The low-altitude economy (LAE) is inherently dependent on unmanned aerial vehicles (UAVs) as its core operational infrastructure, with applications spanning logistics, surveillance, and smart city development. Despite growing attention, LAE's practical deployment faces substantial obstacles, particularly in maintaining reliable UAV operations. These UAVs require secure and efficient wireless communication services delivered by base stations (BSs), yet their high mobility, combined with multipath effects, creates significant channel estimation challenges that threaten the seamless connectivity. According to the fixed airspace and planned routes characteristics of LAE scenarios, in this paper we construct the radio map to assist channel estimation using sensing information. First, a grid-based UAV channel measurement scheme is proposed to collect CSI data labeled with discrete locations and velocities. Then, a new generative adversarial network (GAN)-based model named continuous vector-conditioned GAN (CVCGAN) is developed to complete the discrete map by establishing the mapping from continuous sensing information to their channel space. After that, an integrator is designed to fuse the channel state information (CSI) provided by radio map with that estimated by pilots. Simulation results validate the superiority of the proposed method, which outperforms state-of-the-art (SOTA) approaches including ChannelNet, conditional GAN (CGAN), RadioUNet and long short-term memory (LSTM).

The next generation multiple access (NGMA) schemes are considered to support massive access for a large number of devices, which motivates us to develop a low-complexity approach for next generation systems. Since the generalized spatial modulation (SM) can be adopted to the system, a number of compressive sensing (CS) reconstruction algorithms are deployed for the detection of sparse signals, while the complexity of CS-based approaches is proportional to the number of antennas. In order to decrease the complexity, we propose a two-stage approach to detect sparse signals, where the received signals are divided into groups. Then, the activity variables of aggregated signals are decided and the sparse signal detection is carried out at the signals belonging to active groups. During the activity variable detection, the variational inference algorithm is applied to determine the activity variables. Moreover, in order to analyze the performance of activity variable detection, the$J$-divergence is proposed to measure the distance between the distributions, while the approximate expression of$J$-divergence is derived. Simulation results show that the proposed approach is able to provide good detection performance with low complexity. In addition, the$J$-divergence is confirmed to be useful as an evaluation metric to measure the detection performance.

Magnetic Induction (MI) based transmissions, where the typical applications are wireless power transfer (WPT) and MI communications, have gained substantial attention in recent years. Existing works related to MI based transmissions mainly focus on the case with single resonant frequency. However, from the perspective of practical applications, it is highly demanded to achieve multi-frequency access for MI based transmissions. Although multiple coils can be used to generate additional resonant frequencies, each coil is often with one resonant frequency. In this paper, we develop the Multi-frequency Resonating Compensation (MuReC) coil based Multiple-band, Multiple-Input, and Multiple-Output (MbMIMO) simultaneous wireless information and power transfer (SWIPT), which can generate multiple resonant frequencies with one coil. The access point (AP) is equipped with multiple MuReC coils as well as delivers power and data to single-coil receivers (RXs) with different resonant frequencies. We propose the magnetic beamforming scheme for MuReC-MbMIMO-SWIPT, which aims to minimize the transmit power as well as guarantee the network throughput, total delivered power, and individual requirements of each channel. The beamforming schemes for two special cases of MuReC-MbMIMO-SWIPT, that is, MuReC-MbMIMO-communications and MuReC-MbMIMO-WPT, are also given. The optimal beamforming scheme in closed-form for MuReC-MbMIMO-communications is derived. In addition, the parameter design scheme for MuReC coils, which can support an arbitrary number of resonant frequencies, is developed. Numerical results verify our theoretical analyses and show that MuReC-MbMIMO transmissions and the proposed beamforming schemes can fully use antenna resources and effectively support multi-frequency access.

This paper studies the optimization of pilot training signals in Massive MIMO systems with insufficient pilot length. We aim at finding the optimal pilot design and pilot length that maximize the Spectral Efficiency (SE) of the system. To achieve that, we firstly derive an approximation of average achievable capacity, which is related to channel estimation error and pilot signals. Then some analysis and discussions on finding the optimal non-orthogonal pilot design are presented in order to maximize average sum SE. Since previous research on Massive MIMO systems with pilot training mainly focus on orthogonal pilots, in this paper we give a design criterion of non-orthogonal pilots, and relate the system SE as a function of pilot length. Simulation results show the gap between simulation results and our analytical results. Meanwhile, the performance of uplink SE with orthogonal and non-orthogonal pilots is shown, where for insufficient block length, non-orthogonal pilots could lead to higher SE than the orthogonal pilots. Our analysis and results suggest that choosing non-orthogonal pilots can get a better performance than orthogonal pilots in Massive MIMO systems.