In this paper, we investigate the uplink joint signal detection for low-altitude aerial cell-free (CF) networks, where each flying access point (AP) locally processes the received signals and then forwards these information to a central processing unit (CPU) for the final detection. However, unlike terrestrial CF networks that typically adopt optic fiber fronthaul links, wireless fronthaul in aerial CF networks connecting flying APs with the CPU will significantly affect the communication performance, due to the practically limited fronthaul capacity. Therefore, we adopt a realistic channel model for wireless fronthaul links, which experience Rician fading and are shared among the flying APs through a combination of frequency division multiple access (FDMA) and space division multiple access (SDMA) protocol. Taking into account the imperfect and capacity-limited wireless fronthaul, we propose a joint uplink signal detection framework, where the local processing matrix at APs and the central detector at the CPU are designed based on the long-term statistical channel state information (CSI) by leveraging the operator-valued free probability theory. This approach significantly reduces the need for frequent, high-capacity signaling exchanges between APs and the CPU. Numerical results demonstrate the accuracy and effectiveness of the proposed joint signal detection framework.

In this paper, we consider cell-free communication systems with several access points (APs) serving terrestrial users (UEs) simultaneously. To enhance the uplink multi-user multiple-input multiple-output communications, we adopt a hybrid-CSI-based two-layer distributed multi-user detection scheme comprising the local minimum mean-squared error (MMSE) detection at APs and the one-shot weighted combining at the central processing unit (CPU). Furthermore, to improve the propagation environment, we introduce multiple reconfigurable intelligent surfaces (RISs) to assist the transmissions from UEs to APs. Aiming to maximize the weighted sum rate, we formulate the weighted sum-MMSE (WMMSE) problem, where the UEs’ beamforming matrices, the CPU’s weighted combining matrix, and the RISs’ phase-shifting matrices are alternately optimized. Considering the limited fronthaul capacity constraint in cell-free networks, we resort to the operator-valued free probability theory to derive the asymptotic alternating optimization (AO) algorithm to solve the WMMSE problem, which only depends on long-term channel statistics and thus reduces the interaction overhead. Numerical results demonstrate that the asymptotic AO algorithm can achieve a high communication rate as well as reduce the interaction overhead.

In this paper, we investigate the uplink signal detection in cell-free massive MIMO systems with unmanned aerial vehicles (UAVs) serving as aerial access points (APs). The ground users are equipped with multiple antennas and the ground-to-air propagation channels are subject to correlated Rician fading. To overcome huge signaling overhead in the fully-centralized detection in cell-free systems, we propose a two-layer distributed uplink detection scheme, where the uplink signals are first detected in AP-UAVs by using the minimum mean-squared error (MMSE) detector based on local channel state information (CSI), and then collected and weighted combined at the CPU-UAV to obtain the refined detection. By using the operator-valued free probability theory, the asymptotic expressions of the combining weights are obtained, which only depend on the statistical CSI and show excellent accuracy compared to the exact but intractable expressions. Based on the proposed scheme, we further investigate the impacts of different deployment scenarios on the spectral efficiency (SE). Numerical results show that in urban and dense urban environments, it is more beneficial to deploy more AP-UAVs to increase SE. Nonetheless, in suburban environment, an optimal combination of the number of AP-UAVs and the number of antennas per AP-UAV exists to maximize SE.

In this paper, we investigate the multiple access technique for the aerial networks, where the multi-antenna base stations are carried by unmanned aerial vehicles (UAVs) to serve ground users. The uplink signals are transmitted by the users simultaneously, then detected and recovered by the aerial networks. On one hand, compared to the case that each UAV individually detects the signals of serving users, we aim to improve the quality of the recovered signals by using cooperative techniques that leverage the signals from multiple UAVs. On the other hand, the fully-centralized cooperation requires signaling exchange between UAVs, which incurs huge signaling overhead and latency, and is infeasible for aerial networks. Therefore, we propose a two-stage distributed minimum mean squared error (MMSE) detection with one-shot signal combining. Specifically, the users' signals are first locally detected by each UAV via the MMSE detector, and then weighted combined at the central UAV in a one-shot manner, where the combining weights are designed to minimize the MSE between the combined signals and the original signals. When only imperfect channel state information is locally available at each UAV, by using the random matrix theory, these combining weights are shown to depend on the long-term channel statistics and thus, greatly reduce the interaction overhead and latency. Numerical results show that the proposed scheme outperforms the non-cooperative detection in terms of the achievable spectral efficiency. Meanwhile, with a much smaller interaction overhead, the proposed scheme achieves comparable spectral efficiency as the fully centralized signal detection.

The FIDO protocol suite aims at allowing users to log in to remote services with a local and trusted authenticator. With FIDO, relying services do not need to store user-chosen secrets or their hashes, which eliminates a major attack surface for e-business. Given its increasing popularity, it is imperative to formally analyze whether the security promises of FIDO hold. In this paper, we present a comprehensive and formal verification of the FIDO UAF protocol by formalizing its security assumptions and goals and modeling the protocol under different scenarios in ProVerif. Our analysis identifies the minimal security assumptions required for each of the security goals of FIDO UAF to hold. We confirm previously manually discovered vulnerabilities in an automated way and disclose several new attacks. Guided by the formal verification results, we also discovered two practical attacks on two popular Android FIDO apps, which we responsibly disclosed to the vendors. In addition, we offer several concrete recommendations to fix the identified problems and weaknesses in the protocol.

We present a novel attack named “Authenticator Rebinding Attack,” which aims at the Fast IDentity Online (FIDO) Universal Authentication Framework (UAF) protocol implemented on mobile devices. The presented Authenticator Rebinding Attack rebinds the victim’s identity to the attacker’s authenticator rather than the victim’s authenticator being verified by the service in the UAF protocol, allowing the attacker to bypass the UAF protocol local authentication mechanism by imitating the victim to perform sensitive operations such as transfer and payment. The lack of effective authentication between entities in the implementations of the UAF protocol used in the actual system causes the vulnerability to the Authenticator Rebinding Attack. In this paper, we implement this attack on the Android platform and evaluate its implementability, where results show that the proposed attack is implementable in the actual system and Android applications using the UAF protocol are prone to such attack. We also discuss the possible countermeasures against the threats posed by Authenticator Rebinding Attack for different stakeholders implementing UAF on the Android platform.