Related papers: Robust Activity Detection for Massive Random Acces…

Real-world network applications must cope with failing nodes, malicious attacks, or nodes facing corrupted data - data classified as outliers. Our work addresses these concerns in the scope of the sensor network localization problem where,…

In this paper, we investigate an unsourced random access scheme for massive machine-type communications (mMTC) in the sixth-generation (6G) wireless networks with sporadic data traffic. Firstly, we establish a general framework for massive…

Recently, non-orthogonal codes have been advocated for IoT massive access. Activity detection has been demonstrated to entail common support recovery in a jointly sparse multiple measurement vector (MMV) problem and MMV algorithms have been…

Visual object tracking is an important computer vision problem with numerous real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security.…

This work studies the uplink of grant-free low data-rate massive machine-to-machine communications (mMTC) where devices are only active sporadically, which requires a joint activity and data detection at the receiver. We develop an adaptive…

Recently, the availability of remote sensing imagery from aerial vehicles and satellites constantly improved. For an automated interpretation of such data, deep-learning-based object detectors achieve state-of-the-art performance. However,…

This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of…

With the rapid development of Internet of Things (IoT), massive machine-type communication has become a promising application scenario, where a large number of devices transmit sporadically to a base station (BS). Reconfigurable intelligent…

The main challenge of massive machine-type communications (mMTC) is the joint activity and signal detection of devices. The mMTC scenario with many devices transmitting data intermittently at low data rates and via very short packets…

Indoor localization is critical for IoT applications, yet challenges such as non-Gaussian noise, environmental interference, and measurement outliers hinder the robustness of traditional methods. Existing approaches, including Kalman…

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by…

The next wave of wireless technologies will proliferate in connecting sensors, machines, and robots for myriad new applications, thereby creating the fabric for the Internet of Things (IoT). A generic scenario for IoT connectivity involves…

We develop a real-time anomaly detection algorithm for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic logistic model with interaction terms for sender- and receiver-specific latent…

This paper investigates the robust wideband channel estimation problem in the millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. In such a scenario, the beam squint effect that the array response vectors vary…

This paper considers an uplink massive machine-type communication (mMTC) scenario, where a large number of user devices are connected to a base station (BS). A novel grant-free massive random access (MRA) strategy is proposed, considering…

In order to analyze data produced by the kilometer-scale gravitational wave detectors that will begin operation early next century, one needs to develop robust statistical tools capable of extracting weak signals from the detector noise.…

Functional data analysis offers a diverse toolkit of statistical methods tailored for analyzing samples of real-valued random functions. Recently, samples of time-varying random objects, such as time-varying networks, have been increasingly…

This paper considers the massive connectivity application in which a large number of potential devices communicate with a base-station (BS) in a sporadic fashion. The detection of device activity pattern together with the estimation of the…

Accurate and robust localization is crucial for wireless ad-hoc and sensor networks. Among the localization techniques, component-based methods advance themselves for conquering network sparseness and anchor sparseness. But component-based…

Grant-free random access in massive machine-type communications enables low-latency connectivity with minimal signaling. However, sporadic device activation requires efficient device activity detection. We propose a federated learning-based…