The focus of this study is a joint analysis of the stability of formation control and the capacity of wireless communications between agents moving in a formation. The main results derived in this study show a connection between the second moment stability/convergence of formation control and the expected capacity of the data communications for multiagent systems in which the collective behavior of the multiple agents is dominated by formation control with random errors. More specifically, by developing a joint control-communication framework for the performance analysis, we investigate whether random errors in the formation control have a destructive influence on the data communications between agents. We prove that the expected capacity of the multiagent data communication link has a nonvanishing lower bound that can be evaluated through second moment performance analysis. The results from simulations indicate that the theoretical analysis provides reasonable lower bounds for different system parameters.

Unmanned Aerial Vehicle (UAV) based communication is a promising technology for offering wireless communication services. UAVs can typically be adopted as aerial base stations to collect data from ground Internet of Things (IoT) devices. However, the Ground-to-Air link of UAV-based IoT communications is prone to be interfered by surrounding WiFi devices in the Industrial, Scientific, Medical (ISM) band. This paper aims to enable reliable uplink communications for IoT devices in 5 GHz ISM bands, thereby reducing the hover time and energy consumption of the UAV. To this end, we first study and design suppression frames suitable for WiFi devices. Then a suppression method is proposed, which is shown to be effective for all the tested devices, based on emulation performance verification of the frames for multiple commercial off-the-shelf (COTS) WiFi devices. Moreover, by taking the energy consumption of the UAV and multiple transmission parameters of the suppression method into account, we formulate an optimization problem for the desired uplink, which is designed to minimize the energy consumption of the UAV, under the constraints of satisfying the link channel capacity. Given that the optimization problem is non-convex mixed-integer nonlinear programming, this paper proposes an efficient algorithm that takes advantage of the suppression performance of the utilized frames. Numerical simulations demonstrate the performance of the proposed algorithm in terms of the WiFi device density, UAV weight, channel capacity requirement and parameters of the suppression method. The communication quality of the desired Ground-to-Air link is shown to be significantly improved, while the consumed energy is demonstrated to be obviously reduced.

Spectrum sensing is essential for cognitive radio, while wideband spectrum sensing (WSS) capabilities could enable secondary users to seize more transmission opportunities in frequency domain, where the signal location of primary users might be unknown in practice. To ensure the system stability, it is necessary for spectrum sensing under the condition of low signal-to-noise ratio (SNR). However, the detection performance of conventional energy detection (ED) methods is unsatisfactory when the SNR of the primary user signal is low. Motivated by the correlation attribute among frequency points of each signal in the wideband spectrum, this paper studies the theoretical relationship between SNR of the PU signal and frequency correlation parameters, i.e., the size of correlation window (CW) and the value of frequency correlation ratio. Based on this, an Adaptive Frequency Correlation-based (AFC) method is proposed. This method that does not require any prior knowledge specifically considers the detection of low SNR signals. These two parameters related to frequency correlation can be adaptively adjusted according to the SNR of each signal to be detected. Moreover, to ensure the detection performance of the proposed AFC method, we present a low-complexity inflection-point estimation algorithm which is capable of estimating the noise power, SNR and possible frequency-domain position of the signal quickly and accurately. Numerical results show that the proposed AFC method is suitable for WSS scenarios with unknown prior information. It is capable of detecting signals with bandwidths greater than their minimum detectable bandwidths which are determined by the size of CWs. Additionally, simulation results indicate that the AFC method outperforms the state-of-the-art ED methods, as it shows evidently better detection performance for low SNR signals with acceptable false alarm rate.

Owing to the emerging techniques such as heterogeneous network and cognitive radio that allow a large number of wireless networks to intensively and flexibly use resources of radio spectrum, the network densification and dynamic spectrum access have become two important trends of wireless networking systems, so that the spectral environments will be increasingly crowded in the near future. Under such a background, this study addresses the stability related problem of the power control in multiple coexisting wireless networks. We develop an analytical framework that is capable of utilizing the small-gain theorem to show the considered power control is bounded-input bounded-output (BIBO) stable in the sense of the ℒ ₂ gain if proper sufficient conditions are satisfied. Through modeling the power control as a feedback system and making use of the uncertainty representation, this framework can derive generalized results for the power-control algorithms that emerge in previous works. The framework developed in this study also admits the incorporation of multipath propagation that is a very common phenomenon in wireless communication systems. Within our derivation, making use of the theory for positive systems dramatically simplifies the analysis and leads to many refined results.

This article addresses the problem of identifying disconnected agents in multiagent systems via external estimators. Specifically, we employ external estimators with an appropriately designed decision rule to identify the disconnectedness (i.e., the status of being disconnected) between two arbitrarily chosen agents in formation-control multiagent systems. The design of the decision rule is inspired by the unit-root testing problem of autoregressive time series. To make the best possible decision, a best-effort procedure is also proposed. Then, by introducing the concept of connected components (or just components) in graph theory, and using the methods of consensus analysis and time-series analysis, we develop an analytical framework to show the theoretical performance of the designed decision rule. A particularly important result shown by our analysis is that the miss probability of the decision rule can converge to 0 as the number of data samples increases. Finally, simulation results validate the performance of the decision rule and the best-effort procedure, showing that they can perform well even in small samples.