Low earth orbit Internet of things (LEO-IoT) networks offer global coverage and low-latency communication, making them attractive for large-scale IoT deployments. However, rapidly varying satellite connectivity and uneven, burst ground traffic lead to unstable routing performance, resulting in fluctuating delays and increased packet loss. To address these challenges, we propose a delay-aware routing optimization (DARO) algorithm that integrates traffic prediction and distributed control. A CNN-BiLSTM-Attention model is developed to capture spatial-temporal traffic patterns, enhancing the accuracy of dynamic traffic prediction. A closed-form end-to-end delay model is derived to characterize the effect of routing decisions on network latency. The routing problem is then formulated as a delay and packet loss minimization task and solved using multi-agent proximal policy optimization (MAPPO), enabling each satellite to adapt its routing strategy based on local observations and a shared critic. Simulation results show that DARO improves prediction accuracy by 19.05% to 76.39%, reduces packet loss by 27.35% to 90.76%, and lowers end-to-end delay by 2.43% to 56.04%, demonstrating its effectiveness in dynamic LEO-IoT environments.

To address the demand of digital twin (DT) model construction, we introduce the autonomous exploration framework for Internet of Drones (IoD) network, to autonomously construct models in the preset areas. However, current multi-UAV autonomous exploration frameworks suffer from low efficiency. To address this, we propose an innovative multi-UAV autonomous exploration framework. Using the LiDAR point cloud, our proposed framework firstly generates mesh frontiers based on the unit sphere point cloud flip, without building an occupancy grid map. Secondly, we design a multi-UAV exploration information interaction method, which can efficiently integrates the information obtained from various UAVs. Based on the information, a sparse topological graph is constructed to store the previous local feasible regions. A heuristic function is used to allocate the exploration targets for UAVs. The simulation results indicate that the proposed method achieves higher efficiency in frontier generation and improves exploration efficiency by 13.3-64.6% compared to the state-of-the-art methods.

With the growing demand for low-altitude transportation, the application of the Internet of drones (IoD) in urban logistics has become increasingly significant. However, the complex obstacles present in urban environments, such as tall buildings and no-fly zones, pose numerous challenges for the IoD, including low data-driven efficiency and difficulties in representing implicit knowledge. To address these challenges, this paper proposes an IoD swarm collaborative scheduling method based on distributed knowledge-enhanced multi-agent reinforcement learning (DKEMARL). The method leverages prior environmental knowledge to design a knowledge embedding and expansion module, which provides comprehensive and detailed environmental observation data during training and introduces a reward mechanism that balances task timeliness with flight safety. Within a centralized training and decentralized execution framework, the multi-agent deep deterministic policy gradient (MADDPG) approach is employed to facilitate efficient cooperation among the IoD. Specifically, we develop a scenario model for low-altitude transportation tasks involving an IoD swarm, considering factors such as task timeliness, flight distance cost, and safety constraints, and propose an optimization objective to balance timely task completion with flight safety. Simulation results demonstrate that the proposed DKEMARL algorithm can significantly enhance task completion efficiency compared to baseline methods that do not incorporate knowledge enhancement.

The extensive deployment of Internet of Things (IoT) devices across diverse industries has introduced substantial challenges in information collection, which exactly hinders its further advancement. By virtue of its flexibility and mobility, mobile crowdsensing (MCS), particularly drone-assisted mobile crowdsensing, is regarded as a new paradigm for addressing information collection problems in IoT environments. Nonetheless, owing to the inherent size and energy constraints of drones, planning their trajectories to efficiently perform the crowdsensing tasks from a large number of heterogeneous and spatiotemporally distributed IoT devices presents a significant problem. In this paper, we develop a multi-drone assisted crowdsensing social IoT (SIoT) system that integrates the social attributes of IoT devices and enables performing social community-oriented crowdsensing. Given the significance of information freshness in crowdsensing tasks, we jointly optimize the age of information (AoI) and drone energy consumption within the problem of multi-drone trajectory planning. We formulate the aforementioned problem as a decentralized partially observable Markov decision process (Dec-POMDP) and propose a deep-reinforcement-learning-based algorithm to address this problem. A series of experiments is conducted and the simulation results demonstrate the superiority and robustness of the proposed algorithm in effectively balancing the AoI of the whole SIoT system and energy consumption of drones during the crowdsensing tasks.

Abstract This paper addresses the resource allocation (RA) for ultra‐dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non‐convex and NP‐hard. Therefore, this paper considers and studies a semi‐distributed resource block (RB) allocation scheme, in order to achieve a well‐balanced trade‐off between performance and complexity. In the context of semi‐distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster‐based RB allocation. We first improve the K‐means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K‐means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster‐based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade‐off between the performance and the implementation complexity.

In this study, we design novel approximately mutually unbiased bases (AMUBs) sequences with arbitrary lengths and large family sizes for massive connection systems. Sequences with low correlations are highly demanded for many wireless communications systems, including Industrial Internet of Things (IIoT) systems for various applications. While many sets of sequences have been designed in the past decades, the requirement of large family size, i.e., the number of available sequences for massive connection systems has not yet been addressed. It is well known that mutually unbiased-based (MUB) sequences process desired correlation properties with large family sizes. However, the family size based on the current construction methods is limited by the length of the MUB sequences. In real applications, the longer length may lead to higher overhead and affect the overall transmission rate. This drawback makes MUB sequences have limited applications for industrial massive connection systems. In this article, we modified the original sequences generator of MUB from a quadratic polynomial to a cubic polynomial to further increase the family size. To generate AMUB sequences with arbitrary lengths, we then proposed a construction method based on the exponential sums over finite fields, and optimized the continuous peak-to-average power ratio (PAPR) of the proposed AMUB sequences for real applications. Given the dimension of MUB M, the proposed method can increase M times the number of available sequences. Meanwhile, the length restrictions in MUB sequence construction are removed. Theoretical cross-correlation (CC) bounds are provided and show low correlations of the proposed sequences. The low CC, PAPR, and increased family size of the proposed sequences are then verified by numerical results.

Federated learning (FL), utilizing data from the edge devices (EDs) while protecting user privacy has gained much attention. Its efficacy is substantially influenced by both the quantity of connected devices and the quality of wireless communications. Network congestion, resulting from multiple access and signal attenuation caused by physical obstacles may severely impact the convergence of the FL model. To address these issues, this article employs nonorthogonal multiple access (NOMA) for uplink transmission and designs a two-tier FL framework consisting of ground devices and unmanned aerial vehicles (UAVs) to ensure the construction of Line of Sight (LoS) channels from EDs to the base station. Moreover, we construct a multiobjective joint optimization problem to minimize the FL convergence time considering constraints, such as the NOMA uplink latency, ED selection strategy, local training latency, and energy consumption. We also deduce the theoretical upper bound of the convergence time and transform the proposed multiobjective problem into a solvable form by eliminating the discrete variables determined by the ED selection. In turn, we utilize the proximal policy optimization (PPO) algorithm to solve this optimization problem. Finally, the extensive experimental results demonstrate the advantages of our proposed algorithm in terms of latency and energy consumption, while yielding a high robustness and scalability.

In order to take the advantages of spatial modulation (SM) and support the multiple-access (MA) communications requiring high connectivity, we integrate the SM with DS-CDMA to propose a SM/DS-CDMA scheme, which is in favor of attaining both antenna diversity and frequency diversity. Associated with the SM/DS-CDMA scheme, a range of linear and nonlinear multiuser detectors (MUDs) are proposed and studied. Specifically, for linear MUDs, both minimum mean-square-error-aided joint spatial demodulation (MMSE-JSD) and MMSE-aided separate spatial demodulation (MMSE-SSD) are introduced. For nonlinear MUDs, we first present the joint maximum-likelihood MUD (JML-MUD) for demonstrating the potential of the SM-/DS-CDMA systems. Then, the MMSE-relied iterative interference cancellation (MMSE-IIC) is suggested, and the associated two types of low-complexity high-efficiency reliability measurement schemes are proposed. In this paper, we mathematically analyze both the single-user average bit error rate (ABER) of the SM/DS-CDMA systems employing joint spatial demodulation (JSD) and the approximate ABER of the SM/DS-CDMA systems employing the MMSE-JSD. We investigate and compare the ABER performance of the SM/DS-CDMA systems with various detection schemes and also with some legacy schemes. Our studies and performance results show that the SM/DS-CDMA systems in conjunction with appropriate MUD schemes are capable of providing a desirable tradeoff among the number of users supportable, implementation complexity, and the ABER performance.

In this paper, a multi-cell multiple-input multiple-output (MIMO) uplink system is considered. For uplink transmission, to implement decoding, the base station (BS) generally needs to estimate channels with the aid of training sequences. Obviously, for full frequency reuse systems, training also suffers from inter-cell interference, namely pilot contamination. In this paper, the impact of pilot contamination on the performance of the considered system is studied based on the maximum likelihood (ML) decoder. Specifically, an exact analytical expression of pair-wise error probability (PEP) is derived, and then, a lower bound and an upper bound of PEP are given, to explicitly show the impact of pilot contamination. With the detailed analysis of these bounds, we can discover: (1) pilot contamination can result in error floor, provided that the number of BSs in the considered system is larger than the length of a frame; and (2) if the length of a frame is larger than or equal to the number of BSs, and an appropriate coding scheme is applied, error floor can be removed. Finally, a coding design criterion is proposed. Based on this criterion, it can be shown that the considered system can achieve the same diversity to what can be achieved by a single isolated cell system.

In network multiple‐input–multiple‐output (MIMO) systems, cooperative base stations (BSs) and mobile terminals (MTs) are in general at different geographic locations. Correspondingly, the channel gains with respect to different BSs and/or MTs may obey different distributions, which resulted from, different pathlosses, different strength of line‐of‐sight (LOS) components and so on. Furthermore, future wireless communication systems are expected to be equipped with multiple antennas for transmission/receiving. In these multiantenna systems, signals received by the antennas of one BS from one MT may be correlated. Against the above‐mentioned scenarios, in this contribution, the authors study the capacity of the generalised uplink network MIMO systems with multicell cooperation (MCoP), when propagation pathlosses, fast Rician fading with various LOS components and spatial correlation are simultaneously considered. Specifically, the theory of operator‐valued free probability is introduced to derive the approximate eigenvalue distribution (AED) of the correlation matrix of the equivalent channels. Based on the AED, the approximate capacity of uplink network MIMO systems is then studied. Furthermore, a range of special cases are analysed by specialise the authors model and modifying their results. Finally, both numerical and simulation results are provided for characterising the capacity of network MIMO systems with MCoP.

In this contribution, we analyze the asymptotic spectral-efficiency (ASE) of multiuser MIMO-CDMA systems, when assuming communications over flat fading channels with arbitrary spatial correlation. Our analysis is built on the operator-valued free probability theory, which is applied to obtain the limit distribution of the correlation matrix's eigenvalues, as the MIMO-CDMA systems' size tends to infinity. The spectral-efficiency (SE) performance of the MIMO-CDMA systems is investigated via both analysis and simulations. Our simulation and numerical results show that the ASE is capable of providing a good measure of the SE achieved by the corresponding realistic MIMO-CDMA systems.

In this contribution the asymptotic signal-to-interference-plus-noise ratio (SINR) performance of multicarrier direct-sequence code-division multiple-access systems employing time-frequency-domain spreading, i.e., of the TF/MC DS-CDMA systems, is studied, when separate minimum mean-square error multiuser detection (MMSE-MUD) is considered. The separate MMSE-MUD detects signals first in the time (T)-domain and then in the frequency (F)-domain. Based on random matrix theory, closed-form expressions for the asymptotic SINR of the TF/MC DS-CDMA systems using separate MMSE-MUD is derived, when communicating over additive white Gaussian noise (AWGN) channels. The closed-form expressions show that the asymptotic SINR performance is only depended on the T- and F-domain user load factors as well as noise variance. Hence, they are beneficial to evaluation. Furthermore, our simulation and numerical results show that in most cases the asymptotic SINR can provide a good approximation to the SINR achieved by realistic TF/MC DS-CDMA systems employing separate MMSE-MUD.

In this contribution we study the spectral-efficiency performance of a multicarrier direct-sequence code-division multiple-access system using both time (T)-domain and frequency (F)-domain spreading, which is referred to as TF/MC DS-CDMA for convenience, when communicating over Nakagami-m fading channels. We consider the TF/MC DS-CDMA scheme, since it is a generalized multiple-access scheme that can be readily configured to some other CDMA schemes, so that we can conveniently compare their spectral-efficiency performance. Furthermore, the Nakagami-m fading channel model makes it possible to study the impact of channel quality on the achievable spectral-efficiency. In this contribution, the spectral-efficiency of the TF/MC DS-CDMA is investigated, when various single-user and multiuser detection schemes are invoked, which include optimum detection, minimum mean-square error (MMSE) detection, zero-forcing (ZF) detection and the matched-filter (MF) based detection. One of our conclusions is that channel quality has insignificant impact on the achievable spectral-efficiency of the TF/MC DS-CDMA, when the number of T-domain resolvable paths of the frequency-selective fading channels is sufficiently high.