For formation control systems based on wireless communications, the communication interactions and control performance are highly coupled, while traditional methods do not take this coupling into account. To fill this gap, we study the formation control problem of second-order multiagent systems (MASs) with uncertain dynamics (affected by unknown but bounded (UBB) process noises) and commonly used time division multiple access (TDMA) communications. First, we establish the communication topology among agents as a periodically switching topology governed by precise switching mechanisms, which characterizes the effects of the TDMA mechanism, agent dynamics, and the physical layer of communications. Based on the communication model, we propose a control-communication codesign for MASs, where the delay-based formation controller and the distributed transmit power control algorithm are developed, to guarantee the bounded formation stability of control systems and reliable connections of the desired communication links simultaneously. The codesign framework systematically integrates formation control and communication transmission under physical layer constraints and provides a paradigm to better characterize the coupling relationship between communication interactions and control performance. Finally, experiments using realistic communication characteristics are conducted to validate the effectiveness of our codesign method.

In this work, we consider the optimality of signalling for covert sensing, where a legitimate receiver intends to estimate unknown variables based on the received signals from a transmitter, while ensuring that the probability of these signals being detected by a warden Willie is negligible. Specifically, we consider additive white Gaussian noise (AWGN) for both the estimation channel and detection channel, based on which we first reveal that Gaussian signalling is not optimal in terms of maximizing the estimation accuracy (e.g., maximizing the Fisher information) subject to a covertness constraint, e.g., guaranteeing a Kullback-Leibler divergence being no large than a specific value. To this end, we explicitly show that a skew normal distribution with an optimized skew parameter can achieve a higher estimation accuracy than a normal distribution subject to the same covertness constraint. Furthermore, we develop a framework based on calculus of variations and the Runge-Kutta method to identify the optimal signalling for covert sensing. As expected and explicitly shown in our numerical results, the identified optimal signalling outperforms both the normal and skew normal distributed signalling, which demonstrates the necessity of optimizing signalling in the context of covert sensing.

This article investigates the problem of coordinated path following for fixed-wing unmanned aerial vehicles (UAVs) with speed constraints in the two-dimensional plane. The objective is to steer a fleet of UAVs along the path(s) while achieving the desired sequenced inter-UAV arc distance. In contrast to the previous coordinated path-following studies, we are able through our proposed hybrid control law to deal with the forward speed and the angular speed constraints of fixed-wing UAVs. More specifically, the hybrid control law makes all the UAVs work at two different levels: 1) those UAVs whose path-following errors are within an invariant set (i.e., the designed coordination set) work at the coordination level and 2) the other UAVs work at the single-agent level. At the coordination level, we prove that even with speed constraints, the proposed control law can make sure the path-following errors reduce to zero, while the inter-UAV arc distances converge to the desired value. At the single-agent level, analysis for the path-following error entering the coordination set is provided. We develop a hardware-in-the-loop simulation testbed of the multi-UAV system by using actual autopilots and the X-Plane simulator. The effectiveness of the proposed approach is corroborated with both numerical simulation and the testbed.

In this article, a multilayered and distributed architecture for mission-oriented miniature fixed-wing UAV swarms is presented. Based on the concept of modularity, the proposed architecture divides the overall system into five layers: 1) low-level control layer; 2) high-level control layer; 3) coordination layer; 4) communication layer; and 5) human interaction layer, and many modules that can be viewed as black boxes with interfaces of inputs and outputs. In this way, not only the complexity of developing a large system can be reduced but also the versatility of supporting diversified missions can be ensured. Furthermore, the proposed architecture is fully distributed that each UAV performs the decision-making procedure autonomously so as to achieve better scalability. Moreover, different kinds of aerial platforms can be feasibly extended by using the control allocation matrices and the integrated hardware box. A prototype swarm system based on the proposed architecture is built and the proposed architecture is evaluated through field experiments with a scale of 21 fixed-wing UAVs. Particularly, to the best of our knowledge, this article is the first work which successfully demonstrates formation flight, target recognition, and tracking missions within an integrated architecture for fixed-wing UAV swarms through field experiments.

While Gaussian signalling is assumed in many studies on covert communications, its optimality has not been carefully investigated. In this work, we examine this optimality by considering the approach of upper bounding D(p0(y)∥p1(y)) as the covert communication constraint, where D(p0(y)∥p1(y)) is the Kullback-Leibler divergence from p0(y) to p1(y), p0(y) and p1(y) are the likelihood functions of the observation y at the warden under the null hypothesis (no covert transmission) and alternative hypothesis (a covert transmission occurs), respectively. Considering additive white Gaussian noise at both the receiver and the warden, we prove that Gaussian signalling is not optimal in terms of maximizing the mutual information of transmitted and received signals for covert communications with D(p0(y)∥p1(y)) ≤ 2ϵ2as the constraint. We also explicitly show a skew-normal signalling can outperform Gaussian signalling in terms of achieving higher mutual information subject to the same covertness constraint D(p0(y)∥p1(y)) ≤ 2ϵ2.

In this paper, we study delay-intolerant covert communications in additive white Gaussian noise (AWGN) channels with a finite block length, i.e., a finite number of channel uses. Considering the maximum allowable number of channel uses to be N, it is not immediately clear whether the actual number of channel uses, denoted by n, should be as large as N or smaller for covert communications. This is because a smaller n reduces a warden's chance to detect the communications due to fewer observations, but also reduces the chance to transmit information. We show that n = N is indeed optimal to maximize the amount of information bits that can be transmitted, subject to any covert communication constraint in terms of the warden's detection error probability. To better make use of the warden's uncertainty due to the finite block length, we also propose to use uniformly distributed random transmit power to enhance covert communications. Our examination shows that the amount of information that can be covertly transmitted logarithmically increases with the number of random power levels, which indicates that most of the benefit of using random transmit power is achieved with just a few different power levels.

In this paper, we examine the optimality of Gaussian signalling for covert communications with an upper bound on D(p1||p0) or D(p0||p1) as the covertness constraint, where D(p1||p0) and D(p0||p1) are different due to the asymmetry of Kullback-Leibler divergence, p0(y) and p1(y) are the likelihood functions of the observation y at the warden under the null hypothesis (no covert transmission) and alternative hypothesis (a covert transmission occurs), respectively. Considering additive white Gaussian noise at both the receiver and the warden, we prove that the Gaussian signalling is optimal in terms of maximizing the mutual information of transmitted and received signals for covert communications with an upper bound on D(p1||p0) as the constraint. More interestingly, we also prove that the Gaussian signalling is not optimal for covert communications with an upper bound on D(p0||p1) as the constraint, for which as we explicitly show skew-normal signalling can outperform the Gaussian signalling in terms of achieving higher mutual information. Finally, we prove that, for Gaussian signalling, an upper bound on D(p1||p0) is a tighter covertness constraint in that it leads to lower mutual information than the same upper bound on D(p0||p1), by proving D(p0||p1) ≤ D(p1||p0).

Covert communication is to achieve a reliable transmission from a transmitter to a receiver while guaranteeing an arbitrarily small probability of this transmission being detected by a warden. In this work, we study the covert communication in AWGN channels with finite blocklength, in which the number of channel uses is finite. Specifically, we analytically prove that the entire block (all available channel uses) should be utilized to maximize the effective throughput of the transmission subject to a predetermined covert requirement. This is a nontrivial result because more channel uses results in more observations at the warden for detecting the transmission. We also determine the maximum allowable transmit power per channel use, which is shown to decrease as the blocklength increases. Despite the decrease in the maximum allowable transmit power per channel use, the maximum allowable total power over the entire block is proved to increase with the blocklength, which leads to the fact that the effective throughput increases with the blocklength.

This paper studies an online algorithm for an energy harvesting transmitter, where the transmission (completion) time is considered as the system performance. Unlike the existing online algorithms, which more or less require knowledge on the future behavior of the energy-harvesting rate, we consider a practical but significantly more challenging scenario, where the energy-harvesting rate is assumed to be totally unknown. Our design is formulated as a robust-optimal control problem, which aims to optimize the worst-case performance. The transmit power is designed based only on the current battery energy level and the data queue length directly monitored by the transmitter itself. Specifically, we apply an event-trigger approach, in which the transmitter continuously monitors the battery energy and triggers an event when a significant change occurs. Once an event is triggered, the transmit power is updated according to the solution to the robust-optimal control problem, which is given in a simple analytic form. We present numerical results on the transmission time achieved by the proposed design and demonstrate its robust-optimality.

This paper studies a wireless network consisting of multiple transmitter-receiver pairs where interference is treated as noise. Previously, the throughput region of such networks was characterized for either one time slot or an infinite time horizon. We aim to fill the gap by investigating the throughput region for transmissions over a finite time horizon. Unlike the infinite-horizon throughput region, which is simply the convex hull of the throughput region of one time slot, the finite-horizon throughput region is generally non-convex. Instead of directly characterizing all achievable rate-tuples in the finite-horizon throughput region, we propose a metric termed the rate margin, which not only determines whether any given rate-tuple is within the throughput region (i.e., achievable or unachievable), but also tells the amount of scaling that can be done to the given achievable (unachievable) rate-tuple such that the resulting rate-tuple is still within (brought back into) the throughput region. Furthermore, we derive an efficient algorithm to find the rate-achieving policy for any given rate-tuple in the finite-horizon throughput region.

This paper investigates the offline packet-delay-minimization problem for an energy harvesting transmitter. To overcome the non-convexity of the problem, we propose a C2-diffeomorphic transformation and provide the necessary and sufficient condition for the transformed problem to a standard convex optimization problem. Based on this condition, a simple choice of the transformation is determined which allows an analytically tractable solution of the original non-convex problem to be easily obtained once the transformed convex problem is solved. We further study the structure of the optimal transmission policy in a special case and find it to follow a weighted-directional-water-filling structure. In particular, the optimal policy tends to allocate more power in earlier time slots and less power in later time slots. Our analytical insight is verified by simulation results.

This paper studies a wireless network consisting of multiple transmitter-receiver pairs sharing the same spectrum where interference is regarded as noise. Previously, the throughput region of such a network was characterized for either one time slot or an infinite time horizon. This work aims to close the gap by investigating the throughput region for transmissions over a finite time horizon. We derive an efficient algorithm to examine the achievability of any given rate in the finite-horizon throughput region and provide the rate-achieving policy. The computational efficiency of our algorithm comes from the use of A* search with a carefully chosen heuristic function and a tree pruning strategy. We also show that the celebrated max-weight algorithm which finds all achievable rates in the infinite-horizon throughput region fails to work for the finite-horizon throughput region.

In a mobile ad hoc network (MANET), effective prediction of time-varying interferences can enable adaptive transmission designs and therefore improve the communication performance. This paper investigates interference prediction in MANETs with a finite number of nodes by proposing and using a general-order linear model for node mobility. The proposed mobility model can well approximate node dynamics of practical MANETs. In contrast to previous studies on interference statistics, we are able through this model to give a best estimate of the time-varying interference at any time rather than long-term average effects. Specifically, we propose a compound Gaussian point process functional as a general framework to obtain analytical results on the mean value and moment-generating function of the interference prediction. With a series form of this functional, we give the necessary and sufficient condition for when the prediction is essentially equivalent to that from a binomial point process (BPP) network in the limit as time goes to infinity. These conditions permit one to rigorously determine when the commonly used BPP approximations are valid. Finally, our simulation results corroborate the effectiveness and accuracy of the analytical results on interference prediction and also show the advantages of our method in dealing with complex mobilities.

Visual guidance has attracted more and more attention in the navigation field thanks to its accuracy and robustness. This paper presents a ground-based visual guidance system for the autonomous Unmanned Aerial Vehicles (UAV) landing. The system consists of two cameras and pan-tilt units (PTU) that mounted on both sides of the runway. In this system, computer vision is adopted for UAV detection and tracking. To be more specific, triangulation, a geometric method in binocular vision, is employed to calculate the 3D coordinates of the UAV in order to provide landing guidance parameters and finally achieve autonomous UAV landing. The 3D positioning principles adopted in ground-based measurement are simulated and verified. The results show that the accuracy can be achieved and relevant requirements are satisfied by ground-based visual guidance.