Movable antenna (MA) technology enables flexible beamforming through adaptive adjustment of antenna positions, thereby enhancing communication performance. This paper investigates the MA-aided air-to-ground (A2G) system, where an unmanned aerial vehicle (UAV) equipped with MAs serves multiple ground users. The primary objective is to jointly optimize UAV deployment, antenna positions, beamforming, and power control to maximize the downlink communication sum rate. A key challenge lies in the difficulty of acquiring complete instantaneous channel state information (I-CSI), as the antennas cannot traverse all possible positions to obtain such full-scale I-CSI, which impedes the efficient optimization of system parameters. To address this challenge, we propose a two-stage transmission scheme: in the first stage, location-aware statistical CSI (S-CSI) is leveraged to optimize UAV deployment, power control, and MA positions for maximizing long-term system performance, while the second stage estimates I-CSI based on the optimized UAV and antenna positions to refine short-term transmit beamforming after long-term variables are determined, thereby substantially reducing CSI acquisition overhead. For long-term policy optimization, we introduce a novel dual-spatial-scale hierarchical deep reinforcement learning (DSSH-DRL) framework, which preserves flexibility in global policy formulation while enhancing training efficiency via effective decoupling of control variables. Simulation results demonstrate the effectiveness of the proposed algorithm and validate the promising application potential of MA technology in A2G communication networks.

To address the challenge of anti-jamming transmission in wireless communication systems under complex interference environments, this paper proposes a movable antennas systems anti-jamming transmission scheme based on deep unfolding networks. Although existing research has enhanced the diversity of anti-jamming strategies through mobile antenna technology, its optimization algorithms are complex, system overhead is large, and traditional deep learning schemes face problems such as strong data dependence and poor interpretability. Therefore, this paper innovatively introduces deep unfolding networks into the design of movable antennas systems, achieving efficient optimization through the fusion of algorithm unfolding and neural networks. Specifically, for the multi-jammer confrontation scenario, the non-convex joint optimization problem is decoupled into an alternating optimization problem of the receiving beamforming vector and antenna position, where the beamforming is solved in closed form through the generalized Rayleigh quotient. Further, by mapping the iterative optimization steps to the hidden layers of the neural network, a lightweight deep unfolding framework is constructed, significantly reducing the computational complexity of traditional iterative algorithms. Simulation results verify the effectiveness and reliability of the proposed scheme in real-time anti-jamming transmission under jamming environments.

The drastic growth of the low-altitude airspace (LAA) applications, such as over the horizon control, low-altitude logistic, and all-time supervision, produces strong demands for communication and sensing services toward LAA. Under the idea of integrated sensing and communication (ISAC), this work investigates the low-complexity/high-accuracy active sensing problem toward LAA using a communication information-bearing orthogonal frequency division multiplexing (OFDM) waveform. A general and structured fast/slow-time representation for the OFDM input-output relation is first derived, which can clearly show the effects of delay and Doppler frequency shift (or equivalently, range and radial velocity of target) on the received echo. Based on the fast/slow-time representation, an ambiguity function (AF)-assisted active sensing scheme for the OFDM-based ISAC system is proposed. The scheme consists of three modules, i.e., a coarse search and triangular AF matching module, a gradient-based fine tune module and a modified cell-averaging constant false alarm rate (CA-CFAR) target detection module, which work coordinately to realize joint target detection and high-accuracy target range/velocity estimation. Simulation results show that the proposed scheme can realize better detection reliability and range/velocity estimation accuracy with much less computations than the reference schemes.

Due to the sharing of communication and sensing signals, integrated sensing and communication (ISAC) systems are vulnerable to potential eavesdropping attacks. This article investigates simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled secure ISAC for Internet of Everything (IoE) networks. Two typical ISAC scenarios are explored: 1) communication enhancement (CE) and 2) communication-sensing fusion (CSF). In the CE scenario, the communication user (CU) is located in the Non-Line of Sight (NLoS) region of the ISAC base station (ISAC-BS), while the sensing target is located in the Line-of-Sight (LoS) region of the ISAC-BS. In the CSF scenario, both the CU and the sensing target are located in the NLoS region of the BS. The STAR-RIS is deployed to improve communication and sensing performance. The artificial noise (AN)-aided robust and secure beamforming problems are formulated for different scenarios based on imperfect channel knowledge. Several STAR-RIS protocols are also considered to provide a more complete system design and performance analysis. Efficient iteration-based algorithms are developed to solve these nonconvex and highly coupled problems. Based on simulation results, we observe that: 1) the proposed methods can effectively improve the system performance and 2) from the perspective of communication security, the dedicated AN signal is helpful for STAR-RIS-enabled ISAC systems.

We investigate the resource allocation problem of a cell-free massive multiple-input multiple-output system under the condition of colluding eavesdropping by multiple passive eavesdroppers. To address the problem of limited pilot resources, a scheme is proposed to allocate the pilot with the minimum pollution to users based on access point selection and optimize the pilot transmission power to improve the accuracy of channel estimation. Aiming at the secure transmission problem under a colluding eavesdropping environment by multiple passive eavesdroppers, based on the local partial zero-forcing precoding scheme, a transmission power optimization scheme is formulated to maximize the system’s minimum security spectral efficiency. Simulation results show that the proposed scheme can effectively reduce channel estimation error and improve system security.

Orthogonal time frequency space (OTFS) modulation has been advocated as a promising waveform for achieving integrated sensing and communication (ISAC) due to its superiority in high-mobility adaptability and spectral efficiency. Despite its advantages, due to the complex input-output relation of the OTFS processing, the current OTFS based ISAC schemes require high computation complexity/latency to achieve satisfactory sensing accuracy. In this paper, a concisely fast/slow-time representation for the OTFS input-output relation is derived, which can clearly reveal the physical interactions between the OTFS signals and sensing channels. Under this basis, a novel delay-Doppler spectrum matching assisted active sensing framework is proposed for the OTFS based ISAC systems. The framework consists of three sequentially operated modules, i.e., a coarse searching and triangular spectrum matching module, a local fine tune and quadratic spectrum matching module, and a constant false alarm rate (CFAR) target detection module. The three modules work coordinately to realize joint target detection and high-accuracy target range/velocity estimation. Simulation results show that the proposed framework achieves better detection performance and range/velocity estimation accuracy with much less computations when compared with the reference schemes.

In this article, an intelligent reflecting surface (IRS)-assisted integrated satellite-unmanned aerial vehicle (UAV)-terrestrial (SUT) Internet of Things (IoT) network faced with a smart jammer under imperfect channel state information (CSI) conditions is considered. We propose a Stackelberg game model to describe the adversarial relationship between the satellite, UAV, and IRS and the jammer, which are modeled as the leader and the follower, respectively. For the follower subgame, the jammer aims to minimize the jamming power while guaranteeing that the jamming energy efficiency surpasses a certain threshold. Under this setup, the Angle of Arrival (AoA)-based discretization method is utilized to address the imperfect CSI issue. Then, the use-and-then-forget method and the Lagrangian function are employed to obtain a closed-form expression for the jammer’s power. Finally, a feasible jamming power solution is obtained by means of the Cauchy–Schwarz inequality. For the leader subgame, we aim to optimize the hybrid beamforming design of the satellite, UAV and IRS, with the goal of minimizing the total transmit power, while guaranteeing that the downlink received signal-to-interference-plus-noise ratio (SINR) surpasses the minimum communication threshold. We propose an alternating optimization scheme, in which the Cauchy–Schwarz inequality, the AoA-based discretization method, and nonsmooth penalty functions are employed to alternately obtain the optimal satellite beamforming vector, UAV beamforming vector and IRS phase matrix when the other variables are fixed. Through our analytical and numerical results, the proposed beamforming scheme achieves a reduction of 16.9% in average power consumption compared with other benchmark schemes when obtaining the same anti-jamming performance.

The utilization of the aerial base station (ABS) has been treated as a promising solution to the coverage, deployment and cost savings problems in the wireless networks. Different from the terrestrial networks, in the ABS-assisted air-to-ground network, the service positions of ABSs and user association should be jointly optimized, which is challenging due to the unknown propagation environment in the task area. In this article, a binary radio map (BRM) is constructed to grant the ABSs the location-specific channel knowledge within the entire task area. With the assistance of the BRM, a multiple ABS deployment and user association framework is proposed. The framework consists of an offline optimization stage during which the initial ABS deployment position and user association are jointly optimized based on the BRM, and an online refinement stage during which the ABS positions and user association are refined in real time according to both the observed Quality of Service (QoS) and virtual QoS emulated by the BRM. The performance of the proposed framework is examined in a complex urban scenario with mobile users. The results show that higher achievable rate performance and lower on-board energy consumption can be realized when compared with the reference schemes.

Due to the openness of the wireless propagation environment, wireless networks are highly susceptible to malicious jamming, which significantly impacts their legitimate communication performance. This study investigates a reconfigurable intelligent surface (RIS) assisted anti-jamming communication system. Specifically, the objective is to enhance the system’s anti-jamming performance by optimizing the transmitting power of the base station and the passive beamforming of the RIS. Taking into account the dynamic and unpredictable nature of a smart jammer, the problem of joint optimization of transmitting power and RIS reflection coefficients is modeled as a Markov decision process (MDP). To tackle the complex and coupled decision problem, we propose a learning framework based on the double deep Q-network (DDQN) to improve the system achievable rate and energy efficiency. Unlike most power-domain jamming mitigation methods that require information on the jamming power, the proposed DDQN algorithm is better able to adapt to dynamic and unknown environments without relying on the prior information about jamming power. Finally, simulation results demonstrate that the proposed algorithm outperforms multi-armed bandit (MAB) and deep Q-network (DQN) schemes in terms of the anti-jamming performance and energy efficiency.

In this article, we propose a novel localization and channel estimation integration framework for cell-free massive multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems, in which position information supports accurate channel estimation and accurate channel information can, in turn, improve positioning accuracy. Under this integration framework, we propose a two-phase fingerprint-based localization method consisting of both initial and accurate localization phases and a coarse-location-based (CLB) pilot reassignment scheme. The coarse location information for pilot reassignment is obtained in the initial localization phase of the two-phase localization method, and the fingerprint information used in the accurate localization phase is extracted through channel estimation based on the CLB scheme. Furthermore, for localization, two different fingerprint similarity criteria are proposed to meet the requirements of the different localization phases. Simulation results demonstrate that our proposed two-phase fingerprint-based localization method achieves better positioning performance than existing methods, although there is a slight increase in computational complexity compared to the initial localization. Moreover, our proposed CLB pilot reassignment scheme outperforms the conventional pilot assignment schemes in the comprehensive performance considering both channel estimation performance and complexity.

Elevating base stations (BSs) in the air, forming what are called aerial platforms (APs), has recently been treated as a promising solution to the problems of coverage, deployment and cost savings in mobile networks. Unlike terrestrial transmission with fixed BSs, AP-assisted air-to-ground (A2G) communications require joint optimization of the transmit parameters and AP service position, which is challenging due to the unknown propagation environment in the overall flying area. In this paper, a novel binary channel feature map (CFM) concept is proposed to grant the AP knowledge of the global propagation environment. With the assistance of the binary CFM, a unified A2G information and energy delivery framework is proposed, which includes three modules, i.e., a transmit optimization and user satisfaction evaluation module, an offline AP position planning module and an online AP position adjustment module. These modules operate coordinately to jointly perform beamforming matrix optimization for information delivery, transmit covariance optimization for energy delivery, and three-dimension AP service position planning and real-time adjustment. The simulation results reveal that the proposed framework can achieve higher user satisfaction level and lower outage probability, and can result in great savings of battery power or fuel in the AP when compared with reference schemes.

In this paper, beam-domain (BD) anti-jamming transmission in a downlink massive multiple-input multiple-output (MIMO) system is investigated. A smart jammer with multiple antennas attempts to interfere with the signal reception of users with the desired energy efficiency (EE), whereas a base station (BS) tries to minimize the transmission cost while ensuring uninterrupted communication. A Bayesian Stackelberg game between the BS and jammer, where the jammer is the follower and the BS acts as the leader, is modeled. In the follower subgame, the optimal jamming precoding with a closed-form power solution is introduced. The optimal jamming power is proportional to the transmission power in the downlink, and thus, for the BS, the strategy of suppressing malicious attacks by increasing the transmission power fails. In the leader subgame, generalized zero-forcing (ZF), whose closed-form power solution constitutes the unique Stackelberg equilibrium (SE) with that of the jammer, is found to be the optimal anti-jamming precoding for robust transmission. The results show that there always exists a precoding solution for the BS that ensures reliable transmission when the SE is obtained. A proper increase in the minimum signal-to-interference-and-noise ratio (SINR) threshold or the BD channel approximation error helps the BS save power during the resistance against the jammer. Then, a simplified power solution without the instantaneous channel state information (CSI) of jamming channels is further introduced for practical implementation. Numerical results are provided to verify the proposed solutions.

In this paper, we investigate the interactions between a base station (BS) and a full-duplex (FD) active eavesdropper in the uplink of multi-cell massive multiple-input multiple-output (MIMO) systems. Focusing on the resistance against simultaneous eavesdropping and jamming, we introduce a beam extraction method based on spatial information to eliminate pilot contamination, from which a beam-domain (BD) distributed receiving scheme is proposed to suppress the jamming mixed in users' signals. From a practical point of view, we use the perspective of a hierarchical game to analyze the interactions without the prior information of the eavesdropper. The results show that with the help of the proposed scheme, the performance of the FD-mode attack is inferior to that of passive eavesdropping. A statistical upper bound of eavesdropping is given in a closed-form, which is mainly determined by the direction and distance of the eavesdropper relative to users. Then, a joint power and combining matrix optimization algorithm is proposed to improve the secrecy capacity of the system.

This paper considers the channel estimation for massive multiple-input multiple-output (mMIMO) systems, where the base station (BS) employs hybrid analog-digital processing to reduce the hardware complexity. With hybrid analog-digital processing, the BS cannot obtain enough training samples since the number of radio frequency (RF) chains is limited. This results in significant difficulty in the design of channel estimation scheme. This paper aims to address this problem using the emerging machine learning techniques. By exploiting the sparsity property of angular-domain channel, a novel clustered sparse Bayesian learning (SBL) approach is proposed to estimate the channel reliably. The scheme does not require the knowledge of channel statistics and angular information of users. The numerical simulations show that the proposed scheme based on clustered SBL outperform the reference schemes significantly in term of normalized mean square error (NMSE).

In this study, the authors consider the simultaneous wireless information and power transfer (SWIPT) protocol design for the massive multiple‐input multiple‐output (MIMO) system in the beam‐domain. In this system, the base station (BS) simultaneously serves a set of half‐duplex energy‐constrained terminals that are uniformly distributed within its coverage area. Based on the beam‐domain distributions of channels, the BS can intelligently schedule terminals to mitigate the interference between terminals and improve the transmission spectral efficiency. The entire protocol can be divided into two phases. The first phase is designed for terminals energy harvesting as well as downlink training. During this phase, the BS transmits energy signals to the terminals. The terminals utilise the received energy signals for energy harvesting and downlink channel estimation. In the second phase, the BS forms the receive beamformers to receive signals transmitted by terminals. The transmit powers at the BS and the time switching ratio are optimised under the constraints of the current available energy and minimum transmission rate of terminals, so that the system can achieve the maximum sum‐rate performance. Simulation results show that compared with traditional massive MIMO SWIPT protocols, the proposed SWIPT protocol can achieve better spectral efficiency performance.

In this study, the authors consider the direct link aware relay selection schemes for full‐duplex relaying (FDR) protocol. Different from traditional scheme, the direct link between source and destination is exploited to improve the performance of considered schemes. By investigating the signal‐to‐interference‐plus‐noise ratios at the relay and the destination, the exact expressions of outage probabilities for FDR protocol with proposed relay selection schemes are derived in closed‐form over Nakagami‐ m fading channels. To get more insight about the effect of system parameters, they develop an asymptotic analysis on the outage performance. The authors’ results show that the achievable diversity of the schemes depends on the shape factors of fading channels and the power scaling scheme of the relays. Moreover, they show that the proposed relay selection schemes achieve better outage performance when compared with traditional schemes and are robust to the residual self‐interference caused by the full‐duplex operation.

This paper considers the multi-pair full-duplex massive MIMO relaying, where multiple source-destination pairs communicate simultaneously with a common full-duplex amplify-and-forward relay. The low-complexity transceiver design at the relay based on zero-forcing (ZF) processing is presented. The asymptotic expression of the end-to-end signal-to-interference-plus-noise power (SINR) is derived under the general power scaling scheme. Through the theoretical results, we show that the deployment of very large antenna arrays at the relay has potential to eliminate the effect of echo interference due to the full-duplex operation, if the power scaling scheme is properly selected. Moreover, it is shown that the multi-pair full-duplex relaying outperforms the multi-pair half-duplex relaying in the spectral and energy efficiencies when the number of relay receive/transmit antennas is large.

The opportunistic full‐/half‐duplex relaying schemes are studied where the effects of imperfect channel state information (CSI) and transmit imperfection are taken into account. The opportunistic schemes with different levels of channel knowledge, that is, the scheme with CSI at the transmitter (CSIT), the scheme with CSI at the receiver (CSIR) and the scheme with only statistical CSI, are proposed. Power adaption (PA) schemes with different levels of channel knowledge are presented and combined with the opportunistic schemes to further improve the system performance. The opportunistic schemes are evaluated in the term of average capacity. The author's results show that the opportunistic scheme with CSIR achieves comparative performance to the scheme with CSIT when the PA is not used. Moreover, it is shown that the choice of full‐duplex relaying becomes more attractive when the quality of estimation for desired channels degrades. However, the converse is true as the quality of estimation for echo interference channel degrading or the power of transmit noise increasing.

This paper considers the transceiver design and antenna subset selection for cooperative half-duplex relaying (HDR) and full-duplex relaying (FDR) protocols. We first present the low-complexity transceiver design for given antenna subset at each node. Then the antenna subset selection schemes for both FDR and HDR protocols are proposed and evaluated in the terms of outage probability and achievable diversity. For HDR protocol, we show that the increase of antenna subset size, although cannot increase the achievable diversity, does improve the outage performance of the protocol by increasing the coding gain. Moreover, we show that the antenna configuration at the relay can impact the achievable diversity of FDR protocol significantly and the antenna configuration that optimizes the achievable diversity is analyzed.

In this paper, we analyze the performance of two-way relaying (TWR) protocol in Rayleigh fading channels, where the terminals and relay are interfered by a finite number of co-channel interferers. The relay is assumed to operate in the decode-and-forward mode. The average symbol error rate (SER) performance for binary phase shift keying (BPSK) is analyzed. To make the analysis mathematically tractable, two approximations are adopted to deal with the problem of correlations between the received SINRs of the relay and terminal and a tight approximate expression of the average SER is derived in closed-form. Moreover, it can be shown that the result can be simply applied in straightforward network coding protocol with co-channel interference. Based on the analytic results, we study the impacts of system parameters, such as interference power, number of interferers and relay placement, on the average SER performance. Finally, the correctness of our analytic results is validated through Monte Carlo simulations.

In this paper, we investigate the outage performance of time division broadcasting (TDBC) protocol in independent but non-identical Rayleigh flat-fading channels, where all nodes are interfered by a finite number of co-channel interferers. We assume that the relay operates in the amplified-and-forward mode. A tight lower bound as well as the asymptotic expression of the outage probability is obtained in closed-form. Through both theoretic analyses and simulation results, we show that the achievable diversity of TDBC protocol is zero in the interference-limited scenario. Moreover, we study the impacts of interference power, number of interferers and relay placement on the outage probability. Finally, the correctness of our analytic results is validated via computer simulations.

Opportunistic relay selection (RS) is an efficient method to obtain diversity gain in analogue network coding (ANC) protocol with multiple relays. However, in networks with mobile relays, the channel state information (CSI) used in the RS procedure becomes outdated because of the time‐varying nature of fading channels, which severely deteriorates the system performance. In this study, the authors study the RS strategy which aims to optimise the outage performance of the ANC protocol with multiple mobile relays. RS schemes for two different cases are designed, that is, (i) the scheme with only the outdated CSI during the RS procedure, and (ii) the scheme with both the outdated CSI and the statistical knowledge of channels during the RS procedure. The closed‐form expressions of the outage probabilities as well as the asymptotic expressions are analytically derived for the proposed schemes, and moreover, the achievable diversities are analysed based on the asymptotic expressions. Simulation results are presented to evaluate the performances while validate the theoretical analyses for the proposed schemes.

In this paper, a novel receiver for Hexagonal Multicarrier Transmission (HMT) system based on the maximizing Signal-to-Interference-plus-Noise Ratio (Max-SINR) criterion is proposed. Theoretical analysis shows that the prototype pulse of the proposed Max-SINR receiver should adapt to the root mean square (RMS) delay spread of the doubly dispersive (DD) channel with exponential power delay profile and U-shape Doppler spectrum. Simulation results show that the proposed Max-SINR receiver outperforms traditional projection scheme and obtains an approximation to the theoretical upper bound SINR performance within the full range of channel spread factor. Meanwhile, the SINR performance of the proposed prototype pulse is robust to the estimation error between the estimated value and the real value of time delay spread.