Considering the limited spectrum and incomplete coverage of communication infrastructures for mobile edge computing (MEC), we present a cognitive unmanned aerial vehicle (UAV)-assisted MEC system, where UAVs share licensed spectrum with primary user (PU) for computation offloading. A joint trajectory design, power control, and computation task allocation (JTPAC) problem is formulated to minimize the long term system cost under the constraint of PU communication rate. To solve this complicated problem, we transform it into a partially observable Markov decision process (POMDP) based optimization and propose a multi-agent deep deterministic policy gradient (MADDPG)-based JTPAC algorithm. Simulation results show that the proposed algorithm achieves lower system cost compared to the benchmark algorithms and the PU communication rate can be guaranteed under different situations.

This study focuses on a free space optical (FSO)/radio frequency (RF) satellite-UAV-terrestrial integrated network (SUTIN) to overcome limitations of traditional RF SUTIN on spectrum scarcity and security. Traditional resource allocations for FSO/RF SUTIN often prioritize transmission efficiency while neglecting reliability. Moreover, in this network, atmospheric turbulence, pointing errors, and multipath fading can lead to unreliable data transmission. To resolve these problems, we propose a hybrid FSO/RF SUTIN based on adaptive modulation and coding scheme (MCS) and resource allocation algorithm to ensure both transmission efficiency and reliability with imperfect channel state information (CSI) and random channel fading. A joint optimization problem is formulated, incorporating MCS mode selection, power allocation, and UAV trajectory optimization, with frame error rate (FER) and CSI uncertainty constraints to maximize system throughput. To solve this complicated high dimension optimization problem, we present mathematical mapping models between the optimization variables and communication performance parameters (the average transmission rate and FER) with imperfect CSI and random fading for both FSO and RF link. According to these mapping models, a robust solution based on deep deterministic policy gradient is proposed, integrating decoupling and user clustering techniques to reduce the computational complexity. The simulation results demonstrate that this proposed FSO/RF SUTIN and the corresponding algorithm can improve system throughput and guarantee transmission reliability.

With the increasing complexity of next generation network applications and the coexistence of diverse service requirements, Generative AI (GAI) and Large Models (LMs) based semantic communication are widely regarded as promising solutions to address these challenges. The goal of these systems is not only to reduce system burden by reducing transmission data, but also to adapt to new and complex requirements. In this paper, we propose a semantic communication system designed to meet diverse requirements of speech applications while enabling accurate speech transmission. The semantic encoder comprises an unsupervised model wav2vec 2.0 for learning universal speech representations to enable adaptability across various speech-related tasks. It also includes a prosodic feature encoder from the style embedding module of Global Style Tokens (GST) Tacotron. The semantic decoder integrates a phoneme recognition module and a GST-Tacotron-based text-to-speech (TTS) module to facilitate accurate and expressive reconstruction of the original speech signal, with the incorporation of prosodic features enhancing the naturalness and intelligibility of the synthesized speech. The proposed system has been tested in noisy channels. It demonstrates that the system maintains superior and robust performance even at Bit Error Rate (BER) of 10−1, as reflected by a stable Character Error Rate (CER) approximately 0.0940 and 0.0649 for the base and large versions of wav2vec 2.0 respectively in speech recognition, and consistent cFDSD scores approximately 0.9 in speech quality assessment. This performance surpasses that of the existing semantic communication systems, while also providing reliable support for a wider range of downstream speech applications.

Modulation recognition has emerged as an intensive research topic to improve the communication efficiency in the future 6G network and plays an important role in the security of electromagnetic spectrum. Various pattern recognition methods have been proposed to enhance the performance of modulation recognition, especially deep learning models showing their emerging performance. In this work, we design a modulation recognition model based on an enhanced Transformer, namely MR-Transformer, which is accelerated on a Field Programmable Gate Array (FPGA). The design of MR-Transformer targets on high recognition accuracy, low power consumption, and high computation efficiency, which is suitable for modulation recognition at edge devices. MR-Transformer leverages attention mechanism to extract global features and correspondingly enhance the recognition accuracy. An improved matrix multiplication operation and enhanced Design Space Exploration (DSE) method are proposed in MR-Transformer to improve the computation efficiency and reduce resource consumption. We conduct comprehensive experiments to evaluate the performance of MR-Transformer on three platforms, i.e. Central Processing Unit (CPU), Graphics Processing Unit (GPU), and FPGA, based on two open-source datasets. According to the evaluation results, the MR-Transformer based on FPGA shows the best performance compared with the baseline models considering accuracy, power consumption, and computation efficiency.

Wireless-based federated learning (FL), as an emerging distributed learning approach, has been widely studied for 6G systems. When the paradigm shifts from terrestrial to non-terrestrial networks (NTN), FL may need to address several open challengings, e.g., limited service time of low earth orbit (LEO) satellites and time-efficient uploading and aggregation for massive devices. In this work, we exploit the synergy of LEO and FL for future integrated 6G-satellite systems by taking advantage of ubiquitous wireless access provided by LEO and appealing characteristics of collaborative training and data privacy preservation in FL. The studied LEO-FL framework may need to improve multi-metric performance in practice. Different from most FL works, we simultaneously improve the communication-training efficiency and local training accuracy from a multi-objective optimization (MOO) perspective. To solve the problem, we propose a decomposition, deep reinforcement learning and transfer learning based MOO algorithm for FL (DRT-FL), aiming at adapting to the dynamic satellite-terrestrial environments, achieving efficient uploading and aggregation, and approaching Pareto optimal sets. Compared to the state-of-the-art MOO algorithms, the effectiveness of the proposed LEO-FL framework and DRT-FL algorithm are assessed on MNIST and CIFAR-IO datasets.

The millimeter-wave (mmWave) spectrum has become a core of wireless communication, which has the advantages of richer spectrum resources, larger communication bandwidth, and smaller spectrum interference. Human activity recognition (HAR) by mmWave radar based on point cloud attracts significant attention due to its nature of privacy-preserving, which is an important task of realizing integrated sensing and communication (ISAC). This article proposes a framework of spatial–temporal point cloud transformer (ST-PCT) to realize high precision of HAR, based on sequential point cloud after preprocessing from mmWave radar without voxelization. In ST-PCT, it consists of four enhanced components: 1) a framewise spatial neighbor embedding module to extract the local feature; 2) a temporal and spatial attention mechanism module to find connections within and across frames; 3) an optimized attention mechanism to improve the efficiency of feature extraction; and 4) a sensor fusion module with more motion information to improve the difference between activities. We experimentally evaluate the efficiency of our framework compared with several approaches based on the voxelization or point cloud directly. The experimental results have demonstrated that the proposed ST-PCT network greatly outperforms the other approaches in terms of overall accuracy (oAcc), achieving 99.06% and 99.44%, respectively, on two data sets.

Vehicular edge computing (VEC) is considered to be a key technology to improve the processing efficiency of computing tasks for the Internet of Vehicles (IoV). Using roadside units (RSUs) distributed on both sides of a road as edge servers, computation-intensive and latency-sensitive in-vehicle tasks can be responded to quickly. However, some quality of service (QoS) is often difficult to ensure due to clogged dense urban buildings or lack of infrastructure in remote areas. In this paper, we propose a software-defined network (SDN)-driven partial offloading model for unmanned aerial vehicle (UAV)-assisted VEC networks, where the RSUs and UAVs jointly provide computing services to the vehicles and collect global information through centralized control using a SDN controller. To guarantee these vehicles obtain computing results in time and rationally utilize computing resources, we develop an optimal offloading mechanism using age of information (AoI), together with energy consumption and rental price as a comprehensive weighted cost of our above optimization objective. The total system cost of the performing tasks is minimized by jointly optimizing the UAV trajectory, user association, and offloading decision. Considering the mobility of the vehicles and UAVs and the dynamic network environment, we design a deep reinforcement learning (DRL)-based joint trajectory control and offloading allocation algorithm (DRL-TCOA) to solve the proposed computation offloading problem. Experimental results show that the proposed DRLTCOA algorithm maintains better information freshness and lower system cost than the other baseline offloading strategies.

In this paper, we present a hybrid ambient backscatter communication (ABC) assisted framework for radio frequency (RF) powered cognitive radio networks (CRNs). In these CRNs, the secondary users (SUs) can actively transmit data when the primary user network (PUN) is idle, and harvest energy from the primary signal and transmit their own information over the primary signal when the PUN is busy. The proposed CRNs adopt non-orthogonal multiple access (NOMA) technique to further improve spectral efficiency. Considering two different spectrum sharing paradigms of the PUN and the SUs, we formulate two optimization problems by two Markov decision processes (MDPs) to maximize our long term throughputs for both underlay-interweave and overlay-interweave scenarios. We propose a deep reinforcement learning (DRL) based optimization algorithm, i.e., a deep deterministic policy gradient (DDPG)-based joint reflection coefficients adjustment and resource allocation (JCARA) algorithm, to solve these two non-convex problems under the two constructed MDPs without the non-causal and the statistical information about the dynamic environment a-priori. For the underlay-interweave scenario, the proposed JCARA algorithm jointly optimizes the transmit power and the reflection coefficients of the SUs, while for the overlay-interweave scenario it optimizes the above two variables plus time resource simultaneously. Simulation results clearly show the higher throughput performance of this proposed algorithm for the proposed CRNs in the comparison with other algorithms.

With the fast development of location-based services, an ubiquitous indoor positioning approach with high accuracy and low calibration has become increasingly important. In this work, we target on a crowdsourcing approach with zero calibration effort based on visible light, magnetic field, and WiFi to achieve submeter accuracy. We propose a CrowdFusion simultaneous localization and mapping (SLAM) composed of coarse-grained and fine-grained trace merging, respectively, based on the iterative closest point (ICP) SLAM and GraphSLAM. ICP SLAM is proposed to correct the relative locations and directions of crowdsourcing traces and GraphSLAM is further adopted for fine-grained pose optimization. In CrowdFusion SLAM, visible light is used to accurately detect loop closures and magnetic field to extend the coverage. According to the merged traces, we construct a radio map with visible light and WiFi fingerprints. An enhanced particle filter fusing inertial sensors, visible light, WiFi, and floor plan is designed, in which visible light fingerprinting is used to improve the accuracy and increase the resampling/rebooting efficiency. We evaluate CrowdFusion based on comprehensive experiments. The evaluation results show a mean accuracy of 0.67 m for the merged traces and 0.77 m for positioning, merely replying on crowdsourcing traces without professional calibration.

In recent years, unmanned aerial vehicles (UAVs) have been widely used in wireless communications due to their low cost, small size, flexible deployment, and mobile controllability. However, because of the Line-of-Sight (LoS) communication links, the security threat is always a challenging problem to deal with. In particular, information stolen and leakage may happen in the presence of eavesdroppers. This article proposes a UAV-enabled system with a relay UAV and a jammer UAV, and certain mobile source and destination nodes in the presence of an eavesdropper to solve the secrecy rate maximization problem. In this system, the relay UAV transmits information between pairs of moving source nodes and moving destination nodes with interrupted communication channels due to blockage or long distance, and the jammer UAV interferes with eavesdropper to reduce the milked information through sending jamming signals. We establish an average secrecy rate maximization problem with trajectory and transmit power optimization under certain constraints for this system. Since this problem is nonconvex and reformulated as the Markov decision process (MDP), we use deep reinforcement learning (DRL) method to solve it. In this article, we adopt a proximal policy optimization (PPO) algorithm to find an optimal solution because it can deal with the model of continuous action space. According to our defined states, rewards, and actions in this specified MDP, this algorithm can autonomously learn to optimize the trajectory and power allocation of the UAVs to realize our goal. Simulation results demonstrate that the proposed PPO-based average secrecy rate maximization algorithm is valid, effective and scalable.

Gesture recognition has been a hot research topic in human–computer interaction, since contactless gesture recognition will provide increasing applications in many fields. Millimeter-wave (mmWave) radar well serves this technology because of its high accuracy, easy integration, and strong anti-jamming ability in moving object detection. However, it is still challenging to meet the requirement of high precision in subtle gesture recognition based on traditional methods via point cloud or Range-Doppler heat map of mmWave radar. Considering the raw data from mmWave radar with more information, such as phase, we propose a system that uses the constructed mmWave radar data cube sequence and timedistributed-CNN-transformer network (CTN), called DCS-CTN system, to get higher hand gesture recognition accuracy. In this system, we introduce a time-distributed wrapper (TD) and convolutional neural network (CNN) to extract local features of the data cube sequence, a position encoder to retain time information of the sequence, and a transformer network to get global features of the sequence. The experiments results show that this system can achieve hand gesture recognition accuracy of 99.75%, which is significantly higher than the other traditional approaches.

With the ubiquitous deployment of a massive number of Internet-of-Things (IoT) devices, the satellite-aerial networks are becoming a promising candidate to provide flexible and seamless service for IoT applications. Concerning about the spectrum scarcity issue, we present a cognitive satellite-aerial network where the multiple unmanned aerial vehicles (UAVs) can share spectrum with the satellite without interfering with satellite communications. To further improve spectral efficiency, non-orthogonal multiple access (NOMA) technique is adopted in this network. Considering the delay-sensitive quality-of-service (QoS) requirement, a joint trajectory and power optimization problem is formulated to minimize the total transmission latency over a long-term task period. In order to reduce the computational complexity and ease the burden of information exchange by using the centralized DRL methods, we propose a multi-agent deep deterministic policy gradient (MADDPG) based algorithm which adopts the framework of centralized training with decentralized execution to solve the sophisticated problem. The simulation results show the proposed algorithm can achieve satisfactory performance through joint trajectory control and power allocation for the UAVs compared with other methods.

When there are unmanned aerial vehicles (UAVs) performing their specifically assigned tasks in the air, some of them still have available resources to access different ground communication networks to improve their communication performance, especially for the wireless sensor network. Technically, when they execute their own given missions with predetermined trajectories, they can also provide opportunistic assistance for terrestrial networks at the same time. In this article, we solve an opportunistic UAV-assisted data transmission problem in a wireless sensor network from a novel perspective. In consideration of UAVs dynamic behaviors, varying transmission tasks, and real-time matching between UAVs and sensor clusters, we propose to jointly optimize UAV scheduling and power control aiming to obtain optimal policies to maximize the network data transmission in a long run under the opportunistic access mode. We reformulate this optimization problem as a Markov decision process (MDP) and take deep reinforcement learning (DRL) as our tool to obtain solutions. We develop a DQN-based and a deep deterministic policy gradient (DDPG)-based optimization approaches to adjust the power allocation of cluster heads, and the scheduling and bandwidth allocation of UAVs during their missions over the covered area to improve the whole network data transmission performance. Simulation results demonstrate the validity and superiority of our proposed approaches compared with other benchmark policies in different perspectives.

Green wireless communications have been extensively studied in wireless sensor networks (WSNs), including the use of new energy, renewable energy, and low-power consumption and energy-saving technologies for years. In these networks, due to channel fading, insufficient and random energy arrival, some possible bad deployment of sensors, etc., the communication among sensor nodes in a WSNs will inevitably be affected or even interrupted sometimes, which may result in unacceptable performance in the entire network. In order to solve this problem, we propose a WSN composing of several local subnetworks with amplified forwarding relay and specially designed working time cycle. In this network, we study our resource allocation policies to manage both power and time for throughput maximization. We use deep reinforcement learning (DRL) to develop our resource allocation policies under the model constructed as a Markov decision process for this optimization problem in the subnetwork. We apply an actor–critic strategy to find our optimal solution in continuous state and action space and adaptively achieve maximum throughput of this network based on energy harvesting, causal information of battery state and channel gains. The simulation results demonstrate that the proposed transmission policies can produce higher throughput in the local network and finally improve overall system performance in comparison with greedy policy, random policy, and conservative policy.

In recent years, the demand for indoor positioning systems has grown rapidly with regard to location-based services. As a cost-effective choice, WiFi-based indoor positioning has attracted great increasing research attentions because it does not require external devices installed in the target environment. Although extensive research has been conducted on WiFi fingerprint matching, the problem of automatically adapting radio maps to fresh signal space environment still exists. The traditional methods often conduct site surveys regularly to update the outdated radio maps, which is time consuming and laborious. In this work, we propose an indoor positioning system AAIMSS to automatically update the radio maps based on an enhanced transfer learning (TL) approach with altered access points (APs) identification and mapping space searching. In the system, the proposed TL approach removes the outlier features and then searches a more accurate mapping space between the original radio map and crowdsourcing data. Our lightweight solution does not rely on additional devices and inertial sensors with high-power consumption. A set of experiments has been conducted in a teaching building to evaluate AAIMSS. The results show that this AAIMSS is robust to locate users in a dynamic environment. The average positioning accuracy achieves 2.5m, which significantly outperforms the positioning strategies with the original radio map by 65.3%, the radio map by directly removing the altered APs by 19.4%, and the radio map by the traditional TL by 85.2%.

Recently, indoor positioning has played a critical role in many emerging indoor applications. However, due to complicated indoor environments, it is still challenging to develop an indoor positioning system with high positioning accuracy and low deployment efforts. In this work, an indoor positioning system based on visible light fingerprinting is proposed by leveraging a novel visible light GraphSLAM (VL-GraphSLAM) technique. The proposed VL-GraphSLAM provides enhanced solutions at both frontend and backend to improve the accuracy of estimated trajectories. Then, the estimated trajectory is anchored in floor map based on a novel door detection method to recover indoor walking paths. Based on VL-GraphSLAM, we construct a database with the visible light received signal strength labeled by the locations of walking paths, which is called visible light map. Moreover, a Kalman filter is adopted to fuse the visible light fingerprinting and inertial sensors to locate users. Comprehensive experiments illustrate that our proposed system can accurately recover walking paths (0.4 m) and locate users (0.9 m) in an accuracy of submeter, which significantly outperforms a traditional WiFi-based fingerprinting system and is more convenient to deploy than a traditional visible light positioning based on ranging.

Traditional WiFi positioning relies on a predefined radio map, which is labor-intensive and time-consuming for professionals. Recently, crowdsourcing has emerged as a promising solution for facilitating WiFi positioning. To crowdsense a radio map, traces collected from normal users are merged to recover the original walking paths. In this work, we design a robust iterative trace merging algorithm called WiFi-RITA based on WiFi access points as signal-marks. The algorithm formulates the trace merging problem as an optimization problem in which each trace is translated and rotated to minimize the limitation of distances among traces defined by WiFi access points. WiFi-RITA is further enhanced by removing outliers. WiFi-RITA is robust to the rotation errors of traces and efficient for a large number of short traces. According to the crowdsensed radio map, a sensor fusion approach based on particle filter by fusing inertial sensors and a multivariate Gaussian fingerprinting is proposed to enhance the accuracy of crowdsourcing indoor positioning. The experiment results in two large-scale environments demonstrate that WiFi-RITA positioning with zero-effort calibration achieves high positioning accuracy, which outperforms Pedestrian Dead Reckoning (PDR) and fingerprinting with K Nearest Neighbor.

This paper proposes a distributed power control scheme based on the constrained model predictive control (MPC) for the underlay cognitive satellite terrestrial networks (CSTNs), where the primary satellite communication network coexists with the secondary terrestrial mobile network. We model this power control problem as a closed-loop dynamic control system with the inner loop and outer loop. On the basis of combining target power control (TPC) algorithm in the inner loop and tracking of flexible target signal to interference plus noise ratio (SINR) in the outer loop, we develop a corresponding state space expression of the problem where the fluctuation of each channel power gain is formulated as the exogenous disturbance input so that we do not need the accurate instantaneous channel state information (CSI). Then we design a SINR regulator in the outer loop, which is a constrained model predicted controller with rolling optimal operation subject to the interference temperature constraint obtained by calculating a linear matrix inequality. Finally, we obtain our constrained model predictive power control algorithm. In contrast to the previous static power control schemes based on the optimization theory that highly depend on the known instantaneous CSI and large signalling exchanges, the proposed scheme only needs locally measured information and outdate feedbacks. The performance of the proposed algorithm is shown to be effective through computer simulations.

In this study, a resource allocation problem considering physical layer security and power consumption for cognitive relay networks is studied. In a secondary network, the ratio of secret rate to power consumption is defined as a secret rate per watt (SRW). Fixed circuit power, dynamic circuit power, and transmit power are all considered in the power consumption model. Under the constraints of the maximum total transmit power of secondary user transmitters and relay nodes, minimum secret rate requirement of secondary user receiver and tolerable interference threshold of each primary user receiver, the authors propose a SRW maximisation algorithm to maximise SRW. The resource allocation problem is formulated as a non‐linear fractional programming and it is transformed into an equivalent subtraction based on the Dinkelbach method. They incorporate the non‐convex constraints into the objective function to convert the feasible region into a convex set and achieve an optimal resource allocation scheme with the nested loop iteration algorithm through the Lagrange dual theory and the difference of convex programming. By the Dinkelbach method and the nested loop iteration algorithm, the optimal SRW is obtained. Simulation results demonstrate the effectiveness and the out‐performance of the proposed algorithm comparing with other algorithms.

Ultradense network (UDN) is considered as one of the key technologies for the explosive growth of mobile traffic demand on the Internet of Things (IoT). It enhances network capacity by deploying small base stations in large quantities, but it simultaneously causes great energy consumption. In this article, we use energy harvesting (EH) and energy cooperation technologies to maximize system throughput and save energy. Considering that the energy arrival process and channel information are not available a priori, we propose an optimal deep reinforcement learning (DRL) algorithm to solve this average throughput maximization problem over a finite horizon. We also propose a multiagent DRL method to solve the dimensionality problem caused by the expansion of the state and action dimensions. Finally, we compare these algorithms with two traditional algorithms, greedy algorithm and conservative algorithm. The numerical results show that the proposed algorithms are valid and effective in increasing system average throughput on the long term.

Mobile wireless sensor networks (MWSNs) are resource constrained, and have limited energy and transmission range. Distributed collaborative beamforming (DCB) in MWSNs based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance the energy efficiency of a single sensor node. To achieve a lower maximum sidelobe level (SLL), sensor nodes can move to optimal locations with optimal excitation current weights for DCB. However, this leads to an extra motion energy consumption. In this article, we construct a multiobjective optimization framework (MOF) to jointly optimize the maximum SLL, transmission power, and motion energy consumption of the DCB nodes in MWSNs. Moreover, an improved nondominated sorting genetic algorithm-II (INSGA-II) and a distributed parallel INSGA-II (DPINSGA-II) are proposed for solving the formulated MOF. In addition, a simple but practical DCB scheduling mechanism is proposed. The simulation results show that the maximum SLL, transmission power, and motion energy consumption of the VNAA can be effectively optimized by the proposed algorithms.

Conventional collaborative beamforming with virtual node antenna array often results in high maximum sidelobe level (SLL) due to the unexpected node positions. In this paper, a hybrid optimization approach (HOA) for the SLL suppression and transmission power reduction is proposed. The proposed HOA organizes the node locations according to the concentric circular antenna array for location optimization. Then, a novel algorithm called variation particle chicken swarm optimization (VPCSO) is proposed to further optimize the transmission power weight of the selected array nodes. Simulations are conducted and the results show that the proposed location optimization approach is effective, and the maximum SLL of the beam patterns obtained by VPCSO is lower than that of other algorithms. Moreover, the overall transmission power weights obtained by the proposed VPCSO is the lowest among all the comparison methods.

Antenna arrays can enhance the directivity and save the transmission power of a communication system. Beam pattern optimization for reducing the maximum sidelobe level (SLL) is a classical electromagnetic problem in antenna arrays. In this paper, a novel improved chicken swarm optimization (ICSO) algorithm is proposed to suppress the maximum SLL of the linear antenna array (LAA), the circular antenna array (CAA) and the random antenna array (RAA). Three improved factors that are the global search, the weighting and the local search factors are introduced into the update method of the roosters, the hens and the chicks of the conventional chicken swarm optimization (CSO), respectively, to achieve better optimization results. Simulations are conducted to verify the performance of the proposed ICSO for suppressing the maximum SLL, and the results show that the proposed ICSO can obtain lower maximum SLL in LAA, CAA and RAA cases compared with several benchmark algorithms. Moreover, the stability of ICSO is evaluated and the results show that it outperforms the other algorithms.

Seamless outdoor-indoor positioning plays a critical role in many emerging applications, e.g., large-coverage user navigation in cities, smart buildings, and analytics of user spatial location big data. It is still challenging to construct a large-scale seamless outdoor-indoor positioning system due to the limited coverage of indoor positioning. In this paper, we propose a seamless outdoor-indoor crowdsensing positioning (SoiCP) system in which a radio map is automatically constructed based on crowdsourcing pedestrian dead reckoning (PDR) traces without professional site surveying. The constructed radio map is robust to inaccurate PDR traces and does not rely on prior knowledge of floor plans. In SoiCP, the crowdsensed radio map is obtained by a proposed three-step trace matching algorithm. This algorithm leverages building gates and WiFi fingerprints as landmarks to merge the noisy crowdsourcing traces and accurately construct the user walking paths. Moreover, following the crowdsensed radio map, SoiCP uses an enhanced particle filter to fuse PDR, GPS, and WiFi fingerprinting for seamless outdoor-indoor positioning with high accuracy. The comprehensive real-world experiments in two large-scale shopping malls demonstrate that SoiCP can effectively crowdsense the walking paths and track moving users with high accuracy.

Crowdsensing indoor walking paths based on crowdsourcing traces collected from normal users has recently become an emerging topic for indoor positioning, which can reduce the labor effort of building radio maps and improve the positioning accuracy when a floor plan is unavailable. In this work, we design an indoor walking path crowdsensing system with massive noisy crowdsourcing traces. In this system, we propose a robust iterative trace merging algorithm based on WiFi access points as markers (named 'WiFi-RITA') to merge massive noisy traces. The algorithm formulates the trace merging problem as an optimization problem in which each trace is controlled to translate and rotate to minimize the limitation of distances among traces defined by WiFi access points as markers. WiFi-RITA is robust to the rotation errors and uncertain absolute locations of user traces, and can efficiently work for a large number of user traces. We further adopt a landmark matching algorithm to match the merged traces to the target building and adopt a 2-dimensional histogram approach to remove outlier traces. With such procedures, we generate walking paths of a large-scale building with a mean accuracy of 2.1m.

In this work, we propose an automatic radio map construction system based on crowdsourcing Pedestrian Dead Reckoning (PDR) traces, which does not rely on priori knowledge of floor plans and is robust to inaccurate PDR traces. In this system, we propose to process some opportunistic PDR traces, in which users walk through the building, to generate parts of road paths by translating, rotating and scaling the traces based on the opportunistic GPS locations and gate points as landmarks. Then, we further extend the coverage of road paths by processing the PDR traces entirely obtained indoor by compensating the turning errors and merging the PDR traces based on the similarity of WiFi fingerprints. With such procedures, we can accurately generate indoor road paths of a large-scale building and construct the radio map based on these road paths. Our proposed method achieves a median accuracy of 2.8m and mean accuracy of 2.9m for the constructed road paths. By fusing GPS, PDR, and WiFi fingerprinting with the crowdsourcing radio map, we achieve a median positioning accuracy of 2.9m and mean accuracy of 3.4m without site surveying, which significantly outperforms the positioning algorithm by merely fusing GPS and PDR.

Considering a random time-varying channel model, we propose a decentralized power allocation (PA) scheme based on ℋ∞control theory for a cognitive radio network (CRN) under dynamic formulation by state space model with exogenous input. In this state space model, we transform the interference temperature (IT) constraint and the target signal to interference plus noise ratio (SINR) tracking to a weighted control performance index. We design a ℋ∞controller to make the index minimum to obtain a reasonable target SINR. In the PA scheme, each active secondary user (SU) controls its transmit power related with its instantaneous SINR to track the target SINR. Simulation results show that the proposed strategy using ℋ∞controller is effective and valid for the SINR and IT requirements of both SUs and primary user (PU).

The robust power allocation problem is often solved by different optimization approaches under a convex optimization model. But in this study, we use a distributed projected dynamic system (PDS) to describe this model and realize power allocation by designing a stable controller using Lyapunov function and linear matrix inequality (LMI) for the PDS. The controller can follow our defined target SINR and keeps the quality of service (QoS) required by primary user (PU) under the channel and the interference uncertainties (including feedback error). The simulation results illustrate that our proposed controller can realize power allocation with better performance compared with iterative waterfilling algorithm (IWFA).

In this paper, we study spectrum overlay access to share spectrum between primary users (PUs) and secondary users (SUs). Our goal is to maximize the average throughput by optimal power allocation within finite time duration. To do this, we formulate this optimization problem as a Markov Decision Process with continuous state. An approximate value method with pre-allocation mechanism is proposed, which can effectively protect the PUs by obtaining a continuous closed-form solution rather than a discrete one. Numerical results show that the proposed algorithm exhibits better performance than traditional methods while guaranteeing non-interference to PUs.

Probability density function (PDF) of the modulating retro‐reflector (MRR) turbulence fading channels is crucial for the performance analysis of MRR communication systems. In this study, closed‐form expression for the PDF of normalised MRR free‐space optical (FSO) turbulence fading coefficient is obtained first. Moreover, it is applied to the evaluations of the closed‐form expressions for average capacity and outage probability of MRR FSO links. The effects of the parameters such as atmospheric turbulence conditions, communication distance, receiver aperture diameter and the average signal‐to‐noise ratio on the performance of MRR FSO links are discussed. Results show that in order to achieve successful MRR FSO communication, communication systems employing MRRs are suitable for short‐distance communication in the case of low transmission power, besides quite high transmission power and large receiver aperture diameter are required simultaneously by long‐distance MRR FSO communication.

In this study, the authors study the robust resource allocation problem for orthogonal frequency division multiplexing‐based cooperative cognitive radio networks (CRNs) with decode and forward protocol and consideration of imperfect channel state information. The objective is to maximise the capacity of the cooperative CRN, while the interference to primary user receiver is below a predefined interference threshold and the transmit power of cognitive source and each relay is kept within their power budgets. Considering all possible channel uncertainties, they propose a heuristic robust relay selection scheme and formulate robust power allocation as a semi‐infinite programming (SIP). By the worst‐case approach, the SIP problem is converted into a convex optimisation problem and solved by the Lagrange dual decomposition method. They also analyse feasible regions of the constraints, convergence behaviour and computational complexity of their proposed robust algorithm. Simulation results show the impact of channel uncertainties and the outperformance of the proposed algorithm by comparing with non‐robust algorithms.

In this paper, we study the robust relay selection and power allocation problems for orthogonal frequency division multiplexing (OFDM) based cooperative cognitive radio networks (CRNs) with channel uncertainties. The objective is to maximize the capacity of the cooperative CRN, which is subject to the interference threshold constraints of primary users (PUs) and the total transmit power limitation of secondary user (SU) and relays. We describe all possible channel uncertainties with ellipsoid set and interval set. The robust relay selection and power allocation problems are formulated as semi-infinite programming (SIP) problems, respectively. We convert the SIP problems into their equivalent convex optimization problems with the worst-case approach, which can be solved by the Lagrange dual decomposition method. Simulation results show that the proposed robust relay selection and power allocation algorithm can strictly guarantee the quality of service of PUs under channel uncertainties.

This paper investigates power allocation problems for orthogonal frequency division multiplexing (OFDM)-based cognitive radio networks operating in licensed frequency bands. Considering imperfect spectrum sensing, a new power allocation algorithm is proposed to minimize the total interference introduced to primary user under a minimum capacity constraint of secondary user (SU) and a total transmit power constraint of the SU. The numerical results demonstrate that the proposed power allocation scheme can not only keep the rate requirement of SU under spectrum sensing errors by comparison with traditional method, but also fully make use of the limited spectrum resource.

In this study, a robust power allocation scheme with orthogonal frequency division multiplexing‐based cognitive radio network is proposed to maximise total data transmission rate subject to interference power constraint of primary user (PU) and transmit power budget constraint of secondary user where channel uncertainties and spectrum sensing errors are simultaneously considered. The authors first formulate the interference model by taking the imperfect spectrum sensing into account, then the channel state information errors are considered and assumed to be bounded with ellipsoidal and interval sets to establish robust resource allocation problem. On the basis of the worst‐case approach and Lagrange dual decomposition method, the original optimisation problem is converted into a convex one and solved. Simulation results show the robustness of their proposed scheme and the trade‐off performance with a sub‐optimal data transmission rate, but better protection of PU.

In this study, the problem of robust adaptive power control (PC) in an underlay cognitive radio network with multiple secondary users (SUs) and primary users (PUs) is considered. Due to the effects of uncertainties (i.e. estimation errors, delays), the optimal PC (resource allocation) cannot guarantee the quality of service of SUs and PUs under imperfect channel state information and interference power of PUs. A robust resource allocation problem is formulated to maximise sum throughput of SUs under individual power constraints and signal‐to‐interference‐and‐noise ratio constraints of SUs as well as interference temperature constraints of PUs, whereas channel uncertainties and interference uncertainties induced into the secondary system are modelled by multiplicative uncertainties. Under the worst‐case approach, the problem is transformed into a geometric programming problem solved by Lagrange dual methods. The performance of the different algorithms and the impact of uncertainties are discussed according to several simulation results.

Abstract One of the most challenging problems in dynamic resource allocation for cognitive radio networks is to adjust transmission power of secondary users (SUs) while quality of service needs of both SUs and primary users (PUs) are guaranteed. Most power control algorithms only consider interference temperature constraint in single user scenario while ignoring the interference from PUs to SUs and minimum signal to interference plus noise ratio (SINR) requirement of SUs. In this paper, a distributed power control algorithm without user cooperation is proposed for multiuser underlay CNRs. Specifically, we focus on maximizing total throughput of SUs subject to both maximum allowable transmission power constraint and SINR constraint, as well as interference temperature constraint. To reduce the burden of information exchange and computational complexity, an average interference constraint is proposed. Parameter range and convergence analysis are given for feasible solutions. The resource allocation is transformed into a convex optimization problem, which is solved by using Lagrange dual method. In computer simulations, the effectiveness of our proposed scheme is shown by comparing with distributed constrained power control algorithm and Nash bargaining power control game algorithm. Copyright © 2014 John Wiley & Sons, Ltd.

In cognitive radio networks, conventional power control algorithms (PCAs) based on instantaneous perfect channel gain may lead to performance degradation in practical systems, since channel uncertainties are inevitable because of quantisation errors and estimation errors. As a result, robustness of the algorithms becomes an important issue. However, traditional robust PCAs with probabilistic models require to perfectly know the distribution information of the estimation error (e.g. Gaussian distribution) which is difficult to obtain. Moreover, the distribution function of the actual error may not be Gaussian distribution. In this study, instead of using deterministic distribution model, a robust PCA based on a distribution‐free method is designed to minimise total transmit power of secondary users subject to probabilistic interference and signal to interference plus noise ratio constraints. Based on the minimax probability machine, the original problem is reformulated as a second order cone programming problem solved by interior‐point method. An adaptive estimation scheme is proposed to estimate the actual mean and covariance matrix of uncertain parameters. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm by comparing with the robust algorithms under worst‐case constraints and probabilistic constraints, respectively.