Unlike static biometrics (e.g., faces and fingerprints), dynamic behavioral biometrics are believed to be more difficult to replicate. This paper investigates the security of behavioral biometrics considering the advancements in robotics and AI, particularly as humanoid robots, like Tesla Optimus, are expected to be mass-produced in the coming years. We find that general robotic arms have already gained the capability to reproduce human hand motion trajectories. However, using robots to replicate a user’s behavioral biometrics for attacks remains under-explored due to two long-standing challenges: 1) how to obtain the user’s complex behavioral biometrics through practical eavesdropping (not just trajectories); 2) how to replicate the user’s behavioral kinematics based on the eavesdropped data using a real robot. This work is the first to comprehensively address the two challenges. We develop the point-wise GAN-based Robot Replay Attack (GANRRA) to demonstrate a practical human behavioral replay attack using a hidden camera and a physical robot. GANRRA utilizes a hidden camera to eavesdrop on the user’s hand motions and employs a generative adversarial network to reconstruct the motion data, addressing the sensor discrepancies between the legitimate sensor and the hidden camera and maximizing the behavioral feature similarities. The reconstructed motion data is converted into velocity commands for a robot to execute point by point, replicating both hand movement trajectories and behavioral biometric features. For experiments, we implement an in-air signature system using two existing hand-tracking systems and fool them using a robotic arm attached with a fake hand. Results show that GANRRA reproduces in-air signatures with a 73.1% success rate. To address such robot-relay threats, a novel defense mechanism based on multi-joint behaviors is proposed.

Teleoperated robotics, which translates human behavior into robotic actions, remains a critical area of modern robotics. Although autonomous systems have advanced rapidly, they still struggle in complex and unstructured environments, making human-in-the-loop control indispensable for many real-world tasks. Teleoperation platforms commonly rely on motion-tracking technologies to capture detailed operator behavior, which is subsequently converted into robot control commands. However, these rich behavioral signals can also encode operator-specific biometrics, posing privacy risks such as user re-identification. While prior work shows that behavioral biometrics can be leveraged for reliable authentication, privacy leakage in teleoperation-centric motion streams has received comparatively less attention. To address this gap, we introduce a disentangled representation-learning framework based on a Variational Autoencoder (VAE) to suppress identity-revealing cues while retaining task-relevant motion patterns. We evaluate the proposed approach offline on reconstructed trajectories collected from a tele-robotic prototype, where multiple users perform a set of manipulation tasks. Our results demonstrate a substantial reduction in re-identification risk and a favorable privacy–utility trade-off in terms of task utility. More broadly, our findings highlight the need for robust privacy protections in future robotic teleoperation systems.

This paper explores the growing need for task-oriented communications in warehouse logistics, where traditional communication Key Performance Indicators (KPIs)-such as latency, reliability, and throughput-often do not fully meet task requirements. As the complexity of data flow management in large-scale device networks increases, there is also a pressing need for innovative cross-system designs that balance data compression, communication, and computation. To address these chal-lenges, we propose a task-oriented, edge-assisted framework for cooperative data compression, communication, and computing in Unmanned Ground Vehicle (UGV)-enhanced warehouse logistics. In this framework, two UGVs collaborate to transport cargo, with control functions-navigation for the front UGV and following/conveyance for the rear UGV-offloaded to the edge server to accommodate their limited on-board computing resources. We develop a Deep Reinforcement Learning (DRL)-based two-stage point cloud data compression algorithm that dynamically and collaboratively adjusts compression ratios according to task requirements, significantly reducing communication overhead. System-level simulations of our UGV logistics prototype demon-strate the framework's effectiveness and its potential for swift real-world implementation.

The integration of humanoid and animal-shaped robots into specialized domains, such as healthcare, multiterrain operations, and psychotherapy, necessitates a deep understanding of proxemics-the study of spatial behavior that governs effective human-robot interactions. Unlike traditional robots in manufacturing or logistics, these robots must navigate complex human environments where maintaining appropriate physical and psychological distances is crucial for seamless interaction. This study explores the application of proxemics in human-robot interactions, focusing specifically on quadruped robots, which present unique challenges and opportunities due to their lifelike movement and form. Utilizing a motion capture system, we examine how different interaction postures of a canine robot influence human participants' proxemic behavior in dynamic scenarios. By capturing and analyzing position and orientation data, this research aims to identify key factors that affect proxemic distances and inform the design of socially acceptable robots. The findings underscore the importance of adhering to human psychological and physical distancing norms in robot design, ensuring that autonomous systems can coexist harmoniously with humans.

This paper proposes a task-oriented co-design framework that integrates communication, computing, and control to address the key challenges of bandwidth limitations, noise interference, and latency in mission-critical industrial Cyber-Physical Systems (CPS). To improve communication efficiency and robustness, we design a task-oriented Joint Source-Channel Coding (JSCC) using Information Bottleneck (IB) to enhance data transmission efficiency by prioritizing task-specific information. To mitigate the perceived End-to-End (E2E) delays, we develop a Delay-Aware Trajectory-Guided Control Prediction (DTCP) strategy that integrates trajectory planning with control prediction, predicting commands based on E2E delay. Moreover, the DTCP is co-designed with task-oriented JSCC, focusing on transmitting task-specific information for timely and reliable autonomous driving. Experimental results in the CARLA simulator demonstrate that, under an E2E delay of 1 second (20 time slots), the proposed framework achieves a driving score of 48.12, which is 31.59 points higher than using Better Portable Graphics (BPG) while reducing bandwidth usage by 99.19%.

Existing communication systems aim to reconstruct the information at the receiver side, and are known as reconstruction-oriented communications. This approach often falls short in meeting the real-time, task-specific demands of modern AI-driven applications such as autonomous driving and semantic segmentation. As a new design principle, task-oriented communications have been developed. However, it typically requires joint optimization of encoder, decoder, and modified inference neural networks, resulting in extensive cross-system redesigns and compatibility issues. This paper proposes a novel communication framework that aligns reconstruction-oriented and task-oriented communications for edge intelligence. The idea is to extend the Information Bottleneck (IB) theory to optimize data transmission by minimizing task-relevant loss function, while maintaining the structure of the original data by an information reshaper. Such an approach integrates taskoriented communications with reconstruction-oriented communications, where a variational approach is designed to handle the intractability of mutual information in high-dimensional neural network features. We also introduce a joint source-channel coding (JSCC) modulation scheme compatible with classical modulation techniques, enabling the deployment of AI technologies within existing digital infrastructures. The proposed framework is particularly effective in edge-based autonomous driving scenarios. Our evaluation in the Car Learning to Act (CARLA) simulator demonstrates that the proposed framework significantly reduces bits per service by 99.19% compared to existing methods, such as JPEG, JPEG2000, and BPG, without compromising the effectiveness of task execution.

Teleoperation can be very difficult due to limited perception, high communication latency, and limited degrees of freedom (DoFs) at the operator side. Autonomous teleoperation is proposed to overcome this difficulty by predicting user intentions and performing some parts of the task autonomously to decrease the demand on the operator and increase the task completion rate. However, decision-making for mode-switching is generally assumed to be done by the operator, which brings an extra DoF to be controlled by the operator and introduces extra mental demand. On the other hand, the communication perspective is not investigated in the current literature, although communication imperfections and resource limitations are the main bottlenecks for teleoperation. In this study, we propose an intelligent mode-switching framework by jointly considering mode-switching and communication systems. User intention recognition is done at the operator side. Based on user intention recognition, a deep reinforcement learning (DRL) agent is trained and deployed at the operator side to seamlessly switch between autonomous and teleoperation modes. A real-world data set is collected from our teleoperation testbed to train both user intention recognition and DRL algorithms. Our results show that the proposed framework can achieve up to 50% communication load reduction with improved task completion probability.

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization (C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization (PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk (CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error.

Future robots will be very advanced with high flexibility and accurate control performance. They will have the ability to mimic human behaviours or even perform better, which raises the significant risk of robot attack. In this work, we study the robot mimic attack on the current keystroke-dynamic user authentication system. Specifically, we proposed a robot mimicry attack framework for keystroke-dynamics systems. We collected keyboard logging data and acoustical signal data from real users and extracted the timing pattern of keystrokes to understand victim's behaviour for robot imitation attacks. Furthermore, we develop a deep Q-Network (DQN) algorithm to control the velocity of robot which is one of the key challenges of forging the human typing timing features. We tested and evaluated our approach on the real-life robotic testbed. We presented our results considering user identification and user authentication performance. We achieved a 90.3% user identification accuracy with genuine keyboard logging data samples and 89.6% accuracy with robot-forged data samples. Furthermore, we achieved 11.1%, and 36.6% EER for user authentication performance with zero-effort attack, and robot mimicry attack, respectively.

The metaverse has the potential to revolutionize the next generation of the Internet by supporting highly interactive services with satisfactory user experience. The synchronization between devices in the physical world and their digital models in the metaverse is crucial. This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on the tracking error. To optimize the sampling rate and the prediction horizon, we exploit expert knowledge and develop a constrained deep reinforcement learning algorithm. We validate our framework on a prototype composed of a real-world robotic arm and its digital model. The results show that our framework achieves a better trade-off between the average tracking error and the average communication load compared with a communication system without sampling and prediction. For example, the average communication load can be reduced up to 87% when the average track error constraint is 0.007°. In addition, our policy outperforms the benchmark with the static sampling rate and prediction horizon optimized by exhaustive search, in terms of the tail probability of the tracking error. Furthermore, with the assistance of expert knowledge, the proposed algorithm achieves better convergence time, stability, communication load, and average tacking error.

As drones become widely used in different applications, drone authentication becomes increasingly important due to various security risks, e.g., drone impersonation attacks. In this paper, we propose an idea of drone authentication based on Mel-frequency cepstral coefficient (MFCC) using an acoustic fingerprint that is physically embedded in each drone. We also point out that the uniqueness of the drone’s sound comes from the combination of bodies (motors) and propellers. In the experiment with 8 drones, we compare the authentication accuracy of different feature extraction settings. Three kinds of different sound features are used: MFCC, delta MFCC (DMFCC), and delta-delta MFCC (DDMFCC). We choose the feature extraction settings and the sound features according to the best authentication result. In the experiment with 24 drones, we compare the closed set authentication performance of eight machine learning methods in terms of recall under the influence of additive white Gaussian noise (AWGN) with different levels of signal-to-noise ratio (SNR). Furthermore, we conduct an open set drone authentication experiment. Our results show that Quadratic Discriminant Analysis (QDA) outperforms other methods in terms of the highest average recall (94.19%) in the authentication of registered drones and the third highest average recall (82.35%) in the authentication of unregistered drones.

Motion-controlled robotic arms allow a user to interact with a remote real world without physically reaching it. By connecting cyberspace to the physical world, such interactive teleoperations are promising to improve remote education, virtual social interactions, and online participatory activities. In this work, we build up a motion-controlled robotic arm framework comprising a robotic arm end and a user end, which are connected via a network and responsible for manipulator control and motion capture, respectively. To protect the system access, we propose to verify who is controlling the robotic arm by examining the robotic arm’s behavior, which adds a second security layer in addition to the system login credentials. We show that a robotic arm’s motion inherits its human controller’s behavioral biometric in interactive control scenarios. By extracting the angle readings of the robotic arm’s all joints, the proposed user authentication approach reconstructs the robotic arm’s end-effector movement trajectory that follows the user’s hand. Furthermore, we derive the unique robotic motion features to capture the user’s behavioral biometric embedded in the robot motions and develop learning-based algorithms to verify the robotic arm user to be one of the enrolled users or a nonuser. Extensive experiments show that our system achieves 94% accuracy to distinguish users while preventing user identity spoofing attacks with 95% accuracy.

Motion-controlled robots allow a user to interact with a remote real world without physically reaching it. By connecting cyberspace to the physical world, such interactive teleoperations are promising to improve remote education, virtual social interactions and online participatory activities. This work builds up a motion-controlled robotic arm framework and proposes to verify who is controlling the robotic arm by examining the robotic arm's behavior. We show that a robotic arm's motion inherits its human controller's behavioral biometric in interactive control scenarios. Furthermore, we derive the unique robotic motion features to capture the user's behavioral biometric embedded in the robot motions and develop learning-based algorithms to verify the robotic arm user. Extensive experiments show that our system achieves high accuracy to distinguish users while using the robot's behaviors.

In real-time wireless control systems, the freshness of information is crucial since performance highly depends on timely exchange of information between the plant and the controller. In this study, the age metric, Age of Control Process (AoCP), is proposed for real-time wireless control systems which is different from traditional Age of Information (AoI). The First Generate First Serve (FGFS) M/M/1/1 → M/M/1/1 tandem queue model is considered to present a closed form expression for computation of AoCP and testbed environment is employed for the analysis of AoCP measure. Our experiment results show that FGFS M/M/1/1 → M/M/1/1 queuing model is suitable to represent real-time control systems. In addition, the proposed definition provides closer results to the experiment results when compared with the traditional AoI definition. To the best of our knowledge, this is the first study that conducts information freshness measurements on real-time control testbed.

Fueled by ubiquitous connectivity, packets are expected to be timely updated to the controller of interest in real-time control systems. Recently, the Age of Information (AoI) becomes a popular metric to quantify the packet importance, which improves the efficiency of communication resource utilization. However, it is analyzed only from the information freshness perspective, losing sight of considering feature data. In light of this, we first establish a feature-oriented teleoperation framework to quantify the packet importance, derived from the information bottleneck principle. Under this framework, a packet management method is proposed to increase the average feature quantity of the receiver. Finally, we build a prototype to deploy the proposed method, and the results show superiority in reducing both the communication traffic and the control error.

In industrial internet of things (IIoT), ultra-reliable and low-latency communication (URLLC) is proposed to guarantee the requirement of real-time wireless control systems in worst case, so as to maintain the system working in all cases. However, it is extremely challenging to maintain URLLC throughout the whole control process due to the scarcity of wireless resource. This paper develops an autonomous device-to-device (D2D) communication scheme by jointly considering reliability in URLLC and control requirement. In the proposed scheme, we consider the actual control requirement, i.e., control convergence rate, into communication design, where we find that it can be converted into a constraint on communication reliability. Then, the communication reliability constraint comes from control aspect, instead of URLLC, which leads to that the system does not need to guarantee worst case in URLLC. Second, the sensors autonomously decide whether to be activated with optimal probabilities to participate in the control process, which can maintain the communication reliability requirement with significantly less resource consumption. Simulation results show remarkable performance gain of our method. For instance, compared with fixed activation probability 40% only considering URLLC, the average power consumption of the proposed method can be reduced by at most about 100%.

In real-time feedback control systems, control performance, e.g., control cost and tracking error, is significantly affected by information freshness, which in turn relies heavily on the design of the feedback update policy. In this paper, we study the update policy for real-time feedback control systems. We first discuss the relationship between the age of information (AOI) and control performance, where AOI represents the level of "dissatisfaction" for information staleness. We find that minimizing AOI is not always equivalent to optimizing the control performance. Then, a new metric, called the value of information (VOI), is proposed to evaluate the timeliness of system update by linking AOI to the decay rate of the control system. By maximizing VOI, we design a new update policy, called the α - wait, which has superiorities in improving both control performance and communication cost. Finally, simulation results verify our method.

The coming fifth-generation (5G) cellular networks encourage to support several innovations and services, some of which will demand Ultra-reliable and Low-latency Communications (URLLC). For instance, URLLC can support real-time control to facilitate several emerging applications, such as robotic arms in industrial applications, and remote surgery for healthcare applications. However, URLLC is expected to be supported without considering the resources usage efficiency in wireless control systems due to the challenging to satisfy Quality of Service (QoS) requirements at the expense of diminishing energy efficiency. In this paper, we analyze uplink energy efficiency in URLLC utilizing multiple antennas in the transmitter and the receiver as well (MIMO) in real-time wireless control systems. We firstly formulate an optimization problem to maximize energy efficiency concerning the effect of the control convergence rate constraint. Then, we develop an exhaustive search method to obtain the maximum energy efficiency. Finally, simulation results are provided to demonstrate the performance of our proposed method.

In real-time feedback control systems, the freshness of the packet is crucial to control performance, where packet management is vital to keep data fresh. Recently, age of information (AOI) has been used to measure the freshness of the update information, where minimizing AOI becomes popular in system designs. In this paper, we find that minimizing AOI is not always equivalent to maximizing the control performance. In particular, we define a metric, called the age of stale information (AOSI), to link the instability of the control system to AOI. By minimizing AOSI, we can maximize the control performance, and also reduce the communication cost.

Ultra-reliable low-latency communication (URLLC) is critical to wireless control systems. In this paper, we propose a communication-control co-design method to deal with stringent reliability requirements. In particular, we adopt relay-assisted method to improve communication reliability. More importantly, packetized predictive control (PPC) is adopted to solve the problem of relay system, where the data transmission rate of the relay link is twice as that of the direct link, and this leads to the packet loss probability increasing. Furthermore, we explore the tradeoff between energy consumption of relay and packet length of PPC to maximize reliability. Simulation results verify the performance of the proposed method.

Ultra-reliable and low-latency communication (URLLC) is promising to enable real-time wireless control systems for tactile internet. In such a system, it is difficult to maintain extremely high quality-of-service (QoS) in URLLC for real-time control. In this paper, we develop a probability-based device-to-device (D2D) scheme to deal with this issue, where communication and control are jointly considered. In our scheme, the transmitters autonomously decide whether to be active to participate in the control process of the receiver based on a certain probability, which can significant reduce the interacting communication latency between them, lower the transmission power consumption, and improve communication reliability. Compared with traditional D2D transmission method, simulation results show remarkable performance gain of our method.

As one of the most important communication scenarios in the comingfifth generation (5G) cellular networks, ultra-reliable and low-latency communication (URLLC) is promising to enable real-time wireless control systems. However, one of the biggest challenges is that how to integrate URLLC and control performance together to maximize the overall system performance. In this paper, we investigate the resource allocation for URLLC uplink in real-time wireless control systems. Specifically, we first discuss the relationship between communication and control performance. Based on that, we convert the hybrid co-design problem into a regular wireless resource allocation problem. Then, we propose an iteration algorithm to obtain the optimal wireless resource allocation. Simulation results indicate the performance of our method.

Packetized predictive control (PPC) is an effective solution to ensure the robustness of the control system over unreliable wireless links. However, conventional wireless transmission methods in PPC suffer from either high wireless resource consumption or poor performance of real-time control due to the separately design of the two parts. To deal with the issue, we propose a communication-control co-design approach to achieve good trade-off between real-time control performance and communication energy efficiency. Our results demonstrate the advantages of the communication-control co-design.

In this paper, we establish a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for mission-critical Internet-of-Things (MC-IoT) services with short packets in mobile edge computing (MEC) systems, where enhanced mobile broadband (eMBB) services with long packets are considered as background services. To reduce communication delay, the fifth generation new radio is adopted in radio access networks. To avoid long queueing delay for short packets from MC-IoT, processor-sharing (PS) servers are deployed at MEC systems, where the service rate of the server is equally allocated to all the packets in the buffer. We derive the distribution of latency experienced by short packets in closed form, and minimize the overall packet loss probability subject to the end-to-end delay requirement. To solve the nonconvex optimization problem, we propose an algorithm that converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios. Furthermore, we derive the optimal solutions in two asymptotic cases: communication or computing is the bottleneck of reliability. The simulation and numerical results validate our analysis and show that the PS server outperforms first-come-first-serve servers.

Co-channel interference is one of the most important issue in cache-enabled device-to-device (D2D) wireless networks. In this paper, we propose a D2D link scheduling algorithm to manage the interference, called Max-SIR scheduling algorithm. It consists of two steps, i.e., link scheduling and link removal. In the algorithm, D2D links are scheduled with the maximum signal-to-interference ratios (SIRs) considering the co-channel interference in a cell. Simulation results show that the proposed algorithm outperforms the existing ones in terms of the system throughput and the number of scheduled D2D links.

In this paper, we establish a comprehensive uplink system model for in-band and guard-band Narrowband Internet of Things (NB-IoT) with arbitrary sample duration in the NB-IoT device. The mathematical expressions of received LTE and NB-IoT signals are derived. Moreover, the close-form interference power on the LTE signal from the adjacent NB-IoT signal is given analytically. The result shows that the sample duration of NB-IoT device has significant impact on its desired signal and on the interference to the LTE user equipment (UE). Numerical results show that the analytical expressions match the simulated ones perfectly, which verifies the effectiveness of proposed system model and derivations. The work in this paper provides a valid guidance for NB-IoT system deployment and co-existence analysis.

In this paper, we consider cache-enabled device-to- device (D2D) wireless networks and propose a content-centric link scheduling method, called CTLinQ, to maximize the number of overall D2D links that can be simultaneously activated. Then, interference among different D2D links can be reduced and the quality-of-service (QoS) of each activated link can be guaranteed. Simulation results show that the proposed method outperforms the existing ones in terms of power consumption, the number of activated links, and overall system throughput in particular in high signal-to- interference-plus-noise ratio (SINR) region.

In device-to-device (D2D) networks, co-channel interference is one of the main reasons that causes high power consumption, which further reduces the life time of mobile devices. In this paper, we adopt the superposition coding between macro and D2D users, which is expected to effectively eliminate the co-channel interference. In particular, we develop two power allocation methods to minimize the power consumption in cooperative and non-cooperative modes, respectively. Then, we use mode selection to obtain the minimum overall power consumption of the whole system. In power allocation, we model the average power consumption as a function of channel gain, power allocation factor, transmission rate, and noise power. Then, we obtain the close-form solution. Our results indicate that the proposed method outperforms the conventional non-cooperative methods in terms of power consumption, outage probability, and energy efficiency.

Ultra-reliability and low-latency will be two topics of most concern in the wireless communication to realize the industrial automation during the next few decades. In this paper, we consider a uplink transmission model where massive machine-type-communication (MTC) devices aim to transmit their generated data to the BS under a strict Quality-of-Service (QoS) constraint on reliability and latency. Guaranteeing the strict constraints on transmission reliability and latency is the most significant thing we consider during the data transmission process. The achievable rate based on finite blocklength channel coding is adopted to characterized the decoding reliability at BS. We see the delay-sensitive area spectral efficiency (DASE) as a performance metric, and our goal is to find a optimal resource allocation policy to maximize the DASE while guaranteeing the strict QoS constraint on reliability. Numerical results show that the optimization strategy proposed in this paper can improve the DASE performance to a great extent than the optimization strategy which minimizes the total bandwidths allocated to devices.

In this paper, we consider wireless caching in device-to-device (D2D) cooperative networks and propose a new definition,energy-consumption-ratio (ECR), to measure the energy efficiency that caching schemes can achieve. Based on this definition,we formulate an optimal energy-efficient caching problem and develop a caching scheme, named SBRC-2S. Simulation results show that the proposed scheme outperforms the existing ones and approaches the optimal performance bounds in terms of energy efficiency.

In an underlay cognitive radio network, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-channel gain. In particular, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the instantaneous cross-channel gain by observing the power adaption. Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of art, we show the advantages of the proposed proactive estimator.

In cognitive radio networks, the channel gain between primary transceivers, namely, primary channel gain, is crucial for a cognitive transmitter (CT) to control the transmit power and realize spectrum sharing. To obtain the primary channel gain, a backhaul between the primary system and the CT is needed. However, the backhaul is usually unavailable in practice. To deal with this issue, the CT is enabled to sense primary signals and estimate the primary channel gain in this paper. In particular, two estimators, namely, a high-complexity maximum likelihood (ML) estimator and a low-complexity median based (MB) estimator are proposed. Numerical results show that the ML estimator outperforms the MB estimator in terms of the accuracy if the signal to noise ratio (SNR) of the received primary signals at the CT is no smaller than 4 dB. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and accuracy.

In cognitive radio networks, the channel gain between primary transceivers, namely, primary channel gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary channel gain is estimated in the primary system, and thus unavailable at the CT. To deal with the issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signal, an ML estimator is first developed. To reduce the computational complexity of the ML estimator, a median-based (MB) estimator is then proposed. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Simulation results show that the estimation errors of both estimators can be as small as 0.015. Meanwhile, the ML estimator outperforms the MB estimator in terms of the estimation accuracy if the sensed primary signal at the CT is weak. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and the estimation accuracy.

In this paper, we propose a new estimation method to obtain the cross-channel gain, which avoids the severe interference to the primary receiver (PR) in existing relay-assisted estimation methods. In our method, we let the cognitive transmitter add a time delay when it conducts the full-duplex amplify-and-forward relaying. This forces the time-difference-of-arrival (TDOA) between the direct and relay signals to be large enough rather than randomly large or small. Then we develop our estimation method only in the large TDOA case and precisely control the interference to the PR. Simulation results indicate that the proposed method can significantly reduce the interference to the PR.

In frequency duplex division systems, it is very challenging to position the receiver (Rx) that belongs to the third-party system since the anchors usually do not know which frequency band the Rx uses for transmission. As a result, they can only use the received signal from the third-party transmitter (Tx) to position the Rx. In this paper, we propose a relay-based positioning method to estimate the location of the third-party Rx. In our method, we let each anchor alternatively act as a full-duplex amplify-and-forward (AF) relay for the Rx, which artificially creates a relay path. By estimating the time-difference-of-arrival (TDOA) between the direct and relay paths, the anchors can estimate the Rx location based on the received signal from the third-party Tx. Simulation results indicate the effectiveness of the proposed method.

In frequency division duplex (FDD) two-tier networks, the probing technique has recently been introduced into the cross-channel gain estimation, which requires the tier-two user to act as a relay for the tier-one user. Then the tier-two user can autonomously estimate the cross-channel gain. However, the improper location of the tier-two user, i.e., the relay, may cause severe interference to the tier-one user. In this paper, we analyze the impacts of the probing on the tier-one user and find two location regions, in which the probing does not cause interference. Based on that, we develop a detection method to let the tier-two user autonomously identify its located region. Then the interference caused by the probing can be avoided. Simulation results demonstrate that the proposed method has about 90% correct detection probability.

In cognitive radio networks, the location information of the primary receiver is critical for underlay spectrum sharing. However, positioning the primary receiver in frequency division duplex (FDD) systems is very challenging. In this paper, we propose a novel method to position the primary receiver using the full-duplex amplified-and-forward (AF) relay technique. Simulation results indicate that the proposed method can obtain the same level of estimation error compared with the conventional received signal strength (RSS)-based transmitter positioning methods.

In two-phase cooperative multicast communications, the unbalanced outage probabilities of the cell-center and cell-edge users are the main reason that caps the energy-efficiency of the system. In this paper, we consider the unbalanced outage probability and propose a probability-based relay selection and power control method to improve the energy-efficiency, in which each user can autonomously decide whether to participate in the relay transmission. In particular, we obtain the optimal solution that can minimize the user power consumption. In addition, since our method works in a distributed manner, it does not require any feedback either between the BS and users, or among the users. This saves the extra energy consumption caused by the feedback. Simulation results demonstrate that the proposed method can reduce the user energy consumption up to 54%.

In an underlay cognitive radio network, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-channel gain. Specifically, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the cross-channel gain by observing the power adaption. To demonstrate the accuracy of the estimation, we analytically characterize both an upper bound and a lower bound of the estimation performance. Furthermore, we study the impact of CT's relaying on the primary transmission and observe that the impact is related to the CT's location. By introducing a factor φ (0 ≤ φ ≤ 1) to denote the probability that the CT's relaying improves the primary transmission instead of causes interference, we design the CT location as a function of φ. Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of the art, we show the advantages of the proposed estimator.

In cognitive radio networks, the signal-to-noise ratio (SNR) of the primary receiver (PR), called primary-link SNR, is critical for underlay spectrum sharing. However, it is very difficult for the cognitive user (CU) to obtain the primary-link SNR. In this letter, we propose a new method to let the CU autonomously estimate the primary-link SNR, where the full-duplex relay technique is used. Then, the CU can conduct the underlay spectrum sharing more efficiently. Simulation results indicate the effectiveness of the proposed method.

In cognitive radio networks, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is critical for spectrum sharing and obtaining the cross-channel gain is very difficult. Even though proactive estimation allows the CT to autonomously estimate the cross-channel gain, it may cause severe interference to the PR. This raises a new issue for spectrum sensing, called spectrum sensing interference. In this paper, we deal with the sensing interference and propose a relay-assisted method to conduct the proactive estimation, which obtains the cross-channel gain with much less interference to the PR. In our method, we let the CT act as a full-duplex amplify-and-forward relay to probe the close-loop power control between primary transceivers. By measuring the power adjustment of the primary signal, the CT estimates the cross-channel gain. Simulation results indicate that our method can reduce the sensing interference to an extremely low level.

In this paper, we propose a probability-based relay selection and power control method in two-phase cooperative multicast communications to minimize the user power consumption for any given multicast data rate. Our method gives each user the ability to autonomously decide whether to participate in the relay transmission based on an active probability. In particular, we develop an optimal algorithm to calculate the optimal active probability and relay power by balancing the outage reduction efficiency. We also develop a sub-optimal algorithm by balancing the outage probability of the cell-center and the cell-edge users. Simulation results demonstrate that the proposed optimal and sub-optimal algorithms can reduce the user power consumption up to about 54% and 40%, respectively, compared with the conventional algorithm that requires all users to participate in the relay transmission.

In wireless positioning, estimating the location of a receiver is very challenging since the receiver does not transmit signals. The difficulty is to obtain the distance between the anchors and the silent receiver. To deal with the issue, this paper proposes to use the full-duplex relay technique to estimate the anchor-receiver distance. Then, we can obtain the location of the silent receiver. With the proposed method, the location-aware applications are not limited to the conventional transmitter positioning. Instead, they can be extended to the receiver positioning. Simulation results demonstrate the performance of the proposed method.

In this paper, we investigate the two-phase cooperative multicast communication, where a base station broadcasts the same information to a group of users in the first phase and then some successful users act as relay to help the failed users in the second phase. To maximize the energy efficiency of the cooperative multicast communication, we propose a probability-based relay selection and power control method. With our method, each successful user may autonomously decide whether to conduct the relay for the failed users and also select the proper transmission power for the relay transmission. In particular, the proposed method does not require any handshake either between base station and users, or among users. Simulation results demonstrate that our method obtains almost the same performance as the optimal relay selection and power control method with exhaustive search.

Recently, a proactive estimation method has been proposed to obtain the cross-channel gain from cognitive transmitter (CT) to primary receiver (PR), where CT acts as a full-duplex amplified-and-forward (AF) relay for primary users. However, since the cognitive and primary users usually have no cooperation, it has no guarantee that the direct and relay signals are synchronized, i.e, the time delay between direct and relay signals may be randomly large or small. This may cause interference to PR. In this paper, we analyze the impacts of the small and large time delays on proactive estimation, and propose an interference-free method to estimate the cross-channel gain. Simulation results demonstrate that the proposed method obtains about 5% estimation error with about 99% interference-free probability.

In this letter, we propose a passive detection method to enable cognitive user to detect active primary receiver. With our method, cognitive user may conduct underlay spectrum sharing without the need of channel state information between primary and cognitive users. Simulation results show that our detector can provide about 100% to 300% more access probabilities in average than conventional energy detector.

In this paper, we develop a new proactive estimation method to obtain the cross-channel gain from cognitive transmitter to primary receiver without any backhaul between cognitive and primary users. In conventional proactive methods, the jamming signal is used for probing, which introduces the extra interference to primary receivers. In our method, the relayed primary signal is used for probing, which instead assists the primary transmission. Simulation results demonstrate that the proposed method with 2% estimation errors can obtain up to about 72% throughput improvement introduced by the cross-channel gain.

In this paper, we will propose a novel method to estimate the cross channel gain between cognitive transmitter to primary receiver as well as the primary channel gain between primary transceivers, where the cognitive user is acting as a relay to proactively trigger the primary link adaptation. But, this kind of estimator may obtain two possible estimations for each channel, which may confuse the cognitive user. Thus, we will further develop a selection method to pick the estimation with less errors. Simulation results show that the proposed method can effectively improve the estimation performance.

In cognitive radio systems, the channel gains between primary users (PUs) and cognitive users (CUs) and that between PUs are critical for the coexistence of CUs and PUs. In this paper, we propose a proactive channel gain estimation approach by using the received primary signal for probing, which obtains both kinds of channel gains without information exchange between CUs and PUs. In average, the probing in our proactive approach does not introduce interference to PUs while conventional ones usually do. Simulation results show that the relative estimation errors of the proposed approach are below 0.02 with a proper CU location, where the channels suffer path loss and shadowing, and their gains range from about -120 dB to about -50 dB.

In this paper, we propose a cognitive interference management approach, called fractional frequency donation, to alleviate co-channel interference among selfish femtocells. In such networks, our approach allows each femtocell to access all available bands but requires good femtocells with high throughput to "donate" some bands to poor ones. When the donors and the corresponding donated bands are properly selected, both good performance on average- and 5% edge-throughputs can be achieved. Simulation results show that in femtocell networks, the proposed fractional frequency donation approach is more suitable than the conventional fractional frequency reuse ones.

In this paper, we investigate channel allocation for cooperative relays in cognitive radio networks. Different from conventional cooperative relay channels, cognitive radio relay channels are actually a combination of three kinds of channels: direct, dual-hop, and relay channels, which belong to different spectrum bands and provide parallel end-to-end transmission. In order to maximize the achievable end-to-end throughput, we propose two channel allocation approaches with different complexities to assign all the channels cooperatively. Numerical results illustrate the performance improvement in different number of available channels. In particular, it has about 40% improvement in throughput when the average SNR is 15 dB and eight available channels are used.

Most of existing works on spectrum sensing detect primary transmitters while the purpose of spectrum sensing is to avoid interfering with primary receivers (PRs). Therefore, it is more important to detect PRs. In this paper, we propose a proactive spectrum sensing method that detects whether a PR is within the coverage or the interference range of a CR transmitter by exploiting the close-loop power control policy in primary systems. With the proposed scheme, the CR user may still access the spectrum band even though a primary signal is present as long as its transmission does not interfere with the PR. Simulation results show the advantages of the proposed method.

In this paper, we propose a relay-assisted transmission scheme in cognitive radio (CR) to exploit spatial spectrum holes, which are generated by relay techniques. The proposed scheme enables CR users to coexist with primary users at the same time in the same geographic area and spectrum band. Compared to conventional schemes, a higher spectrum efficiency is achieved by our method. We further analyze the successful communication probability and present numerical results to show advantages of our method.

Spectrum sensing finds spectrum opportunities for cognitive radio (CR) and enables CR users to work without harmful interference to primary users. Most of existing contributions on spectrum sensing detect whether a primary signal is present or absent. Since the ultimate goal of spectrum sensing is to avoid interfering with primary receivers (PRs), it is more efficient to detect PRs directly. In this paper, we propose a proactive spectrum sensing scheme to detect whether a PR is within the coverage or the interference range of a CR transmitter by exploiting the close-loop power control that has been widely used in wireless systems. With the proposed scheme, the CR user may still be able to access the licensed spectrum band even though a primary signal is detected as long as its transmission does not interfere with the PR. As a result, more spectrum opportunities can be exploited compared to conventional spectrum sensing methods.

Spectrum hole (SH) is defined as a spectrum band that can be utilized by unlicensed users, which is a basic resource for cognitive radio (CR) systems. Most of existing contributions detect SHs by sensing whether a primary signal is present or absent and then try to access them so that the CR and primary users use the spectrum band either at different time slots or in different geographic regions. In this paper, we propose a novel scheme with relays or directional relays for CR users to exploit new spectrum opportunity, called spatial SH. It can provide higher spectrum efficiency by coexistence of primary and CR users at the same region, time, and spectrum band. In particular, when the spectrum opportunity of a direct link from a CR transmitter to a CR receiver does not appear, our scheme may still establish the communication through indirect links, i.e., other CR users act as relay stations to assist the communication by using other spatial domains. Furthermore, we analyze the successful communication probabilities of CR users and demonstrate that the spectrum efficiency can be considerably improved by our scheme.

In this paper, we propose a cognitive radio (CR) transmission scheme, which enables secondary users to coexist with primary users by exploiting spatial spectrum holes (SSHs) through directional antennas or antenna arrays with beamforming. To ensure reliable CR links and avoid interference to primary users, some CR users may act as relays . We investigate successful communication probability of CR users when this scheme is applied. We further demonstrate that the spectrum efficiency can be greatly improved by multiplexing CR links with directional transmission.

In this letter, we consider cooperative spectrum sensing based on energy detection in cognitive radio networks. Soft combination of the observed energies from different cognitive radio users is investigated. Based on the Neyman-Pearson criterion, we obtain an optimal soft combination scheme that maximizes the detection probability for a given false alarm probability. Encouraged by the performance gain of soft combination, we further propose a new softened hard combination scheme with two-bit overhead for each user and achieve a good tradeoff between detection performance and complexity.