Simultaneously transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) can assist in achieving full-space signal coverage enhancement. Considering eavesdropping channels, a downlink system model with full coverage of STAR-IRS enabled unmanned aerial vehicle (UAV) secure communication is proposed. The aim is to attain the maximal value of the energy efficiency (EE) by exploring the joint resource allocation of the system. To solve this coupling problem, the lower and upper bounds of the sum-rate for legitimate and eavesdropping users are derived respectively, and Lagrange duality theory is employed to deal with the power control problem. Then the reflection/transmission amplitude splitting coefficient optimization of STAR-RIS using the Hybrid whale-bat (HWB) method in energy splitting (ES) mode are considered to fully exploit the performance gains brought by STAR-IRS deployment. Finally, the simulation verifies that the proposed joint design scheme can enormously promote the EE and safety performance of the system.

Abstract The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation.

Cloud–edge–unmanned aerial vehicle (UAV) computing collaboration is crucial for smart and real-time applications. It highlights the challenges faced by UAVs in task execution, including limited endurance, computing, and communication capabilities, as well as complex external interference. This article proposes a task-driven multimodal communication technology that utilizes a multimodal confusion information reception model to improve the UAV’s ability to demodulate multimodal information. A cloud–edge–UAV collaborative computing model is then proposed to integrate cloud and edge computing capabilities into the UAV task execution system. As a result, the success probability and multimodal diversity of UAV task execution can be improved in complex environments based on both multimodal communication and cloud–edge–UAV collaboration concepts. Numerical results show that: 1) for each single-modal reception, the success probability increases by 15%, and the task execution is robust in severe environmental conditions since the success probability of eavesdropping by malicious users is reduced by 88%; 2) after multimodal combination, additional multimodal receptions results in an increase of the success probability; and 3) the reliability is highly related with the number of independent modals, which equals the multimodal diversity.

In the 6G integrated air-ground network, the process of accomplishing complex tasks through the integrated multimodal communication faces challenges induced by unmanned aerial vehicles (UAVs), such as limited communication, storage and computing capabilities, and the existence of heterogeneous UAV multimodal information and carriers. Inspired by the process of semantic communication, we view successful execution of advanced UAV tasks as semantic recognition and pragmatic execution. Tiny machine learning (TinyML) provides the UAV advanced algorithms and models that can be run on the low-power and resource-constrained platforms. In this article, from the perspective of semantic communication and leveraging the applicability of TinyML for UAVs, we map the heterogeneous multimodal communication and UAV task execution processes aiming to better utilize the capabilities of machine learning and semantic communication to enhance the pragmatic task execution of UAVs. Multimodal virtual semantic communication can provide task-related auxiliary information, enabling the complementary integration of multiple independent modalities in the task domain. The proposed scheme and model achieve a deep integration of communication, sensation, and computation ultimately enhancing the practical task execution capability of UAVs.

The possibility of jointly optimizing location sensing and communication resources, facilitated by the existence of communication and sensing spectrum sharing, is what promotes the system performance to a higher level. However, the rapid mobility of user equipment (UE) can result in inaccurate location estimation, which can severely degrade system performance. Therefore, the precise UE location sensing and resource allocation issues are investigated in a spectrum sharing sixth generation network. An approach is proposed for joint subcarrier and power optimization based on UE location sensing, aiming to minimize system energy consumption. The joint allocation process is separated into two key phases of operation. In the radar location sensing phase, the multipath interference and Doppler effects are considered simultaneously, and the issues of UE’s location and channel state estimation are transformed into a convex optimization problem, which is then solved through gradient descent. In the communication phase, a subcarrier allocation method based on subcarrier weights is proposed. To further minimize system energy consumption, a joint subcarrier and power allocation method is introduced, resolved via the Lagrange multiplier method for the non-convex resource allocation problem. Simulation analysis results indicate that the location sensing algorithm exhibits a prominent improvement in accuracy compared to benchmark algorithms. Simultaneously, the proposed resource allocation scheme also demonstrates a substantial enhancement in performance relative to baseline schemes.

The future integrated sensing and communication (ISAC) networks is expected to equip with sufficient computation resources. However, current research focuses on single-domain resource allocation in ISAC and computing force networks, leaving the joint optimization of sensing, communication, and computation resource allocation unexplored. In this paper, we propose a novel approach to this problem by deep incorporating computation resources, combined with a federated learning framework, while considering sensing precision and power consumption. Firstly, a multi-objective optimization is designed, involving Cramer-Rao Bound, sum rate of ISAC networks, and power consumption of computing force networks. Subsequently, the multi-objective optimization is transformed into a multi-task learning model. We aim to obtain joint optimization of sensing, communication, and computation resource allocation via deep learning techniques. Towards the multi-task learning model, the multiple-gradient descent algorithm is utilized to obtain the multi-objective optimization. Furthermore, a practical low-complexity the multiple-gradient descent algorithm is developed to reduce the computational cost. Finally, the effectiveness of the proposed deep learning algorithms is verified by simulations results.

One of the main challenges of popularizing the integration of sensing and communication (ISAC) network is mutual interference between the two functions. A viable solution is the time division scheme where communication and sensing are separated in time domain. This paper considers a multi-cluster ISAC network model, where the time-domain radio resources are allocated to sensing and communication. At the same time, the time resources can be reused in space-domain. Particularly, the terminals in different work phases can access radio resources of different or the same clusters simultaneously, depending on interference. In this way, the interference is isolated while the resources utilization is improved. Two different resource allocation approaches are proposed according to how interference is considered. The aim is to maximize the sensing detection probability under the constraint of network throughput. The performance improvement in terms of target detection probability brought by the proposed schemes is shown by numerical results compared with benchmark methods.

In this article, we proposed a symbiotic localization and the ambient backscatter communication (SLABC) architecture, which uses existing ambient backscatter communication (AmBC) hardware and received signals to help Internet of Things (IoT) localize target objects. The SLABC can be viewed as a specific realization of integrated passive/symbiotic sensing and communication, which has two mutually beneficial stages: 1) sensing stage with generalized Prony with homologous matching (GPHM) method and 2) communication stage with multisource constant modulus algorithm (MSCMA) method. To formulate SLABC, we propose a unified TMSC signal model to characterize the complex multisource, multipath, and multireflection symbiotic communication (TMSC), and facilitate the key information extraction. Utilizing the frequency domain expression of the unified signal model and the similarity between multipath channel coefficients and tap coefficients of equalizers, our proposed GPHM and MSCMA methods are efficient to mitigate the TMSC interference and retrieve the interested information to locate targets and communicate. The proposed SLABC concept updates the symbiotic AmBC IoT with the least cost and reduces the dependence on hardware stacking. The simulation results show that: 1) information extracted by GPHM and MSCMA can help localization achieve high accuracy and reduce the complexity of multipath interference mitigation and 2) both of the two methods have a positive influence on each other.

Spectrum harvesting is needed for large-scale wireless networks to access underutilized spectrum and support multiple heterogeneous users. Cooperative spectrum harvesting (CSH) allows for improved co-channel existence and intra-/inter-cell interference mitigation, which dramatically improves spectral efficiency. However, good performance metrics to quantify CSH schemes are not available. For example, existing metrics such as data rate, error/outage probability, and multiplexing/diversity gains may not clearly distinguish large signal-to-interference-plus-noise ratio (SINR) scenarios and sum-rate performance for multiple links. To overcome these limitations, we propose two new metrics called spectrum access level (SAL) and user participation level (UPL). The advantages of these metrics are: 1) achieving distinct upper bounds for multiple links; 2) upper bounds being evaluated directly by basic CSH system model; and 3) determining the performance at any power level of CSH schemes even if they are not interference exempt. Moreover, a novel CSH system model is conceived to achieve satisfying spectrum access ability based on SAL and UPL, and an interference-exempt scheme is designed to achieve relevant upper bounds. Numerical results verify the efficiency of SAL and UPL, and the spectrum access ability of proposed system model with interference-exempt scheme.

In this paper, we propose a novel successive two-way relaying (STWR) system that uses a pair of conventional half-duplex (HD) relays to mimic a full-duplex two-way relay (FD-TWR). Although classical FD-TWR is spectral efficient and expands cell coverage, the proposed STWR utilizes the existing HD infrastructure to boost the FD implementation and offers bi-directional data exchange and low-complexity residual self-interference (RSI) mitigation. To formulate STWR, we develop a unified signal model to facilitate the mitigation of the generalized self-interference (GSI). GSI consists of back-propagating interference due to two-way relaying, RSI of FD sources and inter-relay interference caused by the pairs of HD relays. Because the GSI channel matrix has a distinct row linearity, we propose an efficient digital approach to remove the GSI and design two low-complexity algorithms. These algorithms avoid RSI channel estimation, full-rank matrix, and complex matrix computation. Our analysis and simulations show that: 1) the proposed STWR achieves the multiplexing gain of the true FD-TWR; 2) the distance between the two HD relays should be optimized to achieve the highest spectral efficiency; and 3) the STWR system with two algorithms can achieve a diversity order of one or two, respectively. Therefore, the STWR concept achieves a flexible tradeoff between performance and complexity, potentially enabling large-scale relay deployments.

Successive relaying has recently emerged as a spectral-efficient technique for cooperative wireless communications. However, in scenarios with uncertain accuracy of instantaneous channel state information (CSI), the scope of conventional two-path successive relaying protocol shrinks. This is due to the challenges of cancelling inter-relay interference (IRI) and detecting signals efficiently without specific CSI. The two challenges motivate us to propose a novel double listening 3-path successive relaying (DL3PSR) protocol to mitigate the successive relaying's dependence on accurate instantaneous CSI. We overcome the first challenge by proposing a blind IRI cancellation technique, and its effectiveness is proven. After blind IRI cancellation, the challenge of efficient signal detection is overcome by robust cooperative non-coherent detection, which consists of exclusive OR (XOR) demodulation and joint decoding. Based on constellation mapping, XOR demodulation is designed to demodulate M-ary phase-shift keying symbols without instantaneous CSI. Moreover, joint forward and backward decoding strategy is proposed to improve the robustness of data detection by retrieving two independent versions of the original data. Furthermore, the detection threshold, bit error probability, and achievable rate of DL3PSR are theoretically obtained. Simulations verify that in scenarios without full knowledge of CSI, DL3PSR protocol outperforms conventional two-path successive relaying in bit error performance and that the rate of DL3PSR is close to that of the full-duplex relaying. Copyright © 2016 John Wiley & Sons, Ltd.

Owing to environmental, financial and quality of service (QoS) considerations, energy efficient wireless communications have been paid increasing attention under the background of limited energy resource. This study considers a spectrum sharing cognitive radio network, where the primary system leases its radio spectrum to the secondary system for a fraction of time in exchange for secondary users served as relays to assist the transmission of the primary traffic. Based on the cooperative spectrum leasing protocol, the authors formulate a joint optimisation of relay selection and power allocation under QoS requirements to improve energy efficiency (EE). By employing a greedy spectrum sharing (GSS) algorithm, the optimal relay selection, power and sharing time allocation are readily obtained. Monte–Carlo simulations are performed to demonstrate that significant EE improvement is achieved by the proposed GSS algorithm, and that the network performance is enhanced.

The high‐speed mobility of modern transportation worsens the situation of unstable direct links and imprecise channel estimation, which poses challenges on fast and reliable data delivery in vehicular ad hoc networks (VANETs). The goal of the proposed scheme is to provide high‐speed full‐duplex relaying connectivity for unstable direct links, and reliable information detection without precise channel state information (CSI) in VANETs. In the proposed scheme, full‐duplex relaying connectivity is provided by differential successive relays (DSRs) at roadside or inside other vehicles. To improve robustness of DSR without specific CSI, efficient blind interference cancellation is presented to mitigate inter‐relay interference. Meanwhile, reliable information detection without precise CSI is enabled by superposition coding with differential modulation. Furthermore, the advantages, applications of DSR and complexities for real‐world implementation are discussed, and the decision rule, bit error probability and ergodic capacity are theoretically obtained. Numerical results verify significant improvements in the performance of the proposed scheme when compared with conventional schemes with half‐duplex relays and frequent channel estimation in VANETs.