The integrated satellite-terrestrial network architecture has emerged as a hotspot for the Internet of Everything, which is a promising approach to provide communication service anytime and anywhere. In this paper, we aim to facilitate the system delay reduction through the two-tier cooperative edge computing of terrestrial base stations (BSs) and the satellite. To avoid the two-way propagation delay brought by the centralized decision process, we propose a distributed offloading scheme for the BSs and the satellite. Firstly, we equivalently decompose the offloading problem into multiple subproblems, based on which the optimal User-BS time slot allocation strategy for each BS is derived. Then, by means of multi-agent deep deterministic policy gradient reinforcement learning, the resource allocation strategy of each BS is obtained distributedly, which effectively avoids the two-way propagation delay. Based on the BS resource allocation strategy obtained, the optimal satellite computation capacity allocation strategy is proposed to minimize the total edge computing delay at the satellite. Finally, exhaustive simulations are implemented to demonstrate the performance of the proposed distributed offloading strategy.

Parallel and distributed learning frameworks are now widely employed for the training of large language models. However, the extremely large model size and data size have made the communication a bottleneck, severely reducing the training efficiency. In this paper, we introduce two novel hierarchical allreduce algorithms designed for 2D-torus topology. By exploiting the cooperation of the intra-cluster communication link and the inter-cluster communication link, the congestion bottleneck caused by the imbalanced link bandwidth can be alleviated. Numerical results are presented to demonstrate the performance of the proposed algorithms in comparison with existing baseline algorithms.

Hierarchical federated learning (HFL) is a key technology enabling distributed learning with reduced communication overhead. However, practical HFL systems encounter two major challenges: limited resources and data heterogeneity. In particular, limited resources can result in intolerable system latency, while heterogeneous data across clients can significantly degrade model accuracy and convergence rates. To address these issues and fully leverage the potential of HFL, we propose a novel framework called graph-based joint client and resource orchestration. This framework addresses the challenges of practical networks through joint client clustering and resource allocation. First, we propose a learning process where edge servers employ hypernetworks to achieve edge aggregation. This method can generate personalized client models and extract data distributions without directly exposing data distributions. Then, to characterize the joint effects of limited resources and data heterogeneity, we propose a graph-based modeling method and formulate a joint optimization problem that aims to balance data distributions and minimize latency. Subsequently, we propose a graph neural network-based algorithm to tackle the formulated problem with low-complexity optimization. Numerical results demonstrate significant benefits over existing algorithms in terms of convergence latency, model accuracy, scalability, and adaptability to new distributions.

Due to the characteristics of open links, immense coverage and dynamic network topology, access authentication is crucial and challenging in integrated satellite-terrestrial back-haul networks (ISTBNs). Based on the access behavior of users, this paper proposes an ISTBN access authentication approach to prevent the spoofing attack, which is the most prominent security threat in ISTBNs. Concretely, we model the access authentication as a machine learning classification task with spatial-temporal characteristics. In this way, the dependency on identification information can be avoided, which is easy to be copied by adversaries in spoofing attacks. Then, we design an ensemble learning classifier to judge the legality of the access to ISTBNs. By organically integrating tree-based models and deep learning through the soft voting method, the detection performance of unauthorized access can be improved. Experiment results validate the effectiveness of our approach for preventing unauthorized access to ISTBNs.

In this article, we consider an Internet of Things (IoT) system in which a sensor delivers updates to a monitor with exponential service time and first-come-first-served (FCFS) discipline. We investigate the freshness of the received updates and propose a new metric termed as age upon decisions (AuD), which is defined as the time elapsed from the generation of each update to the epoch it is used to make decisions (e.g., estimations, inferences, and controls). Within this framework, we aim at improving the freshness of updates at decision epochs by scheduling the update arrival process and the decision-making process. The theoretical results show that: 1) when the decisions are made according to a Poisson process, the average AuD is independent of decision rate and will be minimized if the arrival process is periodic (i.e., deterministic); 2) when both the decision process and the arrival process are periodic, the average AuD is larger, but decreases with decision rate to, the average AuD of the corresponding system with the Poisson decisions (i.e., random); and 3) when both the decision process and the arrival process are periodic, the average AuD can be further decreased by optimally controlling the offset between the two processes. For practical IoT systems, therefore, it is suggested to employ periodic arrival processes and random decision processes. Nevertheless, making the periodical updates and decisions with properly controlled offset is also a promising solution, if the timing information of the two processes can be accessed by the monitor.

In this paper, we focus on the mutual information, which can characterize the transmission ability because it shows correlation between channel input and channel output. Shannon entropy and mutual information are the cornerstones of information theory. In addition, Chernoff information is another fundamental channel information measure, and it describe the maximum achievable exponent of the error probability in hypothesis testing. Uased on alternating conditional expectation (ACE) algorithm, we decompose these two mutual information. In fact, their decomposition results are similar in big data prespective. In this sense, these two kinds of mutual information are just different measures of the same information quantity. This paper also deduces that the channel performance only depends on channel parameters and the decomposition results of a new proposed mutual information should agree with the impact of the parameters.

This paper mainly focuses on the utilization frequency in receiving end of communication systems, which shows the inclination of the user about different symbols. When the using number is limited, a specific utility distribution is proposed on the best effort in term of fairness, which is also the closest one to occurring probability in the relative entropy. Similar to a switch, the parameter of this special utility distribution can be selected to make it satisfy the personalized user demands: negative parameter means the user focus on high-probability events and positive parameter means the user is interested in small-probability events. In fact, the utility distribution can be regraded as a measure of message importance in essence. It illustrates the meaning of message importance measure (MIM), and extend it to the general case by selecting the parameter. Based on it, we connect personalized user demands to the message importance. Numerical results show that this utility distribution characterizes the message importance like MIM and its parameter determines the concern of users like a switch.

The storage and the transmission of messages in big data are discussed in this paper, where message importance is taken into account. To this end, we propose to use non-parametric message importance measure (NMIM) as a measure of message importance, which can characterize the uncertainty of random events like Shannon entropy and Rényi entropy. We prove that NMIM sufficiently describes the two key characters of big data, i.e., the rare events finding and the large diversities of events. Based on NMIM, we then propose an effective compressed encoding mode for data storage, and discuss the transmission of messages over some typical channel models with limited message importance loss. Our numerical results show that the proposed strategy occupies less storage space without losing too much important information, and the maximum received entropy rate increases with the increasing of message importance loss until it reaches saturation, which contributes to designing of better practical communication system.

This paper mainly considers the compressed storage problem for big data in wireless communication systems, where the message importance is taken into account. Similar to Shannon Entropy and Renyi Entropy, we first define a non-parametric message important measure (NMIM) as a measure for message importance. It can characterize the uncertainty of random events. It is proved that it can sufficiently describe the two key characters of big data: rare events finding and large diversities of events. Based on NMIM, we propose an effective compressed encoding mode for data storage in wireless communication systems. Numerical simulation results show that using our developed strategy takes up very little storage space without losing too much message importance.

Message importance measure (MIM) is applicable to characterize the importance of information in the scenario of big data, similar to entropy in information theory. In fact, MIM with a variable parameter can make an effect on the characterization of distribution. Furthermore, by choosing an appropriate parameter of MIM, it is possible to emphasize the message importance of a certain probability element in a distribution. Therefore, parametric MIM can play a vital role in anomaly detection of big data by focusing on probability of an anomalous event. In this paper, we propose a parameter selection method of MIM focusing on a probability element and then present its major properties. In addition, we discuss the parameter selection with prior probability, and investigate the availability in a statistical processing model of big data for anomaly detection problem.

In this paper, we present a practical simple location-aided umbrella-shaped beamforming scheme with transmit diversity of massive Multiple-input Multiple-output (MIMO) system for high speed railway scenarios. Unlike conventional schemes which combines space-time block coding (STBC) with adaptive beamforming or orthogonal switched beamforming, our scheme needs neither uplink channel covariance matrix (UCCM) nor downlink CCM (DCCM) but precalculates the beamforming weights with the help of train location information, which can be completed through pure off-line calculation and therefore reduce system implementation complexity. A closed-form solution of power allocation optimization is derived and the performance of our scheme is verified with simulations from the perspectives of instantaneous received signal-to- noise ratio (SNR), bit error rate (BER) and handover success probability. It indicates that the performance of our scheme approaches to the combination scheme of STBC and adaptive beamforming (STBC-ABF) without introducing any on-line system complexities.

High mobility may destroy the orthogonality of subcarriers in OFDM systems, resulting in inter-carrier interference (ICI), which may greatly reduce the service quantity of high speed railway (HSR) wireless communications. This paper focuses on ICI mitigation in the HSR downlinks with distributed transmit antennas. In such a system, its key feature is that the ICIs are caused by multiple carrier frequency offsets corresponding to multiple transmit antennas. Meanwhile, the channel of HSR is fast time varying, which is another big challenge in the system design. In order to get a good performance, low complexity real-time ICI reduction is necessary. To this end, we first analyzed the property of the ICI matrix and then propose a low complexity ICI reduction method based on location information. For evaluating the effectiveness of the proposed method, the maximum and minimum remaining interference after ICI reduction is analyzed and the service quantity is also discussed. Numerical results are presented to verify our theoretical analysis and the effectiveness of the proposed ICI reduction method. One important observation is that our proposed ICI mitigation method can achieve almost the same service quantity with that obtained on the case without ICI when the velocity of the train is 300km/h.

A wideband field-programmable gate array (FPGA) based hardware implementation for the m parameter estimation of the Nakagami-m fading channel is introduced. It requires fresh estimation of the noise spectrum power density to actually evaluate the signal-to-noise ratio (SNR), and with it, one can efficiently measure the m parameter of Nakagami-m fading channel. The hardware employs a Xilinx Virtex-6 SX475T-2c FPGA integrated Minibee platform operated in Centos system, in which a wideband quadrature modulator ADL5375 is integrated, with output frequency ranging from 400MHz to 6GHz. Such a hardware framework enables the measure system to function well in wireless wideband systems. In addition, we utilize it to conduct real-scenario estimations and compare the results with the software simulations. It also indicates our developed m parameter ML estimation has a better performance compared with some known estimation algorithms.