Drones are expected to be promising aerial platforms in air-to-ground (A2G) integrated communication networks, where the A2G propagation channel is fundamental for reliable communication links. This paper proposes a hybrid parameter generation framework combining the deterministic and statistical methods for a cluster-based A2G channel model. In this framework, the map-based deterministic method is used to generate delay and angle parameters, which can achieve great scenario consistency. However, it is difficult for users to provide precise material information of scatterers, which would cause deviation of power parameters. To tackle this issue, a measurement-driven power generation method is proposed. Firstly, a bandwidth-dependent clustering method is developed to group the rays into clusters. Then, the cluster power is generated by measurement-driven statistical models with a power-decomposition idea. It decomposes the power parameter into several parts that are less dependent on the scenario. Moreover, it can avoid massive measurement campaigns and is more robust when applied in unmeasured scenarios. Finally, a new channel measurement campaign in a street canyon scenario is performed for validations. The proposed method is also compared with a ray-tracing (RT) method and the 3rd Generation Partnership Project (3GPP) channel model. It is shown that the proposed framework and power generation method are great alternatives for accurate and robust modeling requirements under specific A2G communication scenarios.

Trust region optimization-based received signal strength indicator (RSSI) interference source localization methods have been widely used in low-altitude research. However, these methods often converge to local optima in complex environments, degrading the positioning performance. This paper presents a novel unmanned aerial vehicle (UAV)-aided progressive interference source localization method based on improved trust region optimization. By combining the Levenberg-Marquardt (LM) algorithm with particle swarm optimization (PSO), our proposed method can effectively enhance the success rate of localization. We also propose a confidence quantification approach based on the UAV-to-ground channel model. This approach considers the surrounding environmental information of the sampling points and dynamically adjusts the weight of the sampling data during the data fusion. As a result, the overall positioning accuracy can be significantly improved. Experimental results demonstrate the proposed method can achieve high-precision interference source localization in noisy and interference-prone environments.

With the deep integration of the Internet of Vehicles and air-ground collaborative communication networks, urban spectrum management faces multidimensional challenges, including an increasingly prominent supply-demand imbalance of spectrum resources. Conventional spectrum coordination methods suffer from low utilization efficiency in the presence of heterogeneous network environments and dynamic service demands. To address this issue, this paper proposes a dynamic spectrum prediction method based on temporal knowledge graphs. By integrating multidimensional knowledge including electromagnetic spectrum data, equipment parameter features, and environmental data, we construct a knowledge graph for communication spectrum coordination with fusion of static and dynamic knowledge (SDKG). By employing a knowledge graph embedding (KGE) model based on the recurrent evolution network via graph convolution network (RE-GCN) combined with graph neural networks (GCNs) and a long short-term memory (LSTM) reasoning algorithm, a GCN-LSTM with RE-GCN dynamic spectrum prediction algorithm is proposed. Simulation results demonstrate that the GCN-LSTM with RE-GCN algorithm can effectively enhance frequency prediction accuracy.

Specific emitter identification (SEI), known as radio frequency fingerprint (RFF) identification, is one of the key techniques to provide effective protection for the low-altitude security. However, most existing SEI methods cannot achieve satisfactory identification performance in low signal-to-noise ratio (SNR) environments. By fusing background information of the environment, this paper presents a new deep learning-based SEI method that can accurately identify emitters in the environments with severe noises. We first construct a dual convolutional neural network (DCNN) and a U-shaped Convolutional Network (UNet) to extract the RFFs and background information features, respectively. Then, a background-fingerprint attention fusion network (BFAFN) is designed to fuse the background information with RFF features. Using this network, we can obtain detailed emitters information through the fused signals, improving the identification accuracy. Experimental results show that our proposed SEI method outperforms other methods in performance on both open-source and collected unmanned aerial vehicles (UAVs) datasets, with an improvement in identification accuracy of 2% to 5%.

Constructing three-dimensional (3D) radio environment maps (REMs) has emerged as a promising solution to visualize the spectrum information over the geographical map. In this paper, we propose a novel online path planning method for unmanned aerial vehicle (UAV)-based 3D spectrum mapping in unknown environments. The UAV can effectively collect spectrum data along dynamically planned paths while adhering to budget constraints. We formulate the path planning problem by a surrogate objective that maximizes the information gain along the sampling path. Specifically, a Gaussian process (GP) is adopted to estimate the spatial distribution of received signal strength (RSS) based on observations. A goal location decision algorithm based on the negative integrated posterior variance (NIPV) criterion is developed, which identifies high-value locations by maximizing the reduction in uncertainty. Besides, an uncertainty-aware local path planner is introduced to optimize sampling paths during flight. Simulation results demonstrate that it achieves at least a 66.49% improvement in REM construction accuracy and 42.74% reduction in mapping uncertainty compared to traditional methods.

Radio environment maps (REMs) have been used to visualize the information of invisible electromagnetic spectrum. Although in the past there have been many research activities dealing with the reconstruction of static REMs, they did not consider the time variation of the dynamic spectrum operational environment. In this article, we present a novel time-variant REM reconstruction methodology based on sparsely distributed sensors which jointly considers sensor layout optimization, propagation model improvement, and missing spectrum data recovery. First, a low complexity and computationally efficient method is proposed to improve the sampling efficiency. The proposed method jointly employs the gradient descent method and an upgraded greedy matching algorithm to optimize the sensor positions even when large-scale scenarios are considered. Then, by using the sampled spectrum data obtained from these sensors, the accuracy of commonly employed propagation models is improved and subsequently used to construct a channel dictionary for such time-varying environments. By exploring the heterogeneity of dynamic spectrum operational environments, an improved optimal reconstruction method is designed to recover the spectrum data using their spatial-temporal correlation. By considering a typical university campus environment as a case study, simulation and measurement data are obtained to reconstruct the time-variant REM. Through the simulation data, the reconstruction performance results are compared with those obtained from other state-of-the-art methods showing that the proposed methodology outperforms the others with respect to the sampling scheme and missing rate. Additionally, field measurement results have demonstrated that the proposed approach can effectively reconstruct time-variant REMs under dynamic scenarios.

Metaverse, which enables the combination of the virtual and the physical worlds, requires mobile networks with high-capacity and reliable connectivity. Radio maps (RMs) can offer the knowledge of the wireless environments to improve the connectivity, by charactering the spatial distribution of received signal strength (RSS) throughout physical spaces. This paper investigates a collaborative RM reconstruction scheme, where client unmanned aerial vehicles (UAVs) collect RSS samples measured by mobile users for local training, while a server UAV performs model aggregation to optimize the global model. Unfortunately, RSS samples measured in practice can be sparse, non-uniformly distributed and non-independent and identically distributed (non-iid), such that reconstructing a complete RM is intractable. Therefore, we propose a novel RM reconstruction scheme based on federated learning (FL) with generative adversarial network (GAN), where GAN is exploited to generate a RM with sparsely and non-uniformly distributed RSS data. In order to tackle with non-iid RSS data, the FL is integrated with an adaptive client UAV selection strategy with model similarity evaluation, as well as a model weight assignment method with earth mover’s distance evaluation for model aggregation. Simulation results reveal that benefiting from the aforementioned design, the proposed scheme can significantly enhance the reconstruction accuracy and convergence speed compared to the conventional algorithms.

With the rapid advancement of 5 G communication and the Internet of Things (IoT), unmanned aerial vehicles (UAVs) has shown its potential to play a pivotal role as aerial base stations within the evolving landscape of future wireless networks and edge computing systems. However, in such UAV-assisted communication networks, the complexity of multi-agent scenarios creates challenges due to the problem of learning and decision-making in complex tasks. To address this issue, we propose a novel algorithm that optimizes UAV trajectory planning and autonomous clustering of user equipment (UE) for communication. The proposed algorithm leverages hierarchical reinforcement learning and multi-agent cognitive consistency to improve UE communication decisions in interference-prone environments. Simulation results confirm the convergence of the proposed algorithm and reveal special strength in coordinating spectrum utilization in complex air-ground environments.

Search and location of terrestrial emitters, especially those using spectrum illegally/improperly, can facilitate the utilization of spectrum resources and mitigate interference. However, the dynamic directional RF beams make search and geolocation of terrestrial terminals (such as the terminals for low-earth orbit satellite internet) intractable. This paper investigates the issue of searching and geolocating a radio frequency (RF) emitter with an unmanned aerial vehicle (UAV). To handle this issue, a novel UAV autonomous trajectory planning scheme is proposed by employing reinforcement learning (RL) and target probability map (TPM). Based on the detection probability and false alarm probability, a TPM is constructed to assist UAV to perform effective trajectory planning in the absence of knowledge about the uplink beam direction and the potential geographical location of the RF emitter. By employing the TPM information as a part of the state information, a deep-Q-network-based trajectory planing algorithm is proposed for searching. Simulation results confirm the advantages of the proposed algorithm over various baselines in time consumption and geolocation accuracy.

The development of Industrial Internet of Things (IIoT) has completely changed the traditional manufacturing industry. The data exchange between controllers and actuators needs to achieve extremely low delay in IIoT. Due to the limited communication resources, it is necessary to reasonably schedule data flow to reduce delay. Although the studies of data flow scheduling exist in IIoT, they have not considered the impact of time-varying environmental factors and most of them adopted centralized scheduling schemes, which increase computation and communication cost rapidly in large-scale network scenarios. In this article, the consensus-based distributed optimal transport (OT) algorithm is proposed to optimize data flow scheduling for IIoT networks. Specifically, a data flow scheduling optimization mechanism based on time-varying environmental factors is proposed and an online distributed data flow scheduling optimization algorithm is designed. Compared with the random data flow scheduling algorithm, numerical results show that the proposed algorithm can maximally reduce the average delay by 87%, increase the transmission rate and the spectral efficiency by 157% and 98%, respectively.

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division multiple access. To improve the robustness against CSI imperfection, this paper proposes a deep learning (DL)-based rate-splitting multiple access (RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically, we first propose a hybrid data-model driven DL-based RSMA precoding scheme, including the passive precoding at the RIS as well as the analog active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme, and further design a model-driven deep unfolding active precoding network (DFAPN) by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead. The proposed DL-based RSMA scheme for RIS-aided Tera-Hertz multi-user MIMO systems can exploit the advantages of RSMA and DL to improve the robustness against CSI imperfection, thus achieving higher spectral efficiency with lower signaling overhead.

Unmanned aerial vehicles (UAVs) are expected to improve the quality of services for the fifth-generation (5G) and beyond networks. Nevertheless, in the context of multi-access edge computing (MEC), a key pillar for meeting 5G key performance indicators, state-of-the-art design suffers from difficulty in matching time/spatial-varying communication/computation demands with distributed resources in highly dynamic air-ground integrated networks. To handle this issue, this paper proposes a joint online trajectory planning and offloading scheduling scheme based on reinforcement learning (RL), such that a UAV and base stations in a multi-cell network can dynamically and collaboratively offer edge computing services. We formulate the decision-making on trajectory planning/offloading scheduling as mutually embedded Markov decision processes, so as to avoid exponentially increasing joint state/action spaces and non-cooperative decision-making. In order to learn the policies for decision-making, novel RL algorithms are proposed based on deep Q-network and the kernel method, respectively. It is shown that the learned policy is able to make the joint trajectory planning and offloading scheduling adaptive to dynamic computation demands. Benefiting from this, the air-ground collaborative MEC can significantly outperform the terrestrial-only MEC in terms of the average backlog of newly produced computational task bits.

Cognitive radio networks (CRNs) are expected to be promising techniques for improving the spectrum efficiency of wireless network utility in the squeezed sub-6-GHz frequency bands. Nevertheless, frequency allocation and transmission scheduling for secondary users (SUs) in CRNs suffer from no prior knowledge of other SUs’ network behaviors or the distribution of the amount of data generated at each SU. As a countermeasure, this article develops a protocol for the joint channel selection and transmission scheduling such that SUs with heterogeneous data transmission demands could be served with limited spectrum resources. Then, we formulate the dynamic optimization of the protocol as mutually embedded Markov decision processes (MDPs). To address the intractable MDPs,$Q$-learning-based channel selection and transmission scheduling based on reinforcement learning with basis function approximation are, respectively, proposed. It is shown that compared with various baselines, the proposed channel selection algorithm enables each SU to select the best frequency-domain channel that does not interfere with other SUs. In particular, the proposed transmission scheduling algorithm outperforms algorithms based on off-the-shelf approaches, such as$Q$-learning and Lyapunov optimization, in terms of both energy efficiency and long-term accumulative amount of bits at each SU.

Wireless power transfer and backscatter communications have emerged as promising solutions for energizing and communicating with power limited devices. Despite some progress in wirelessly powered backscatter communications, the focus has been on backscatter and energy harvesters (EHs). Recently, significant progress has been made on the design of the transmit multisine waveform, adaptive to the channel state information at the transmitter (CSIT), in a point-to-point backscatter system. In this paper, we leverage the work and study the design of the transmit multisine waveform in a multi-user backscatter system, made of one transmitter, one reader, and multiple tags active simultaneously. We derive an efficient algorithm to optimize the transmit waveform so as to identify the tradeoff between the amount of energy harvested at the tags and the reliability of the communication, measured in terms of signal-to-interference-plus-noise ratio (SINR) at the reader. The performance with the optimized waveform based on the linear and nonlinear EH models is studied. The numerical results demonstrate the benefits of accounting for the EH nonlinearity, multiuser diversity, frequency diversity, and multisine waveform adaptive to the CSIT to enlarge the SINR-energy region.

Waveform design is a key technique to jointly exploit a beamforming gain, the channel frequency selectivity, and the rectifier nonlinearity, so as to enhance the end-to-end power transfer efficiency of wireless power transfer (WPT). Those waveforms have been designed, assuming perfect channel state information at the transmitter. This paper proposes two waveform strategies relying on limited feedback for multi-antenna multi-sine WPT over frequency-selective channels. In the waveform selection strategy, the energy transmitter (ET) transmits over multiple timeslots with every time a different waveform precoder within a codebook, and the energy receiver (ER) reports the index of the precoder in the codebook that leads to the largest harvested energy. In the waveform refinement strategy, the ET sequentially transmits two waveforms in each stage, and the ER reports one feedback bit indicating an increase/decrease in the harvested energy during this stage. Based on multiple one-bit feedback, the ET successively refines waveform precoders in a tree-structured codebook over multiple stages. By employing the framework of the generalized Lloyd’s algorithm, novel algorithms are proposed for both strategies to optimize the codebooks in both space and frequency domains. The proposed limited feedback-based waveform strategies are shown to outperform a set of baselines, achieving higher harvested energy.

This paper investigates relaying schemes in an amplify-and-forward multiple-input multiple-output relay network, where an energy-constrained relay harvests wireless power from the source information flow and can be further aided by an energy flow (EF) in the form of a wireless power transfer at the destination. However, the joint optimization of the relay matrix and the source precoder for the EF-assisted (EFA) and the non-EFA (NEFA) schemes is intractable. The original rate maximization problem is transformed into an equivalent weighted mean square error minimization problem and optimized iteratively, where the global optimum of the nonconvex source precoder subproblem is achieved by semidefinite relaxation and rank reduction. The iterative algorithm finally converges. Then, the simplified EFA and NEFA schemes are proposed based on channel diagonalization, such that the matrices optimizations can be simplified to power optimizations. Closed-form solutions can be achieved. Simulation results reveal that the EFA schemes can outperform the NEFA schemes. In addition, deploying more antennas at the relay increases the dimension of the signal space at the relay. Exploiting the additional dimension, the EF leakage in the information detecting block can be nearly separated from the information signal, such that the EF leakage can be amplified with a small coefficient.

This paper investigates a three-node amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay network, where an autonomous relay harvests power from the source information flow and is further helped by an energy flow in the form of a wireless power transfer (WPT) at the destination. An energy-flow-assisted two-phase relaying scheme is proposed, where a source and relay joint optimization is formulated to maximize the rate. By diagonalizing the channel, the problem is simplified to a power optimization, where a relay channel pairing problem is solved by an ordering operation. The proposed algorithm, which iteratively optimizes the relay and source power, is shown to converge. Closed-form solutions can be obtained for the separate relay and source optimizations. Besides, a two-phase relaying without energy flow is also studied. Simulation results show that the energy-flow-assisted scheme is beneficial to the rate enhancement, if the transmit power of the energy flow is adequately larger than that of the information flow. Otherwise, the scheme without energy flow would be preferable.

Recently, physical-layer network coding (PNC) attracts much attention due to its ability to improve throughput in relay-aided communications. However, the implementation of PNC is still a work in progress, and synchronization is a significant and difficult issue. This paper investigates the feasibility of synchronous PNC with M-ary quadrature amplitude modulation (M-QAM). We first propose a synchronization scheme for PNC. Then, we analyze the synchronization errors and overhead of potential synchronization techniques, which includes phase-locked loop (PLL) and maximum likelihood estimation (MLE) based synchronization schemes. Their effects on the average symbol error rate and the goodput are subsequently discussed. Based on the analysis, we perform numerical evaluations and reveal that synchronous PNC can outperform conventional network coding (CNC) even when taking synchronization errors and overhead into account. The theoretical throughput gain of PNC over CNC can be approached when using the MLE based synchronization method with optimized training sequence length. The results in this paper provide some insights and benchmarks for the implementation of synchronous PNC.

Physical-layer network coding (PNC) brings throughput improvement for wireless networks. However, its synchronization requirement is widely recognized as an obstacle to its implementation. In this paper, we focus on phase-level synchronization and propose a time-slotted carrier synchronization scheme for PNC. We then analyze the phase error tolerance of PNC under different bit error rate (BER) requirements, and the synchronization overhead for obtaining synchronous signals below the phase error margin. We also consider the impact of different hardware (in particular, the phase-locked loop) parameters on the overhead in our analysis. Afterwards, we evaluate the performance of the proposed synchronization scheme with simulations. The results show that the proposed scheme is feasible with some typical hardware parameters. The throughput gain of PNC when using the proposed scheme is only slightly lower than the theoretical gain.

Recent theoretical studies of physical-layer network coding (PNC) show much interest on high-level modulation, such as M-ary quadrature amplitude modulation (M-QAM), and most related works are based on the assumption of phase synchrony. The possible presence of synchronization error and channel estimation error highlight the demand of analyzing the symbol error rate (SER) performance of PNC under different phase errors. Assuming synchronization and a general constellation mapping method, which maps the superposed signal into a set of M coded symbols, in this paper, we analytically derive the SER for M-QAM modulated PNC under different phase errors. We obtain an approximation of SER for general M-QAM modulations, as well as exact SER for quadrature phase-shift keying (QPSK), i.e. 4-QAM. Afterwards, theoretical results are verified by Monte Carlo simulations. The results in this paper can be used as benchmarks for designing practical systems supporting PNC.