As the sixth-generation (6G) networks evolve towards a deep integration of communication and computation (ICC), they face challenges of inherent interference and resource competition between heterogeneous services. To address this issue, this paper investigates an uplink ICC system enhanced by a stacked intelligent metasurface (SIM), where SIM’s unique multi-layer structure transforms the wireless channel into a controllable, task-oriented medium. The system is designed to support the coexistence of over-the-air computation (AirComp) tasks, which require high-precision results, and traditional tasks that demand high-quality communication. To this end, we formulate a joint optimization framework aiming to minimize the total mean squared error (MSE) of all computation tasks while strictly guaranteeing the communication quality of service (QoS). To solve the highly non-convex problem of synergistically designing the system resources, we propose an efficient alternating optimization (AO) algorithm. Simulation results demonstrate that the proposed algorithm not only converges rapidly but also achieves up to a 95.2% reduction in total computation MSE compared to an ICC system without SIM, while also significantly outperforming other benchmark schemes, validating the great potential of SIM in proactively managing multi-service conflicts and enabling efficient ICC.

Next-generation low Earth orbit (LEO) satellite constellations face critical bottlenecks in spectral efficiency and onboard hardware complexity. To overcome these limitations, this paper introduces a novel architecture enabled by metasur-face antennas (MAs) at the LEO satellites. In particular, MAs are metasurface-integrated feed antennas that perform highprecision beamforming directly in the wave domain, thereby effectively mitigating multi-user interference. Based on such an antenna architecture, a weighted sum rate (WSR) maximization problem is formulated by jointly optimizing the scheduling of feed antennas to terrestrial users (TUs) and the passive beamforming of the metasurface for system performance enhancement. To address this mixed-integer nonlinear programming (MINLP) challenge, an alternating optimization (AO)-based joint scheduling and beamforming algorithm is proposed. On the one hand, the proposed algorithm incorporates a polynomial-time minimum-cost maximum-flow (MCMF) method, which is dedicated to the optimal scheduling of feed antennas and TUs. On the other hand, it adopts a weighted minimum mean square error (WMMSE) method integrated with semidefinite relaxation (SDR) technique, which is tailored for metasurface beamforming design. Simulation results confirm the effectiveness of the proposed algorithm for MA-enabled LEO satellite constellation communications.

Low earth orbit (LEO) satellite constellations are becoming a cornerstone of next-generation satellite networks, enabling worldwide high-precision navigation and high-quality remote sensing. This paper proposes a novel dual-function LEO satellite constellation frame structure that effectively integrating navigation and remote sensing. Then, the Cramer-Rao bound (CRB)-based positioning, velocity measurement, and timing (PVT) error and the signal-to-ambiguity-interference-noise ratio (SAINR) are derived as performance metrics for navigation and remote sensing, respectively. Based on it, a joint beamforming design is proposed by minimizing the average weighted PVT error for navigation user equipments (UEs) while ensuring SAINR requirement for remote sensing. Simulation results validate the proposed multi-satellite cooperative beamforming design, demonstrating its effectiveness as an integrated solution for next-generation multi-function LEO satellite constellations.

To provide multiple-satellite coverage for global Internet of Things (IoT), a low Earth orbit (LEO) satellite IoT constellation usually contains multiple-layer orbits with different altitudes. However, the performance of multiple-layer LEO satellite IoT constellations under practical Rician fading satellite channels remains unknown due to complex theoretical modeling and intractable mathematical analysis. To address these challenges, this paper proposes a stochastic geometry-based modeling and analysis framework for multiple-layer LEO satellite IoT constellations, integrating Rician channel modeling and Cox point processes. Specifically, we introduce a novel channel approximation method to overcome the intractable expressions caused by the Rician fading. Building on this method, we derive exact closed-form expressions for key performance metrics, including connectivity probability, coverage probability, and transmission rate, especially in the case of IoT short-packet transmission. Extensive simulation results validate the accuracy and effectiveness of the proposed model and reveal significant design insights. The results not only provide new theoretical perspectives for modeling and analysis of LEO satellite IoT constellations but also offer practical guidance for system deployment and optimization.

Low Earth orbit (LEO) satellite Internet of Things (IoT) has been identified as one of the important components of the sixth-generation (6G) non-terrestrial networks (NTN) to provide ubiquitous connectivity. Due to the low orbit altitude and high mobility, a massive number of satellites are required to form a global continuous coverage constellation, leading to a high construction cost. To this end, this paper proposes a LEO satellite IoT constellation design algorithm with the goal of minimizing the total cost while satisfying quality of service (QoS) requirements in terms of coverage ratio and communication quality. Specifically, with a novel fitness function and efficient algorithm’s operators, the proposed algorithm converges more quickly and achieves lower constellation construction cost compared to baseline algorithms under the same QoS requirements. Theoretical analysis proves the global and fast convergence of the proposed algorithm due to a novel fitness function. Finally, extensive simulation results confirm the effectiveness of the proposed algorithm in LEO satellite IoT constellation design.

Low earth orbit (LEO) satellite Internet of Things (IoT) has been recognized as a pivotal element within the realm of sixth-generation (6 G) non-terrestrial networks (NTN), aimed at delivering ubiquitous connectivity. Due to the low orbit altitude and fast movement speed, a massive number of satellites are needed to form a satellite constellation, resulting in substantial construction costs. To this end, this paper proposes a LEO satellite IoT constellation design algorithm with the goal of minimizing the total cost while satisfying quality of service (QoS) requirements in terms of coverage ratio and communication quality. Simulation results validate the efficiency of the proposed algorithm in LEO satellite IoT constellation.

Integrated sensing and communication (ISAC) and ubiquitous connectivity are two usage scenarios of sixth generation (6G) networks. In this context, low earth orbit (LEO) satellite constellations, as an important component of 6G networks, is expected to provide ISAC services across the globe. In this paper, we propose a novel dual-function LEO satellite constellation framework that realizes information communication for multiple user equipments (UEs) and location sensing for interested target simultaneously with the same hardware and spectrum. In order to improve both information transmission rate and location sensing accuracy within limited wireless resources under dynamic environment, we design a multiple-satellite cooperative information communication and location sensing algorithm by jointly optimizing communication beamforming and sensing waveform according to the characteristics of LEO satellite constellation. Finally, extensive simulation results are presented to demonstrate the competitive performance of the proposed algorithms.

In this paper, we provide a novel dual-function low earth orbit (LEO) satellite constellation architecture that provides information communication services while enabling location sensing of potential target. In order to improve both information transmission rate and location sensing accuracy, we propose a joint communication beamforming and sensing waveform design algorithm. Finally, numerical results and Monte Carlo simulations are presented to demonstrate the competitive performance of the proposed algorithm.

In this paper, we investigate the issue of satellite-terrestrial computing in the sixth generation (6G) wireless networks, where multiple terrestrial base stations (BSs) and low earth orbit (LEO) satellites collaboratively provide edge computing services to ground user equipments (GUEs) and space user equipments (SUEs) over the world. In particular, we design a complete process of satellite-terrestrial computing in terms of communication and computing according to the characteristics of 6G wireless networks. In order to minimize the weighted total energy consumption while ensuring delay requirements of computing tasks, an energy-efficient satellite-terrestrial computing algorithm is put forward by jointly optimizing offloading selection, beamforming design and resource allocation. Finally, both theoretical analysis and simulation results confirm fast convergence and superior performance of the proposed algorithm for satellite-terrestrial computing in 6G wireless networks.

In this paper, we investigate the issue of uplink integrated sensing and communication (ISAC) in 6G wireless networks where the sensing echo signal and the communication signal are received simultaneously at the base station (BS). To effectively mitigate the mutual interference between sensing and communication caused by the sharing of spectrum and hardware resources, we provide a joint sensing transmit waveform and communication receive beamforming design with the objective of maximizing the weighted sum of normalized sensing rate and normalized communication rate. It is formulated as a computationally complicated non-convex optimization problem, which is quite difficult to be solved by conventional optimization methods. To this end, we first make a series of equivalent transformation on the optimization problem to reduce the design complexity, and then develop a deep learning (DL)-based scheme to enhance the overall performance of ISAC. Both theoretical analysis and simulation results confirm the effectiveness and robustness of the proposed DL-based scheme for ISAC in 6G wireless networks.

Low earth orbit (LEO) satellite internet of things (IoT) is a promising way achieving global Internet of Everything, and thus has been widely recognized as an important component of sixth-generation (6G) wireless networks. Yet, due to high-speed movement of the LEO satellite, it is challenging to acquire timely channel state information (CSI) and design effective multibeam precoding for various IoT applications. To this end, this paper provides a deep learning (DL)-based joint channel prediction and multibeam precoding scheme under adverse environments, e.g., high Doppler shift, long propagation delay, and low satellite payload. Specifically, this paper first designs a DL-based channel prediction scheme by using convolutional neural networks (CNN) and long short term memory (LSTM), which predicts the CSI of current time slot according to that of previous time slots. With the predicted CSI, this paper designs a DL-based robust multibeam precoding scheme by using a channel augmentation method based on variational auto-encoder (VAE). Finally, extensive simulation results confirm the effectiveness and robustness of the proposed scheme in LEO satellite IoT.

In this paper, we investigate the issue of integrated communication and computation for multiple time-sensitive computation-intensive user equipments (UEs) with different types of tasks in edge-intelligent networks. Especially, we consider a practical edge-intelligent network with both communication and computation uncertainties, where channel state information (CSI) is partially obtained by the base station (BS) and the task complexity is inaccurately estimated by the mobile edge computing (MEC) server. To effectively mitigate the influences of these unfavorable uncertainties and guarantee user fairness, a task-driven robust design algorithm for integrated communication and computation with the objective of minimizing the maximum system delay among all UEs is put forward by jointly optimizing transmit power at the UEs, receive beamforming at the BS and computing resources at the MEC server based on task types. Both theoretical analysis and simulation results confirm the robustness and the effectiveness of the proposed algorithm for edge-intelligent networks.

In this paper, we investigate the issue of integrated localization and computing (ILAC) in edge-intelligent networks. By exploiting the dual-function radio frequency (RF) signals, we put forward a unified design framework for ILAC in edge-intelligent networks where the localization task and the computing task are conducted cooperatively by multiple user equipments (UEs) and multiple base stations (BSs). In particular, a joint resource allocation algorithm is proposed for ILAC by optimizing the available radio and computing resources with the goal of minimizing the weighted total energy consumption while ensuring the performance requirements of the localization task and the computing task. Finally, numerical results verify the effectiveness of the proposed algorithm over baseline ones.

Mass data traffics, low-latency wireless services and advanced artificial intelligence (AI) technologies have driven the emergence of a new paradigm for wireless networks, namely edge-intelligent networks, which are more efficient and flexible than traditional cloud-intelligent networks. Considering users’ privacy, model sharing-based federated learning (FL) that migrates model parameters but not private data from edge devices to a central cloud is particularly attractive for edge-intelligent networks. Due to multiple rounds of iterative updating of high-dimensional model parameters between base station (BS) and edge devices, the communication reliability is a critical issue of FL for edge-intelligent networks. We reveal the impacts of the errors generated during model broadcast and model aggregation via wireless channels caused by channel fading, interference and noise on the accuracy of FL, especially when there exists channel uncertainty. To alleviate the impacts, we propose a robust FL algorithm for edge-intelligent networks with channel uncertainty, which is formulated as a worst-case optimization problem with joint device selection and transceiver design. Finally, simulation results validate the robustness and effectiveness of the proposed algorithm.

The roll-out of various emerging wireless services has triggered the need for the sixth-generation (6G) wireless networks to provide functions of target sensing, intelligent computing and information communication over the same radio spectrum. In this paper, we provide a unified framework integrating sensing, computing, and communication to optimize limited system resource for 6G wireless networks. In particular, two typical joint beamforming design algorithms are derived based on multi-objective optimization problems (MOOP) with the goals of the weighted overall performance maximization and the total transmit power minimization, respectively. Extensive simulation results validate the effectiveness of the proposed algorithms. Moreover, the impacts of key system parameters are revealed to provide useful insights for the design of integrated sensing, computing, and communication (ISCC).

In this article, we investigate the issue of integrated sensing, computation and communication (SCC) in beyond fifth-generation (B5G) cellular internet of things (IoT) networks. According to the characteristics of B5G cellular IoT, a comprehensive design framework integrating SCC is put forward for massive IoT. For sensing, highly accurate sensed information at IoT devices are sent to the base station (BS) by using non-orthogonal communication over wireless multiple access channels. Meanwhile, for computation, a novel technique, namely over-the-air computation (AirComp), is adopted to substantially reduce the latency of massive data aggregation via exploiting the superposition property of wireless multiple access channels. To coordinate the co-channel interference for enhancing the overall performance of B5G cellular IoT integrating SCC, two joint beamforming design algorithms are proposed from the perspectives of the computation error minimization and the weighted sum-rate maximization, respectively. Finally, extensive simulation results validate the effectiveness of the proposed algorithms for B5G cellular IoT over the baseline ones.

In this paper, we design a multi-beam low earth orbit (LEO) satellite internet of things (IoT) for beyond fifth-generation (B5G) wireless networks. Rather than time division multiple access (TDMA), a new non-orthogonal multiple access (NOMA) scheme is adopted to support massive IoT over a very wide range. In order to reduce the power consumption of multi-beam satellite, a spot beam design algorithm is proposed with the goal of minimizing the total power consumption subject to quality-of-service (QoS) requirements. Furthermore, considering high computational complexity of spot beam design in the context of massive IoT, a simple multi-beam design algorithm is provided. Finally, simulation results confirm the effectiveness of the proposed algorithms over conventional ones.

In this paper, we investigate the issue of integrated computation and communication in beyond fifth-generation (B5G) cellular internet of things (IoT) networks with massive connectivity. By exploiting the open nature of wireless channels, a comprehensive deign framework integrating computation and communication over the same spectrum is first put forward for massive IoT. To achieve efficient integration of computation and communication under practical but adverse conditions, a robust algorithm is proposed by jointly optimizing transmit power and receive beamforming, with the goal of minimizing the computation error of computation signals while guaranteeing the requirement of communication signals. Finally, extensive simulations validate the robustness and effectiveness of the proposed algorithm for B5G cellular IoT.

In this article, we investigate the issue of massive access in a beyond fifth-generation (B5G) cellular Internet of Things (IoT) network. To reduce the overhead of channel state information acquisition and the complexity of transceiver design, an integrated framework of massive beam-division multiple access (BDMA) is proposed according to the characteristics of beamspace propagation. Then, we analyze the performance of the proposed massive BDMA scheme and derive a closed-form expression for the weighted sum rate in terms of channel conditions and system parameters. To improve the overall performance, we propose a massive access algorithm by jointly optimizing transmit power and receive vector. It is found that the optimal receive vector for each IoT device is the base beam corresponding to the arrival of angle of the IoT device's signal in the beamspace, which significantly simplifies the design of the receiver. Considering the constraint of radio-frequency (RF) chains in practical networks, we propose a clustering-based massive access algorithm, which allocates an RF chain for a cluster. Finally, extensive simulation results confirm that the proposed algorithms can provide small overhead and low complexity massive access schemes for B5G cellular IoT.

In beyond fifth-generation (B5G) cellular internet of things (IoT) networks, energy supply and data aggregation of a massive number of devices are two vitally challenging issues. To address these challenges, we propose a wireless powered MIMO over-the-air computation (AirComp) design framework. Firstly, wireless power transfer (WPT) is utilized to charge massive IoT devices simultaneously by exploiting the open nature of wireless broadcast channel. Then, AirComp is adopted to reduce latency of massive data aggregation via exploring the superposition property of wireless multiple-access channel. To realize efficient convergence of energy supply and data aggregation in practical IoT networks, a robust design algorithm is provided by jointly optimizing beamforming of both WPT and AirComp. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones.

In this paper, we investigate the issue of simultaneous wireless information and power transfer in the cellular Internet of Things (IoT) with a massive number of different access devices, e.g., information decoding devices, energy harvesting (EH) devices, and hybrid devices. Especially, we consider a practical scenario of the cellular IoT, where the IoT devices have a nonlinear EH receiver and perform imperfect successive interference cancellation (SIC) due to a limited capability. The benefits offered by a multiple-antenna base station are exploited to enhance the efficiency of both information transmission and power transfer. In particular, we propose to jointly optimize the spatial beam, transmit power, and power splitting ratio to alleviate the impacts of both nonlinear EH and imperfect SIC. To this end, two effective algorithms are designed from the perspectives of maximizing the weighted sum rate and minimizing the total power consumption, respectively. Finally, extensive simulation results are presented to validate the effectiveness of the proposed algorithms.

In this paper, we investigate the issue of sustainable communications for beyond fifth-generation (B5G) cellular internet of things (IoT) networks under adverse but practical conditions. A massive number of simple IoT devices without batteries harvest requisite energy from a part of the received signal. A design framework including channel state information (CSI) acquisition, signal construction, information decoding and energy harvesting, is first provided for sustainable communications of massive IoT. Then, based on the proposed design framework, we reveal the impacts of practically adverse factors, e.g., channel uncertainty, successive interference cancellation (SIC) and non-linear energy harvesting, on the performance of B5G cellular IoT. Furthermore, in order to effectively alleviate the impacts of these adverse factors, an outage-constrained robust algorithm is designed to maximize the overall performance of sustainable B5G cellular IoT. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones.