With the development of federated learning (FL), mobile devices (MDs) are able to train their local models with private data and send them to a central server for aggregation, thereby preventing leakage of sensitive raw data. In this paper, we aim to improve the training performance of FL systems in the context of wireless channels and stochastic energy arrivals of each MD. To this purpose, we dynamically optimize MDs transmission power and training task scheduling. We first model this dynamic programming problem as a constrained Markov decision process (CMDP). Due to high dimensions of the proposed CMDP problem, we propose online stochastic learning methods to simplify the CMDP and design online algorithms to obtain an efficient policy for all MDs. Since there are long-term constraints in our CMDP, we utilize a Lagrange multipliers approach to tackle this issue. Furthermore, we prove the convergence of the proposed online stochastic learning algorithm. Numerical results indicate that the proposed algorithms can achieve better performance than the benchmark algorithms.

Satellite-based automatic dependent surveillance broadcast (ADS-B) is of great importance in the next generation air traffic management system. However, there are two major challenges: interference and low signal to noise ratio (SNR). The interference can be mitigated by various separation algorithms. In this paper, we are committed to improving the detection performance for the ADS-B signals with downlink format field equal to 17 at low SNRs. At the moment, commercial receivers work well with sufficient SNRs but may not function properly in the case of the low SNRs. To this end, based on the characteristics of the ADS-B signal frame format, we propose a novel multi-criteria preamble detection algorithm. The proposed algorithm is a constant false alarm rate (CFAR) detection algorithm, and the determination of threshold is independent of noise power. We make a detailed theoretical analysis of each criterion, and develop the analytic expression of the detection probability. All the theoretical results are validated by the Monte Carlo simulations. The simulation shows that the proposed algorithm has an excellent probability of detection when the SNR is low, which is suitable for the satellite-based ADS-B application.

In this paper, we present a new construction of asymmetric quantum codes (AQCs) by combining classical concatenated codes (CCs) with tensor product codes (TPCs), called asymmetric quantum concatenated and tensor product codes (AQCTPCs) which have the following three advantages. First, only the outer codes in AQCTPCs need to satisfy the orthogonal constraint in quantum codes, and any classical linear code can be used for the inner, which makes AQCTPCs very easy to construct. Second, most AQCTPCs are highly degenerate, which means they can correct many more errors than their classical TPC counterparts. Consequently, we construct several families of AQCs with better parameters than known results in the literature. Third, AQCTPCs can be efficiently decoded although they are degenerate, provided that the inner and outer codes are efficiently decodable. In particular, we significantly reduce the inner decoding complexity of TPCs from $\Omega (n_{2}a^{n_{1}})(a>1)$ to $O(n_{2})$ by considering error degeneracy, where $n_{1}$ and $n_{2}$ are the block length of the inner code and the outer code, respectively. Furthermore, we generalize our concatenation scheme by using the generalized CCs and TPCs correspondingly.

As an emerging technology, digital twin (DT) can provide real-time status and dynamic topology mapping for Internet of Things (IoT) devices. However, the implementation of DT within IoT networks necessitates substantial, distributed data support, which raises privacy concerns. To address these issues, we develop a dynamic resource scheduling algorithm tailored for the asynchronous federated learning (FL)-based lightweight DT empowered IoT network. Specifically, our approach aims to minimize a multi-objective function that considers both energy consumption and latency by optimizing IoT device selection and transmit power control, while adhering to long-term performance constraints derived from the convergence upper bound for asynchronous FL. We utilize the Lyapunov method to decompose the formulated problem into a series of one-slot optimization problems and design a two-stage optimization algorithm to achieve optimal transmit power control and efficient IoT device scheduling. In the first stage, we derive closed-form solutions for optimal transmit power on the IoT device side. In the second stage, to address the uncertainty in partial state information, the edge server leverages a multi-armed bandit (MAB) framework to model the IoT device selection problem and employs an efficient online algorithm, the client utility-based upper confidence bound (CU-UCB), to solve it. Moreover, we analyze the algorithm’s optimality gap and time-averaged regret, further demonstrating its effectiveness in resource allocation. Numerical results confirm the superior performance of our algorithm compared to benchmark methods, with the CU-UCB algorithm achieving an 18.9% reduction in average cost and a 6% improvement in accuracy on real-world datasets.

This study presents a chip rate and pseudo‐noise (PN) sequence estimation algorithm of direct sequence spread spectrum signals. The received signal samples are divided into temporal segments, from which the correlation matrix is computed and decomposed. Then the principal eigenvector of this matrix is de‐noised. The chip rate and the PN sequence are estimated from the de‐noised principal eigenvector. The computational complexity is also evaluated. Theory analysis and computer simulation results show that, compared with other algorithms, the performance of proposed algorithm is significantly improved at the approximate computational complexity. In addition, an improved version with low computational complexity is available. Simulation results also verify the effectiveness and superior performance of improved algorithm in low signal‐to‐noise ratio.