In scenarios with extremely harsh channel conditions and severely limited communication resources, the reliability and effectiveness of semantic communication require urgent enhancement to satisfy the increasing demands of 6G technology. To address this issue, we propose an importance prioritized framework that integrates both message importance and feature importance to identify critical semantics for reliable and efficient semantic transmission. Considering service personalization and task intelligence, we analyze the message importance by factoring the receiver’s preferences and the communication tasks requirements. Specifically, a large AI model is introduced to quantify message importance, while an importance-based metric for semantic accuracy is established to evaluate the overall reliability of semantic communication. To safeguard significant messages in harsh channel conditions, an unequal error protection strategy based on message importance is employed. Furthermore, we propose a novel approach for feature importance analysis based on loss variation to accurately identify critical features. A feature importance prediction network is designed for algorithm deployment. Additionally, a semantic compression strategy based on feature importance is utilized to prioritize the transmission of essential features in limited communication resources scenarios. Extensive experimental results demonstrate substantial performance advantages of our framework and methods, especially in low signal-to-noise ratio and communication resource shortages, providing a reliable and efficient solution for semantic communication in adverse communication environments.

Robust speaker verification under noisy conditions remains an open challenge. Conventional deep learning methods learn a robust unified speaker representation space against diverse background noise and achieve significant improvement. In contrast, this paper presents a noise-conditioned mixture-of-experts framework that decomposes the feature space into specialized noise-aware subspaces for speaker verification. Specifically, we propose a noise-conditioned expert routing mechanism, a universal model based expert specialization strategy, and an SNR-decaying curriculum learning protocol, collectively improving model robustness and generalization under diverse noise conditions. The proposed method can automatically route inputs to expert networks based on noise information derived from the inputs, where each expert targets distinct noise characteristics while preserving speaker identity information. Comprehensive experiments demonstrate consistent superiority over baselines.

The channel nonlinearity and high-speed mobility in low-earth orbit (LEO) constellation communication are key bottlenecks that constrain the performance of waveform transmission. To address these challenges, we propose a universal continuous phase modulation (CPM) spread-spectrum communication method with high spectral efficiency, and a Doppler-insensitive signal detection mechanism. First, an enhanced dual-phase CPM spread-spectrum (DP-CPM-SS) waveform is investigated. By analyzing the principles and power spectrum characteristics of DP-CPM-SS, we correct the modulation index and design the optimalWiener filtering reception for CPM, improving the spectral efficiency and noise resilience. Subsequently, a CPM noncoherent detection integrating frequency estimation and phase pre-compensation is developed. The performance lower bound of the partial matched filter-fast fourier transform (PMF-FFT) frequency estimation algorithm is derived, revealing the relationship among the length and number of partial matched filters, the normalized frequency offset and the mean square error (MSE) of frequency estimation. Additionally, error probability performance and frequency offset adaptation range are analyzed. Numerical results show that the proposed method exhibits superior and stable performance under severe Doppler effects, which is a promising scheme for LEO constellation communication.

By focusing on the intrinsic meaning of information, semantic communication (SemCom) marks a fundamental paradigm shift from physical bit transmission to personalized semantic service. Considering the importance of personalized features related to the speaker in speech for source recovery and understanding, we propose a semantic-driven framework for personalized speech transmission, named PerSemCom, which combines speaker acoustic features with semantic information. Specifically, we first introduce an efficient semantic extraction mechanism to achieve the conversion from speech to text transcriptions, and design a semantic corrector coupled with multi-domain knowledge to mitigate the effects of wireless channel distortion. Building upon the reliable transcriptions at receiver, we further establish a speaker embedding vector knowledge base and achieve high-fidelity speech reconstruction through quantitative modeling of speaker-specific acoustic features. Extensive experimental results demonstrate that our proposed framework outperforms existing schemes in terms of subjective perception at harsh channel conditions. Complexity analysis and latency measurements also show competitive advantages in computational efficiency and real-time capabilities. Reconstructed personalized speech samples have been publicly available at https://kwtankw.github.io/PerSemCom/.

Unmanned aerial vehicles (UAVs) acting as aerial base stations are regarded as an effective solution for emergency communication, due to their high mobility and low cost. In this paper, we consider the differentiated communication requirements in disaster relief scenarios, where the uplink demands high throughput and the downlink requires low latency and high reliability. To simultaneously capture the timeliness and reliability requirements of downlink transmissions, we propose a novel QoS evaluation metric based on finite blocklength theory. Building on this, we formulate a system utility maximization problem and solve it by jointly optimizing UAV trajectory and ground node access control. To address this problem, we propose a novel algorithm named PW-QMIX, which integrates a priority-based heuristic access control policy with a QMIX-based trajectory optimization scheme, effectively tackling the challenges posed by the high-dimensional joint action space. Furthermore, to tackle the challenge of dynamic observation caused by UAV movement and sensing limitations, we design a weight generation network (WGN) and incorporate it into the input layer of QMIX. The WGN dynamically generates weight matrices based on observed nodes, enhancing UAV’s adaptability to local observation variations. Simulation results validate the significance of jointly optimizing uplink and downlink communications. Moreover, compared with state-of-the-art algorithms, the proposed PW-QMIX demonstrates significant advantages in convergence and scalability.

Semantic communication (SemCom) is emerging as a key technology for efficient and robust data transmission in future networks. To improve its reliability, the integration of hybrid automatic repeat request (HARQ) has garnered increasing attention. However, current semantic HARQ methods often rely on redundant retransmissions and do not effectively utilize past semantic and channel information, leading to inefficient resource usage. In this paper, we propose a semantic communication framework that incorporates cross-training feedback integration. This system dynamically fuses semantic features and channel states from previous transmissions to optimize the current encoding process. At the transmitter, a fusion module utilizes the semantic representation from the previous decoding to refine the current transmission. At the receiver, the channel buffer and the semantic combiner work together to progressively integrate features, thereby improving the reliability of semantic decoding. Extensive experiments on image transmission under various channel conditions and bandwidth settings demonstrate that the proposed method achieves significant performance gains.

Most existing deep learning-based path loss (PL) models rely on measurements in a given frequency range and specific scenarios, making it difficult to balance accuracy and generalization. The emergence of pre-trained language model (PLM) provides a solution to this challenge. In this paper, we integrate prior knowledge, engineering data, and environmental features into PLM for multi-modal feature extraction and fusion, and use the Low-Rank Adaptation (LoRA) for lightweight fine-tuning to achieve cross-modal knowledge transfer. Moreover, in our model, a residual structure is introduced to bridge the gap between the statistical model and the measurement data. The experimental results show that the proposed model achieves a prediction error [root mean square error (RMSE)] of 3.5 ± 0.2 dB compared to the 7.1 ± 1.5 dB for 3GPP statistical model prediction, and the pearson correlation coefficient of 0.88. When extrapolated to new scenarios, the proposed model has excellent few-shot performance.

Adopting federated learning (FL) in unmanned aerial vehicle (UAV) networks is a promising paradigm, which can empower UAV networks with enhanced intelligence to support complex applications. Considering the imbalanced data properties, limited energy and unstable wireless connection of UAVs, an effective client scheduling scheme is critical for the design of efficient FL. In this paper, in order to properly consider the priority criteria, we first propose two importance metrics from the perspectives of data attributes and local updates, namely data importance measurement (DIM) and gradient importance measurement (GIM). Then, take into account DIM and GIM, an optimization problem is formulated to jointly optimize the client scheduling, computation and communication of UAVs. Due to the non-convex nature of this problem, we decompose it into two sub-problems and derive their optimal closed-form solutions. Simulations demonstrate that, compared to benchmark schemes, our proposal ensures better performance on test accuracy, convergence and energy saving.

Unmanned aerial vehicles (UAVs) significantly augment mobile edge computing (MEC) networks with their flexible deployment. In this paper, we investigate a priority-driven multi-UAV cooperative MEC system, in which the task priority are jointly determined by the task queue and task type. The system aims to maximize the task priority gain, subject to the constraints on offloading decision, UAV trajectory design and task scheduling. To solve this problem, we develop a priority scheduling insert based heterogeneous Q-mixing networks (PSI-HQMIX) framework, where the PSI scheme dynamically updates the position of tasks within the queues and the HQMIX algorithm is used to obtain the optimal offloading decisions and trajectories. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of the achieved average priority gains and convergence.

Semantic and task-oriented communications have emerged as significant paradigm shifts for next-generation communication networks, which extracts and transmits task-relevant information rather than raw data for downstream tasks. However, most existing work focused on bit-level loss functions, such as mean square error (MSE) and cross-entropy (CE), rather than directly optimizing at the semantic level. These approaches often lack interpretability of semantic representation and result in higher system complexity and less efficient transmission for various tasks. To this end, we develop a novel task-oriented semantic communication system for multitask scenarios and further develop a semantic-level framework. This framework can extract structured semantic representation by compressing raw data with different labels into mutually orthogonal subspaces. Simulation results demonstrate that the proposed framework not only extracts structured semantic representation, but also outperforms existing benchmarks in terms of data recovery and AI inference performance.

Unmanned aerial vehicles (UAVs) are recognized as effective data collectors for wireless sensor networks. The Age of Information (AoI), a metric indicating data freshness, is crucial for decision making in time-sensitive applications. It can be significantly reduced by jointly optimizing UAV trajectories and communication scheduling of sensor nodes (SNs). However, rapid changes in the environment make it challenging to predesign UAV trajectories and communication scheduling decisions using traditional methods, especially when central controllers are absent and the numbers of UAVs and SNs vary. In this article, we propose hypernetwork-based QMIX (HyperQMIX), a population-invariant multiagent deep reinforcement learning (MADRL) algorithm capable of transferring policies across tasks with varying population sizes. First, we design neural network modules adaptable to varying input and output dimensions, facilitated by parameter generation through a hypernetwork. Then, HyperQMIX leverages these modules to process fluctuations in state and action dimensions. This approach ensures that the network structure remains consistent regardless of population sizes, thereby enhancing the algorithm’s scalability. Extensive simulations demonstrate that HyperQMIX significantly outperforms state-of-the-art algorithms in terms of learning efficiency and converged performance. Moreover, agents pretrained with HyperQMIX perform well in tasks of different population sizes without additional training. Fine-tuning these models achieves performance comparable to training from scratch.

Most AI-based channel estimation methods with static environment assumptions suffer from performance degradation due to the distribution shift caused by the varying channel environment. As one of the solutions, transfer learning also faces the problem of catastrophic forgetting, where the model tents to fail in previous estimation tasks after learning from new ones. In this paper, we propose a continuous learning-based channel estimation (CLCE) scheme that integrates a series of subnetworks to preserve historical knowledge and achieve an ongoing process of self-improvement. To determine whether the wireless environment is previously unobserved, we first propose a distance-based unsupervised out-of-distribution (OOD) detection algorithm to perceive the distribution shift of the channel environment. The OOD detection algorithm is developed based on the representation of channel data in the latent space of a variational autoencoder (VAE), which is designed to infer the latent variable that implies the characteristics of the wireless environment. Then, a new channel estimation subnetwork is initiated with meta-learning to adapt to the dynamic channel environments with a small set of OOD channel data. Simulation results reveal that our proposed scheme can accurately detect unobserved channel environments without introducing additional detection network, and efficiently adapt to them with few online samples. Furthermore, the mean square error (MSE) result of the channel estimations across multiple environments demonstrates that CLCE effectively mitigates catastrophic forgetting and outperforms the competitors.

Mixed-numerology transmission still suffers from a large peak-to-average power ratio (PAPR) and the conventional PAPR reduction methods cannot be applied straightforwardly due to its multiple baseband processing units. In this paper, we develop a novel parallel PAPR reduction approach for the decentralized baseband processing architecture. By considering the in-band distortion minimization problem subject to the PAPR constraint, we find that this problem is separable in both the objective function and the constraints. Based on the “decomposition-coordination” mode of alternating direction method of multipliers (ADMM), original problem can be divided into several subproblems which can be easily solved in each subbands with its dedicated numerology. Because of the independence between each subbands, the proximate Jacobian method is also applied so that the subproblems can be updated parallelly and are suitable to the decentralized baseband processing architecture. Analysis corroborated by simulations demonstrate that the proposed approach is convergent. Numerical results illustrate that the proposed approach is less time-consuming than existing benchmark when the same PAPR reduction performance is achieved.

As a potential technique, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) can provide flexible coverage and computing services for real-time applications such as emergency search, traffic control and disaster rescue. In this paper, we investigate a freshness sensitive multi-UAV assisted MEC system where tasks arrive stochastically. The system aims to minimize the age of information (AoI), subject to the constraints on computation offloading, trajectory control and communication resource allocation. Due to the dynamic environment and the coupling of variables, we develop a multi-agent reinforcement learning (MARL) scheme, in which a federated updating method is introduced. Through our scheme, smart mobile devices, UAVs and cloud center can collaborate to learn interactive policies. Simulation results validate that our scheme outperforms local computing, remote computing, and centralized solutions in terms of both the average AoI and convergence.

The radio map has emerged as a promising tool for optimizing spectrum resource utilization and shaping the future landscape of intelligent wireless networks. However, the deployment of radio maps across the network introduces computational and latency challenges, restricting their real-time applications from the user’s perspective. In this paper, we introduce an innovative scheme for constructing and distributing radio maps in unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks. Initially, we transform the distribution of radio maps into a collaborative process between UAV server and smart mobile devices (SMD), proposing four distribution modes tailored to different network conditions. This ensures that each SMD can access radio maps with the lowest cost. Additionally, our scheme integrates a deep reinforcement learning (DRL) framework, fostering seamless coordination between UAV server and SMD to enhance overall system performance and operational efficiency. Simulation results validate the efficiency and efficacy of our proposed scheme in optimizing radio map distribution strategies and resource allocation, further confirming the potential real-time applications of radio maps in future wireless networks.

The 6G wireless communication demands intelligent and versatile interaction between humans and machines that can deal with various intelligent tasks. Semantic communication that focuses on transmitting the meanings rather than the data is expected to be one of the promising technologies to achieve this goal. However, most existing semantic communication systems optimize the whole system under a single objective, lacking the scalabi-lity to dynamic scenarios. For a dynamic scenario with changing channel conditions and background knowledge, we propose a layered semantic communication system (LSCS), which takes advantage of layered coding architec-ture at the semantic and syntactic levels. In addition, a symbolic attention-based denoising network is designed at the receiver to recover transmitted meanings. Simulation results demonstrate that the proposed LSCS can adapt to dynamic scenarios and achieve superior performance over benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) region.

Unmanned aerial vehicles (UAVs) as mobile base stations are recognized as effective means for emergency communications. The performance of such systems depends on the movement of UAVs and scheduling of ground users (GUs). However, devising an efficient algorithm to jointly optimize UAV trajectories and user scheduling is still challenging, especially in real-time scenarios lacking central controllers. Multi-agent deep reinforcement learning (MADRL) provides a promising solution to this problem. Nevertheless, as the numbers of UAVs and GUs increase, existing MADRL algorithms encounter scalability and sample efficiency issues. In this paper, we develop a novel symmetry-augmented MADRL approach for learning scalable UAV trajectory design and user scheduling policies. The core idea is to utilize symmetries to reduce the multi-agent state-action space and enhance sample efficiency. Specifically, we design a family of neural networks to learn individual policies, namely entity permutation equivariant policy networks (EP2Nets). EP2Nets effectively leverage the permutation symmetry to reduce redundancy in the state-action space. Additionally, we achieve data augmentation by exploiting rotational and reflection symmetries, further boosting sample efficiency. Finally, a Symmetric QMIX (SymmQMIX) algorithm is proposed by integrating the EP2Net and data augmentation method into the QMIX algorithm. Simulation results indicate that SymmQMIX significantly outperforms QMIX and other symmetry-enhanced algorithms, achieving a 4.5-fold increase in converged performance and a 100-fold improvement in sample efficiency.

Non-orthogonal multiple access (NOMA) with mixed numerology is a promising technology that blends flexibility and high spectral efficiency. However, since NOMA enables multiplexing of the same time-frequency resources for different users and mixed-numerology allows superimposing sub-signals with different numerologies, high peak-to-average power ratio (PAPR) as well as power fluctuation problem in NOMA detection becomes cumbersome especially when applying PAPR reduction techniques. This study considers minimizing PAPR in mixed numerology NOMA systems using selected-mapping (SLM) method. The Riemann sequence is one of the simplest phase sequence that can be used to generate a set of signal copies for PAPR reduction. Our analysis reveals that as the amplitude variation caused by the Riemann sequence grows, the upper bound of PAPR for signal copies decreases correspondingly. Leveraging this insight, we introduce a new maximum-range Riemann (MRR)-based phase sequences, in which the amplitude factor can be adjusted to control the power fluctuations. Compared to previous works, our study delves deeper into the influence of phase sequence design of SLM on PAPR reduction performance. Simulations show that the proposed method offers significant performance advantages of PAPR reduction and bit error rate (BER) improvement even with consideration of power-amplifier.

Unmanned aerial vehicles as base stations (UAV-BSs) are recognized as effective means for tackling eruptive communication service requirements especially when terrestrial infrastructures are unavailable. Quality of service (QoS) received by ground terminals (GTs) highly depends on the spatial movement of UAV-BSs. In this paper, we investigate the cooperative trajectory design problem of multiple UAV-BSs towards fair throughput maximization of GTs. Considering the restriction of coverage and sensing, we first propose a heterogeneous-graph-based formulation of relations between GTs and UAV-BSs. Subsequently, we design a framework named graph vision and communication (GVis&Comm) to 1) let each UAV-BS efficiently manage time-varying local observations; 2) facilitate cooperation between UAV-BSs through explicit information exchange. To further reduce the overhead of over-the-air cooperation, we realize discretization of the message passing process among UAV-BSs while still enabling end-to-end training. By leveraging multi-agent reinforcement learning (MARL), UAV-BSs as agents learn a distributed trajectory design policy. Extensive numerical simulation shows that our framework on the one hand achieves remarkable efficiency in processing local observations of each UAV-BS, and on the other improves the overall network performance via close cooperation among UAV-BSs.

Some Internet of Things (IoT) applications represented by vehicular networks, Internet of Medical Things (IoMT), and fire alarm systems have high requirements on the freshness of receiving information. Due to limited computing capability of IoT devices, mobile edge computing (MEC) is applied to reduce packet calculation time and improve packet freshness. In this paper, we investigate a MEC system for sharing vehicle status information and use the age-of-information (AoI) to define the freshness of information in the MEC system. The whole system is modeled as a two-stage tandem queue model with multi source-destination pairs. We derive the closed-form expression for the average AoI of partial computing and analyze the impact of system parameters on the average AoI, which provides guidance on how to set parameters to maximize the information freshness of the MEC system. As a more flexible scheme, partial computing we used reduces the AoI of the MEC system compared to remote computing. Numerical analysis validates our theory.

Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems. However, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques and their cost in terms of computational complexity. We further investigate and also experimentally validate how information is flown in a DNN-based communication system under the information theoretic concepts.

With the development of wireless communication and Internet of Things (IoT), there are massive wireless devices that need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this article, we investigate the distributed DSA problem for multiusers in a typical multichannel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we propose a centralized off-line training and distributed online execution framework based on cooperative multi-agent reinforcement learning (MARL). We employ the deep recurrent$Q$-network (DRQN) to address the partial observability of the state for each cognitive user. The ultimate goal is to learn a cooperative strategy which maximizes the sum throughput of a cognitive radio network in a distributed fashion without information exchange between cognitive users. Finally, we validate the proposed algorithm in various settings through extensive experiments. The experimental results show that the proposed CoMARL-DSA algorithm outperforms the state-of-the-art deep$Q$-learning for spectrum access (DQSA) in terms of successful access rate and collision rate by at least 14% and 12%, respectively.

Recently, deep neural network (DNN)-based physical layer communication techniques have attracted considerable interest. Although their potential to enhance communication systems and superb performance have been validated by simulation experiments, little attention has been paid to the theoretical analysis. Specifically, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity. To achieve this goal, we first analyze the encoding performance of a DNN-based transmitter and compare it to a traditional one. And then, we theoretically analyze the performance of DNN-based estimator and compare it with traditional estimators. Third, we investigate and validate how information is flown in a DNN-based communication system under the information theoretic concepts. Our analysis develops a concise way to open the “black box” of DNNs in physical layer communication, which can be applied to support the design of DNN-based intelligent communication techniques and help to provide explainable performance assessment.

With innovative applications of unmanned aerial vehicle (UAV) ad hoc networks in various areas, their demands on broad bandwidth, large capacity and low latency become prominent. The combination of millimeter wave, directional antenna and time division multiple access techniques, which enables concurrent transmissions, is promising to deal with it. In this paper, we study the resource allocation problem in UAV ad hoc networks. Specifically, the slot assignment, antenna boresight and transmit power are jointly optimized to promote the network capacity. First, we formulate the optimization problem as the maximization of the fairness-weighted network capacity, subject to the constraint on priority guarantee. Then, because the formulated problem is a mixed integer non-linear programming problem (MINLP), which is NP-hard, two algorithms called dual-based iterative search algorithm (DISA) and sequential exhausted allocation algorithm (SEAA) are respectively proposed to efficiently solve it with acceptable complexity. DISA slacks the MINLP into a continuous-variable optimization problem and solves it with the Lagrangian dual method in an iterative manner. As a heuristic method, SEAA schedules links sequentially, i.e., from high-priority to low-priority ones. Numerical results demonstrate that both DISA and SEAA can efficiently allocate resources for UAVs, while guaranteeing the fairness and priority of links.

Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks. Although superb performance is achieved on small and simple networks, most existing ML-based approaches are confronted with difficulties when heterogeneity occurs and network size expands. In this paper, specifically focusing on power control/beamforming (PC/BF) in heterogeneous device-to-device (D2D) networks, we propose a novel unsupervised learning-based framework named heterogeneous interference graph neural network (HIGNN) to handle these challenges. First, we characterize diversified link features and interference relations with heterogeneous graphs. Then, HIGNN is proposed to empower each link to obtain its individual transmission scheme after limited information exchange with neighboring links. It is noteworthy that HIGNN is scalable to wireless networks of growing sizes with robust performance after trained on small-sized networks. Numerical results show that compared with state-of-the-art benchmarks, HIGNN achieves much higher execution efficiency while providing strong performance.

Devices in the Internet of Things (IoT) are usually limited in computing resources and energy capacity, which means that encryption schemes with higher complexity are not suitable for them to ensure secure communication. As a promising solution to this problem, physical layer key generation suggests that shared secret keys can be generated from noisy wireless channel measurements to enhance the security of wireless communications. In this article, we propose a key generation scheme with extremely low implementation complexity, which allows physical layer key generation to be implemented on IoT nodes. First, we preprocess the channel measurements with simple moving average filtering before quantization to improve channel reciprocity. Next, a bidirectional difference quantization scheme is proposed to realize reliable quantization of channel measurements, which is ingenious in that the quantization process does not depend on quantization thresholds, and thus, the mismatched key bits caused by measurements close to quantization thresholds can be effectively avoided. Then, we propose an improved Cascade protocol to achieve lightweight and efficient information reconciliation. The simulation results show that our scheme can well balance the reliability and efficiency of key generation, and has excellent performance in terms of implementation complexity and key randomness.

The heterogeneous cloud radio access network (H‐CRAN) is considered a promising solution to expand the coverage and capacity required by fifth‐generation (5G) networks. UAV, also known as wireless aerial platforms, can be employed to improve both the network coverage and capacity. In this paper, we integrate small drone cells into a H‐CRAN. However, new complications and challenges, including 3D drone deployment, user association, admission control, and power allocation, emerge. In order to address these issues, we formulate the problem by maximizing the network throughput through jointly optimizing UAV 3D positions, user association, admission control, and power allocation in H‐CRAN networks. However, the formulated problem is a mixed integer nonlinear problem (MINLP), which is NP‐hard. In this regard, we propose an algorithm that combines the genetic convex optimization algorithm (GCOA) and particle swarm optimization (PSO) approach to obtain an accurate solution. Simulation results validate the feasibility of our proposed algorithm, and it outperforms the traditional genetic and K‐means algorithms.

Due to the flexibility in 3-D space and high probability of line-of-sight (LoS) in air-to-ground communications, unmanned aerial vehicles (UAVs) have been considered as means to support energy-efficient data collection. However, in emergency applications, the mission completion time should be main concerns. In this article, we propose a UAV-aided data collection design to gather data from a number of ground users (GUs). The objective is to optimize the UAV’s trajectory, altitude, velocity, and data links with GUs to minimize the total mission time. However, the difficulty lies in that the formulated time minimization problem has mutual effect with trajectory variables. To tackle this issue, we first transform the original problem equivalently to the trajectory length problem and then decompose the problem into three subproblems: 1) altitude optimization; 2) trajectory optimization; and 3) velocity and link scheduling optimization. In the altitude optimization, the aim is to maximize the transmission region of GUs which can benefit trajectory designing; then, in the trajectory optimization, we propose a segment-based trajectory optimization algorithm (STOA) to avoid repeat travel; besides, we also propose a group-based trajectory optimization algorithm (GTOA) in large-scale high-density GU deployment to relieve massive computation introduced by STOA. Then, the velocity and link scheduling optimization is modeled as a mixed-integer nonlinear programming (MINLP) and block coordinate descent (BCD) is employed to solve it. Simulations show that both STOA and GTOA achieve shorter trajectory compared with the existing algorithm and GTOA has less computational complexity; besides, the proposed time minimization design is valid by comparing to the benchmark scheme.

With the emergence of Internet of Things (IoT), where any device is able to connect to the Internet and monitor/control physical elements, several applications were made possible, such as smart cities, smart health care, and smart transportation. The wide range of the requirements of these applications drives traditional IoT to cognitive IoT (CIoT) that supports smart resource allocation, automatic network operation and intelligent service provisioning. To enable CIoT, there is a need for flexible and reliable wireless communication. In this paper, we propose to combine cognitive radio (CR) with a biological mechanism called reaction–diffusion to provide efficient spectrum allocation for CIoT. We first formulate the quantization of qualitative connectivity-flexibility tradeoff problem to determine the optimal cluster size (i.e., number of cluster members) that maximizes clustered throughput but minimizes communication delay. Then, we propose a bio-inspired algorithm which is used by CIoT devices to form cluster distributedly. We compute the optimal values of the algorithm’s parameters (e.g., contention window) of the proposed algorithm to increase the network’s adaption to different scenarios (e.g., spectrum homogeneity and heterogeneity) and to decrease convergence time, communication overhead, and computation complexity. We conduct a theoretical analysis to validate the correctness and effectiveness of proposed bio-inspired algorithm. Simulation results show that the proposed algorithm can achieve excellent clustering performance in different scenarios.

Exploiting unmanned aerial vehicles (UAVs) as flying base stations (BSs) to assist the terrestrial cellular networks is promising in 5G and beyond. Despite the inherent potentials, one challenging problem is how to optimally deploy multiple UAVs to achieve on-demand coverage for ground user equipment (UE). In this article, we model the deployment problem as minimizing the number of UAVs and maximizing the load balance among them, which is subject to two main constraints, i.e., UAVs should form a robust backbone network and they should keep connected with the fixed BSs. To solve this optimization problem with low complexity, we decompose the problem into two subproblems and propose a hybrid algorithm to solve them stepwise. First, a centralized greedy search algorithm is used to heuristically obtain the minimum number of UAVs and their suboptimal positions in a discontinuous space. Then, a distributed motion algorithm is adopted which enables each UAV to autonomously control its motion toward the optimal position in a continuous space. The proposed algorithm is applicable to various scenarios where UAVs are deployed alone or with fixed BSs regardless of the UE distribution. Extensive simulations validate the proposed algorithm.

Unmanned aerial vehicle (UAV) has been witnessed as a promising approach for offering extensive coverage and additional computation capability to smart mobile devices (SMDs), especially in the scenario without available infrastructures. In this paper, a UAV-assisted mobile edge computing system with stochastic computation tasks is investigated. The system aims to minimize the average weighted energy consumption of SMDs and the UAV, subject to the constraints on computation offloading, resource allocation, and flying trajectory scheduling of the UAV. Due to nonconvexity of the problem and the time coupling of variables, a Lyapunov-based approach is applied to analyze the task queue, and the energy consumption minimization problem is decomposed into three manageable subproblems. Furthermore, a joint optimization algorithm is proposed to iteratively solve the problem. Simulation results demonstrate that the system performance obtained by the proposed scheme can outperform the benchmark schemes, and the optimal parameter selections are concluded in the experimental discussion.

In channel-hopping (CH)-based distributed cognitive radio networks (CRNs), the time duration that secondary users (SUs) spend for establishing communication links is called access delay. To evaluate access delay, we propose an access delay model by jointly considering imperfect spectrum sensing and multi-channel multi-SU transmission, from the cross-layer perspective. The model considers two typical scenarios. The first scenario assumes that the SUs do not use contention scheme (CS) which indicates that the time slot is relatively shorter to just allow a transmission. The second scenario assumes that the SUs employ CS [i.e., modified Distributed Coordination Function (DCF)-based Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) in this paper], which indicates that the time slot is long enough to regulate multiple transmissions. We then propose a bio-inspired algorithm for the first scenario and a self-adaptive step-length algorithm for the second scenario to search for the optimal values of spectrum sensing parameters. The theoretical analysis and simulation results validate the proposed access delay model and show that the proposed algorithms can reduce the most redundant computation. They also show that the optimization of cross-layer parameters can significantly decrease SUs' access delay. Moreover, we conduct a cost-benefit analysis to evaluate the performance of the two scenarios.

The distributed mobile robotic network consists of a group of mobile nodes, such as mobile sensors, unmanned vehicles, unmanned submarines, unmanned air vehicles, or mobile robots. The mobile robotic network keeping a regular topology can utilize efficient network protocols and is also promising in many application scenarios. We propose a distributed algorithm that controls multiple distributed robotic nodes to form regular topology, including straight line, ring, triangular lattice, and square lattice. Our algorithm generates artificial forces, including the attractive force towards a reference point to gather the distributed nodes, the repulsive force from neighboring nodes to keep the desirable distance among them, the formation force to form a specific shape, and the obstacle avoidance force to avoid possible obstacles, such that each node simply follows the resultant force to move. The algorithm works in a fully distributed manner, converges fast, and is easy to deploy, requiring only one-hop local network geometry information. And, it is effective under both 2D and 3D scenarios. A computer demo is developed to demonstrate the effectiveness of the algorithm for large numbers of robotic nodes.

In this paper, we propose an optimization approach on access delay by jointly modeling imperfect spectrum sensing and multi-channel multi-secondary user (SU) access contention, from a cross-layer perspective, for channel-hopping (CH) based cognitive radio networks (CRNs). Specifically, we first employ an absorbing Markov chain to model the multi-SU access contention process by combining the impacts of spectrum sensing and rendezvous; furthermore, we propose a methodology to derive the enclosed expression of rendezvous probability by analyzing the regularity and the periodicity of a CH rendezvous algorithm. We further analyze the access delay model and compute the optimal parameter combination (e.g., sensing duration and access contention duration) to build a communication link within minimum time duration Theoretical analysis and simulation results validate the proposed optimization approach and show that the optimization of cross-layer parameters can significantly decrease SUs' access delay. Moreover, the impact of the numbers of SUs and channels is also analyzed to study the relationship between CRN size and SUs' minimum access delay.

Due to the flying nature of unmanned aerial vehicles (UAVs), it is very attractive to deploy UAVs as aerial base stations and construct airborne networks to provide service for on-ground users at temporary events (such as disaster relief, military operation, and so on). In the constructing of UAV airborne networks, a challenging problem is how to deploy multiple UAVs for on-demand coverage while at the same time maintaining the connectivity among UAVs. To solve this problem, we propose two algorithms: a centralized deployment algorithm and a distributed motion control algorithm. The first algorithm requires the positions of user equipments (UEs) on the ground and provides the optimal deployment result (i.e., the minimal number of UAVs and their respective positions) after a global computation. This algorithm is applicable to the scenario that requires a minimum number of UAVs to provide desirable service for already known on-ground UEs. Differently, the second algorithm requires no global information or computation, instead, it enables each UAV to autonomously control its motion, find the UEs and converge to on-demand coverage. This distributed algorithm is applicable to the scenario where using a given number of UAVs to cover UEs without UEs' specific position information. In both algorithms, the connectivity of the UAV network is maintained. Extensive simulations validate our proposed algorithms.

Collective motion is a fundamental operation of robot swarms by which a group of robots move from a source to a destination in a cohesive way (i.e., connectivity is preserved during these movements). However, the collective motion of robot swarms along preplanned paths has not been well studied. In this paper, we propose self-adaptive collective motion algorithms for swarm robots in 3-D space. Using the proposed collective motion algorithms, robots are able to move along a preplanned path from a source to a destination while satisfying the following requirements: 1) the robots use only one-hop neighbor information; 2) the robots maintain connectivity of the network topology for information exchange; 3) the robots maintain a desired neighboring distance; and 4) the robots are capable of bypassing obstacles without partitioning the robot swarm (i.e., member loss). Our basic idea is to introduce a guidance force and a topology force into the system. The guidance force is used to guide the robots to their destination along the preplanned path. It ensures that the robots continue to move until they reach their destination. The topology force is used to maintain a “good” topology of the robot swarm, such as maintaining connectivity of the network topology and the desired distance between neighboring robots. The resultant of the guidance and topology forces determines the movement of a robot. We develop collective motion algorithms for three cases: 1) no obstacles or leaders; 2) no obstacles with a leader; and 3) with obstacles (with and without a leader). Extensive simulations are conducted to evaluate performance of the proposed algorithms. The simulation results show that: 1) our algorithms meet all the requirements; 2) our algorithms are resistant to GPS errors and robot failures; and 3) self-adaptive control of our algorithms makes network topologies more stable and significantly saves travel time of swarm robots. Note to Practitioners-We propose self-adaptive collective motion algorithms that enable swarm robots to move along a preplanned path from a source to a destination in 3-D space. Our algorithms use only one-hop neighbor information and operate without central controllers. The algorithms are designed to be self-adaptive in the sense that robots are able to dynamically determine proper moving parameters, based on their environments and statuses. With the proposed algorithms, swarm robots are able to: 1) maintain connectivity and a desired neighboring distance during movement; 2) bypass obstacles without member loss (i.e., the robot swarm is partitioned); and 3) be resistant to GPS errors and robot failures. We address both cases of with a leader and without a leader. Simulation results show effectiveness of our algorithms which can be applied in applications of surveillance, search and rescue, mining, agricultural foraging, autonomous military units, and distributed sensing in micromachinery or human bodies.

Cognitive radio (CR) has emerged as an advanced and promising technology to exploit the wireless spectrum opportunistically. In cognitive radio networks (CRNs), any pairwise communicating nodes are required to rendezvous on a commonly available channel prior to exchange information. In the earlier research, the most popular method is selecting a Common Control Channel (CCC) in CRNs to establish the rendezvous. However, employing a CCC has many problems such as the control channel saturation, vulnerability to jamming attacks, and inapplicability to dynamic network scenarios. Therefore, the blind rendezvous, which requires neither CCC nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait (SJ-RW) blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis, computer simulations and experiment with testbed have validated the proposed SJ-RW algorithm.

The tradeoff between decreasing interference to primary user (PU) and increasing secondary user's (SU's) throughput is of great importance for cooperative sensing in cognitive radio networks. Non‐ideal spectrum sensing in PHY and multiple SUs' access contention in MAC jointly impact SUs' transmission and the tradeoff. In this study, the authors investigate the joint impact from a cross‐layer perspective. First, they quantify the reliability of cooperative sensing and compute SUs' transmission probability under the conditions of non‐ideal sensing and access contention. Closed‐form expressions of the interference probability to PU and SUs' throughput are derived. Specially, two widely‐used contention‐based MAC protocols, i.e. slotted Aloha and distributed coordination function, are studied. Then, they formulate the sensing‐throughput tradeoff problem by using interference probability to PU, rather than the detection probability, as the constraint. Finally, a 2‐dimension search algorithm is proposed to obtain the optimal solution, including the optimal fusion rule, sensing duration and detection threshold. Simulation results validate the outperformance of the cross‐layer scheme. They also demonstrate how the optimal solution varies with some key parameters, i.e. PU's signal‐to‐noise ratio and the number of contending SUs.

We propose a channel selection strategy with hybrid architecture, which combines the centralized method and the distributed method to alleviate the overhead of access point and at the same time provide more flexibility in network deployment. By this architecture, we make use of game theory and reinforcement learning to fulfill the optimal channel selection under different communication scenarios. Particularly, when the network can satisfy the requirements of energy and computational costs, the online decision algorithm based on noncooperative game can help each individual sensor node immediately select the optimal channel. Alternatively, when the network cannot satisfy the requirements of energy and computational costs, the offline learning algorithm based on reinforcement learning can help each individual sensor node to learn from its experience and iteratively adjust its behavior toward the expected target. Extensive simulation results validate the effectiveness of our proposal and also prove that higher system throughput can be achieved by our channel selection strategy over the conventional off-policy channel selection approaches.

Cognitive radio (CR) has emerged as a promising technology to increase the utilization of spectrum resource. A pivotal challenge in CR lies on secondary users' (SU) finding each other on the frequency band, i.e., the spectrum locating. In this demo, we implement two kinds of multi-channel rendezvous technology to solve the problem of spectrum locating: (i) the common control channel (CCC) based rendezvous scheme, which is simple and effective when a control channel is always available; and (ii) the channel-hopping (CH) based blind rendezvous, which could also obtain guaranteed rendezvous on all commonly available channels of pairwise SUs in a short time without a CCC. Furthermore, the cognitive nodes in the demonstration could adjust their communication channels autonomously according to the dynamic spectrum environment for continuous data transmission.

This paper describes our demonstration of a self-adaptive network architecture reconfiguration technology in Cognitive Radio Networks (CRNs) under complex and vicious environment. The technology enables CRN to switch between three kinds of typical network architectures, i.e., centralized, ad hoc and cooperative relay in an autonomous and flexible way. This self-adaptive switch can solve the problem of physical breakdown and invalid communication link in vicious network environment, and hence enhance the network robustness. We implement the technology on a CRN testbed consisting of GNU Radio and USRPs and verify its performance including switching time and throughput.

The blind rendezvous, which requires neither Common Control Channel (CCC) nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis and computer simulations have validated our algorithm.

In order to improve the connectivity and survivability of Cognitive Radio Networks (CRNs) under complex and vicious communication environment, we propose a network architecture self-adaption technology. The technology enables CRN to switch between three kinds of architectures, i.e., centralized, single-hop ad hoc and cooperative relay in an autonomous and flexible way. This self-adaptive switching can deal with physical breakdown and invalid communication link due to great distance or spectrum heterogeneity, and enhance the network robustness thereby. The work patterns of each architecture and the switching scheme between them were demonstrated, and theoretical switching time was also calculated. Moreover, a testbed based on GNU Radio and USRPs was set up to test its performance including switching time and throughput. Testing results prove the effectiveness of the technology.

For energy harvesting cognitive radio (EHCR), secondary user's (SU's) energy harvesting duration and spectrum sensing parameters jointly impact SU's capacity but have not been studied yet. In this paper, we investigate the joint impact. First, we obtain the closed-form expression of SU's capacity considering both limited energy supply and imperfect spectrum sensing. Then, we maximize SU's capacity while satisfying detection probability constraint and guaranteeing that the harvested energy is sufficient to transmit a certain amount of data payload. Simulation results prove that the optimized energy harvesting duration and spectrum sensing parameters dramatically improve SU's capacity.

The tradeoff between increasing secondary users’ (SUs’) throughput and decreasing interferences to primary user (PU) is an important problem in cognitive radio networks. Joint design of sensing duration and frame duration has a crucial impact on both these two conflicting attributes but has not been studied yet. In this study, using a cross‐layer approach, the authors investigate joint design of sensing duration and frame duration for the tradeoff. Specially, they consider that PU's traffic randomly changes within a secondary frame and multiple SUs contend to use the licensed channel based on widely used large/small‐scale‐backoff‐based MAC protocols. By modelling more realistic PU's traffic, imperfect spectrum sensing in PHY and multiple SUs’ access contention in MAC, the authors reformulate the sensing‐throughput tradeoff problem to maximise SUs’ throughput while restricting interference probability to PU under a tolerable level. Moreover, the optimal solution is analysed and a bi‐dimensional search algorithm is presented. Simulation results show that the authors’ proposal achieves better throughput performance than conventional approaches. They also show how the optimal solution varies with received PU's signal‐to‐noise ratio and PU's traffic distribution.

The tradeoff between the interference to primary users' (PUs) and secondary users' (SUs) throughput is of great importance in cognitive radio networks. Both imperfect spectrum sensing and multi-channel access contention impact the tradeoff. In this paper, jointly considering imperfect spectrum sensing and multi-channel access contention from cross-layer perspective, we obtain the expressions of interference probability to PUs' and SUs' throughput for both slotted Aloha and distributed coordination function-based Medium Access Control (MAC) protocols. Compared against related contributions, which use detection probability as constraint, we formulate the sensing-throughput tradeoff problem by taking the interference probability as the optimization constraint. We further propose to use the access strategies set and exhaustive search on sensing duration to jointly optimize spectrum sensing parameters and access parameters with the objective of a completely cross-layer design. Numerical results show that the proposed cross-layer method can improve SUs' throughput performance significantly by relaxing the requirement of sensing reliability. Moreover, SUs' throughput performance when the realistic multi-channel scenario is taken into account is worse than the predicted performance in related contributions assuming single-channel scenario. In addition, the optimal solution varies with the number of divided sub-channels and frame duration, and thus needs to be carefully designed.

From cross-layer perspective, the impact of imperfect spectrum sensing and access contention on the cooperative sensing in cognitive radio networks is investigated in the context of the tradeoff between interferences to PUs and aggregated secondary throughput. Jointly considering imperfect spectrum sensing and access contention, we reformulate the sensing-throughput tradeoff problem via taking the interference probability, rather than the detection probability, as the optimization constraint, and further obtain the optimal combination of fusion rule, sensing duration and detection threshold to maximize the secondary throughput under the interference probability constraint. Numerical results show that the proposed cross-layer method can improve the secondary throughput performance significantly, especially in the case of low signal-to-noise ratio.

In multi-channel cognitive radio networks, it is important to investigate the sensing-throughput tradeoff from cross-layer perspective. Jointly considering imperfect spectrum sensing in physical layer and multi-channel access contention in MAC layer, we reformulate this tradeoff via taking the interference probability, rather than the detection probability, as the optimization constraint. And we further obtain the optimal sensing duration and detection threshold depending on the number of available channels to maximize the secondary throughput under the interference probability constraint. Numerical results show that the proposed cross-layer method can improve the secondary throughput performance significantly.

The authors propose a novel and practical approach to estimate resources and perform a distributed admission control in multi-hop ad hoc networks based on multi-rate enabled IEEE 802.11 technology. The main challenge is to determine if there exist sufficient resources [i.e. the available bandwidth (AB)] for a new incoming flow to be admitted rather than quantifying the exact amount of existing resources. In order to determine the AB along a multi-hop path, the authors take into consideration the channel rate at each hop as well as the channel idle ratio of relevant neighbouring nodes. Furthermore, the admission control is performed at the same time as the AB is determined which minimises overhead. The proposed approach can be applied hop-by-hop in a distributed manner by the end-user, thus being suitable for wireless ad hoc networks. Analysis and simulation based on the Network Simulator version 2 (NS2) platform verify the proposed approach.

Most existing research has made use of simulation and analytical methods to study Wireless Local Area Networks (WLAN). In this paper, we use FPGA to model and implement the 802.11 Distributed Coordination Function (DCF). Firstly, we define the functional blocks and their working behavior, then, we describe the corresponding implementation onto FPGA devices. The design of all functional blocks strictly follows the specifications of the 802.11 standard. Thus, the proposed FPGA-based DCF module can seamlessly interconnect with existing commercial WLAN chipsets, furthermore all parameters of the 802.11 DCF are wide-open to system designers (i.e., can be easily configured/modified). The proposed implementation provides a normative prototype by which the development of DCF based systems can be tested and evaluated conveniently.

To determine the end-to-end throughput capacity of a multi-hop route in wireless networks, existing work either use a simplistic approach to divide the 1-hop throughput capacity by the number of contending links in the bottleneck region, which has limitations in terms of accuracy, or rely on complicated non-linear equations, which is impractical to solve for a large number of hops. In this paper, we present an optimization methodology to analytically calculate the end-to-end throughput capacity of IEEE 802.11-based multi-hop wireless networks. The calculation considers the interference due to neighboring nodes and assess the impact of hidden node collision as well as multi-rate terminals (i.e., nodes can transmit at different rates) on throughput capacity. The proposed methodology provides a very accurate calculation of the end-to-end throughput capacity when compared to existing work, and yet it is more practical to implement.

A distributed approach to perform admission control in multi-hop ad hoc networks is proposed. Existing algorithms normally compute the total amount of available resources in the network, however our method evaluates if there are sufficient resources to satisfy the bandwidth demand of a new incoming flow rather than quantifying the total amount, which is impractical when considering implementation. Our methodology also considers multi-rate scenarios and can be implemented hop-by-hop in a distributed manner, which makes the approach scalable and suitable for wireless ad hoc networks. The distributed admission control algorithm introduced in this paper is assessed via analysis and simulation.

In this paper we proposed a typical cooperative MIMO system grounded on wireless mobile ad hoc networks and brought forward the problem of time efficiency in cooperative MIMO system. And then adopting time efficiency, we analyzed the Shannon capacity limit of the cooperative MIMO system. The analysis shows two implications. First, only when intra-cluster channel is better enough than inter-cluster channel, cooperative MIMO can bring increment of channel capacity; second, there should be an optimal number of cooperative partners in a cooperative MIMO system. For instance it's optimal to use 3 cooperative partners in the proposed typical cooperative MIMO system, when it can achieve a channel capacity increment of about 2 bps/hz compared with direct transmission.