typical statistical estimation problem. Its optimal performance relies on the prior distribution of the channel coefficients, which are environment-specific. However, the conventional channel estimators, such as the least square (LS) and linear minimum mean square error (LMMSE) estimators, do not fully exploit the prior channel distribution law. To address this limitation, we propose to use the conditional diffusion model (DM) to achieve environment-aware channel estimation, referred to as EACE-DM. In this framework, the environment information is incorporated as the condition to guide the EACE-DM in learning the hidden features of channels from various environments. The trained EACE-DM is functionally decomposed into two components, i.e., the environment identification module and the channel estimation module. The environment identification module first uses the DM’s forward process to diffuse LS channel estimation into a noisy sample. Then it executes the DM’s reverse denoising process conditioned on candidate environments to recover the LS estimation from the noisy channel. The environment is then identified via maximum a posteriori (MAP) estimation by comparing these recovered estimations with the ground-truth LS estimation. Finally, the identified environment is utilized to guide the channel estimation module, denoising the LS estimation. Numerical simulations demonstrate that the proposed EACE-DM significantly decreases normalized mean square errors (NMSEs) of channel estimation across diverse environments while incurring a moderate increase in computational complexity compared to conventional estimators and existing DM-based approaches.

In future sixth-generation (6G) communication systems, it is foreseen that complex communication scenarios and critical performance requirements will necessitate more flexible air interface configurations. Traditional air interface adaptation will no longer be applicable to 6G due to issues such as high computational complexity, sub-optimal trade-offs among multi-objective performance metrics, outdated configurations due to fast-varying channels, etc. In this paper, the relevant user behaviors, communication environment, and system are virtualized via the digital twinning technique. Then, a knowledge graph-based multi-objective recommendation framework is proposed to configure the digital twinning air interface to adapt to channel conditions, while balancing various service requirements. First, the knowledge graph is applied to reveal complex dependencies between the air interface and the service requirements, and more importantly, to reconcile possibly contradictory performance targets. Furthermore, the air interface configuration, empowered by the digital twin technique, is able to exploit predicted prior knowledge about user behavior and the channel characteristics, thus improving the utilization efficiency of wireless resources promptly. Moreover, the digital twin technique allows the candidate air interfaces to be virtually verified and compared with little effort. Finally, two case studies are presented to demonstrate the potential of the knowledge graph-based recommendation method for the digital twinning air interface.

In this letter, we develop a near-field system model for a multi-user distributed multiple-input multiple-output (D-MIMO) system based on a non-uniform spherical wave (NUSW) channel model. The proposed model incorporates key physical characteristics, including the projected aperture of isotropic antenna elements and the radiation pattern of directional antenna elements, to accurately capture near-field effects. To maximize the average system capacity, we propose a meta-heuristic optimization algorithm that alternates between access point (AP) placement and array topology design, effectively reducing computational overhead while achieving near-optimal solutions. Simulation results demonstrate the effectiveness of the proposed approach.

The identification of channel scenarios in wireless systems plays a crucial role in channel modeling, radio fingerprint positioning, and transceiver design. Traditional methods to classify channel scenarios are based on typical statistical characteristics of channels, such as K-factor, path loss, delay spread, etc. However, statistic-based channel identification methods cannot accurately differentiate implicit features induced by dynamic scatterers, thus performing very poorly in identifying similar channel scenarios. In this paper, we propose a novel channel scenario identification method, formulating the identification task as a maximum a posteriori (MAP) estimation. Furthermore, the MAP estimation is reformulated by a maximum likelihood estimation (MLE), which is then approximated and solved by the conditional generative diffusion model. Specifically, we leverage a transformer network to capture hidden channel features in multiple latent noise spaces within the reverse process of the conditional generative diffusion model. These detailed features, which directly affect likelihood functions in MLE, enable highly accurate scenario identification. Experimental results show that the proposed method outperforms traditional methods, including convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and random forest-based classifiers, improving the identification accuracy by more than 10%.