In recent years, the rapid growth of the Industrial Internet of Things (IIoT) has placed increasing demands on Low-Power and Lossy Networks (LLNs) for data collection and communication in complex environments. As key nodes in clustered architectures, Cluster Heads (CHs) directly influence network lifetime and transmission efficiency. This paper proposes a lightweight rule-based two-stage CH selection mechanism that integrates association-rule filtering with a decision-tree–style logical evaluator that combines association rule mining with a decision tree, jointly evaluating features such as neighbor density, residual energy, and communication cost to improve candidate filtering and final election accuracy. The mechanism is implemented in Contiki and evaluated through Cooja simulations. Results show that the proposed approach significantly enhances energy efficiency and extends network lifetime, offering a promising direction for intelligent CH election and adaptive communication protocols in LLNs.

The satellite Internet of Things (IoT) covers a vast area with multiple nodes and has limited resources for random access, which leads to low throughput. In this letter, we propose a Q-learning combined with diversity slotted compressive random access control scheme (QDCC) to enhance resource utilization and throughput. Moreover, we address the issue of reduced throughput during network overload by introducing an adaptive frame length QDCC (AFL-QDCC) scheme. This scheme adjusts the frame length by utilizing the support sets estimated by the compressive sensing (CS) reconstruction algorithm. Simulation results demonstrate that the proposed QDCC scheme outperforms conventional schemes in terms of throughput. Furthermore, the AFL-QDCC scheme can maintain stable and high throughput performance even with a large number of nodes.

Sparse code multiple access (SCMA) is a code-domain non-orthogonal multiple access (NOMA) technology proposed to meet the access needs of large-scale intelligent terminal devices with high spectrum utilization. To improve the accuracy and computational complexity of SCMA to accommodate the internet of things (IoT) scenario, we design a new end-to-end autoencoder combining convolutional neural networks (CNNs) and residual networks. A residual network with multitask learning improves the decoding accuracy, and CNN units are used for SCMA codeword mapping, with sparse connectivity and weight-sharing to reduce the number of trainable parameters. Simulations show that this scheme outperforms existing autoencoder schemes in bit error rate (BER) and computational complexity.

Mobile edge computing (MEC) is a promising technique to support the emerging delay-sensitive and compute-intensive applications for user equipment (UE) by means of computation offloading. However, designing a computation offloading algorithm for the MEC network to meet the restrictive requirements towards system latency and energy consumption remains challenging. In this paper, we propose a joint user-association, task-partition, and resource-allocation (JUTAR) algorithm to solve the computation offloading problem. In particular, we first build an optimization function for the computation offloading problem. Then, we utilize the user association and smooth approximation to simplify the objective function. Finally, we employ the particle swarm algorithm (PSA) to find the optimal solution. The proposed JUTAR algorithm achieves a better system performance compared with the state-of-the-art (SOA) computation offloading algorithm due to the joint optimization of the user association, task partition, and resource allocation for computation offloading. Numerical results show that, compared with the SOA algorithm, the proposed JUTAR achieves about 21% system performance gain in the MEC network with 100 pieces of UE.

超密集网络可以通过虚拟小区间的协作来提升用户体验,但由于小区的重叠覆盖使得用户间存在较复杂的干扰问题.因此,提出了一种基于判别函数的聚类算法来缓解强干扰带来吞吐量下降的问题.首先,利用用户间干扰信道的余弦相似度定义用户间的干扰网络;然后,基于干扰网络选出簇头并划分用户,同时为了解决虚拟小区下的模糊用户归属簇问题,以簇间干扰权重之和最大,簇内干扰权重之和最小为原则设计判别函数,对用户进行模糊归类.仿真结果表明,在不增加复杂度的同时,所提算法比其他方法的系统吞吐量提升了10％ ～30％,对于边缘用户具有一定优势.

Integrated localization and wireless communication(ILWC) is a new information technology based on hardware resources and software information sharing to realize the coordination of location and communication functions. This paper briefly introduces the research progress of wireless communication technology and localization technology, and reveals that ILWC technology is the inevitable result of the bearer services expansion of wireless communication system. This study summarizes the definition and connotation of ILWC technology in different research. The core idea of ILWC is defined as 'hardware integration and software sharing'. The research progress of ILWC technology is summarized according to the two stages of equipment reuse and deep fusion. Combined with the particularity of the scene of the coal mine, the concept of ILWC in the coal mine is put forward. Based on the sharing of time, space, spectrum, computing and other resources, the technology is the fusion technology of communication function and localization function with an automatic scene perception and dynamic and adaptive resource allocation mechanism. The adaptability of underground ILWC in roadway, central substation, underground parking lot, coal working face and other scenes is discussed. It is pointed out that the challenges faced by underground ILWC are the complexity of underground wireless channel, unbalanced deployment of base stations and precise recognition of complex underground scene.

广义空间调制(Generalized Spatial Modulation,GSM)在多输入多输出(Multiple Input Multiple Output,MIMO)系统中的应用能够提高系统的能量效率,但与此同时GSM-MIMO系统激活多根天线也给接收机的信号检测带来了极大的挑战,而接收端是否能正确高效地检测出发送信息对整个通信系统至关重要.由于在GSM-MIMO系统中,活跃用户和活跃天线都具有稀疏性,可以应用压缩感知算法进行用户和天线检测.针对多用户广义空间调制信号的结构和稀疏性,将检测问题转化为块稀疏恢复问题,提出了一种块稀疏自适应匹配追踪(Block Sparse Adaptive Matching Pursuit,BSAMP)算法,设计了支撑集检测的两步策略:第1步,利用块稀疏特征检测出活跃用户,得到块级支撑;第2步,通过求解活跃用户的各天线的l2范数得到天线级的索引支撑集.有效利用每个用户的有源天线,检测出活跃用户及其用户数据.在信号值检测阶段,利用GSM-MIMO系统的特点对活跃用户有源天线上的信号取均值,降低了重构的误比特率.此外,采用稀疏度自适应的方法,接收端不需要严格知道活跃用户数目,更贴近实际环境.仿真结果表明,提出的BSAMP算法能够提高检测精度.

为了改善稀疏码分多址系统在多天线应用中的误码率性能,将深度学习引入多输入多输出稀疏码分多址系统,提出了一种降噪自编码器辅助的编解码方法.发射端使用多个深度神经网络单元构建多天线稀疏码分多址编码器,通过神经网络的学习获得每个用户在不同发射天线上的码本,采用降噪自编码器的结构在输入端引入噪声层,使得编码器的输出为更具鲁棒性的特征表示;接收端设计了一个全链接的深度神经网络作为解码器,该解码器将多天线检测与多用户检测联合进行,一次解码即可获得用户数据;采用端到端的训练方式对编解码器进行训练,优化神经网络的结构与参数,使得神经网络能够快速收敛.实验结果表明,提出的编解码方法可以降低多输入多输出稀疏码分多址系统的误码率,同时减少接收端检测的时间.

Device-free passive (DfP) intrusion detection system is a system that can detect moving entities without attaching any device to the entities. To achieve good performance, the existing algorithms require proper access point (AP) deployment. It limits the applying scenario of those algorithms. We propose an intrusion detection system based on deep learning (IDSDL) with finer-grained channel state information (CSI) to free the AP position. A CSI phase propagation components decomposition algorithm is applied to obtain blurred components of CSI phase on several paths as a more sensitive detection signal. Convolutional neuron network (CNN) of deep learning is used to enable the computer to learn and detect intrusion without extracting numerical features. We prototype IDSDL to verify its performance and the experimental results indicate that IDSDL is effective and reliable.

针对无源目标分类系统中精度和费用之间不平衡、采用手工提取特征的方法进行特征提取工作量较大的问题,提出了一种基于误差逆传播(BP)神经网络的信道状态信息(CSI)无源目标分类方法.通过提取WiFi信号的CSI作基信号,并结合具有自主学习数据特征能力的神经网络方法,设计了BP神经网络的训练模型,减少了手工提取特征带来的开销.实验结果表明,以身高分类为例,所提方法能够区分4个不同身高段,且平均分类准确度可以达到90％以上.

Mobile edge computing (MEC) reduces latency and power consumption of mobile devices by deploying servers on the network edge. Due to the limited computing resources of the MEC server, excessive offloading of the users in local cells will bring unexpected queuing delay and high energy consumption. To deal with these problems, this paper proposes a Multi-user Queue Offloading multi-dimensional game algorithm (MQO). By the algorithm, users could choose the most suitable strategy, in which the strategy space is two-dimensional to satisfy different needs of different users to a certain extent, including the offload strategy and power control of users. Simulation results show that MQO can improve the performance of both overhead and offloading number with the increase of users.

提出基于斯皮尔曼相关系数的指纹法,具有非空间属性的指纹向量间的相似度可用该指纹法进行比较.在指纹法和几何法基础上,提出一种基于RSSI的指纹法与几何法联合的室内无源感知定位方法(F&G法).仿真结果表明,相对指纹法和几何法,F&G法能提高定位精度.

由于移动边缘计算可通过借助边缘计算服务器实现计算任务卸载,不再受限于移动终端的计算能力,所以当边缘服务器过载时,往往会选择排队、推迟或拒绝移动终端的业务请求.但现有研究大都没有考虑此时服务器如何缓解负载压力,由于服务中断和等待延长,用户的服务质量将明显恶化.研究了将过载移动边缘计算服务器的任务卸载到与其同一协作区间的其他服务器来提高计算卸载能力.将罚函数与两步拟牛顿法相结合给出了优化方法,联合优化包含边缘计算网络总时延和能耗的联合效用函数.针对优化目标对时延或者能效的不同需要,使用经验因子灵活调整时延和能量消耗最小化之间的优化偏差.仿真结果表明,卸载优化方法在提高关于时延和能耗的系统性能方面优于现有的两种比较方法,且以更快的速度收敛.

With the rapid development of smart devices and WiFi networks, WiFi-based indoor localization is becoming increasingly important in location-based services. Among various localization techniques, the fingerprint-based method has attracted much interest due to its high accuracy and low equipment requirement. Traditional fingerprint-based indoor localization systems mostly obtain positioning by measuring the received signal strength indicator (RSSI). However, the RSSI is affected by environmental influences, thereby limiting the precision of positioning. Therefore, we propose a new indoor fingerprint localization system based on channel state information (CSI). We adopt a novel method, in which the amplitude and phase of the CSI are fused to generate fingerprints in the training phase and apply a weighted [Formula: see text]-nearest neighbor (KNN) algorithm for fingerprint matching during the estimation phase. The system is validated in an exhibition hall and laboratory and we also compare the results of the proposed system with those of two CSI-based and an RSSI-based fingerprint localization systems. The results show that the proposed system achieves a minimum mean distance error of 0.85[Formula: see text]m in the exhibition hall and 1.28[Formula: see text]m in the laboratory, outperforming the other systems.

Massive machine type communication (mMTC) serves an irreplaceable role in the development process of the Internet of Things (IoT). Because of its characteristics of massive connection and sporadic transmission, compressed sensing (CS) has been applied in joint user activity and data detection in the uplink grant-free non-orthogonal multiple access (NOMA) system. In previous work, greedy iterative-based multi-user detection (MUD) algorithms were developed in mMTC scenarios because of the computational benefit and competitive performance. However, conventional greedy iterative-based MUD algorithms still suffer from high computational complexity due to the process of large-size matrix inversion with the accession of massive devices into the system. In this paper, gradient information is used to address this problem. A low-complexity gradient descent-based gradient pursuit MUD (GDGP-MUD) algorithm is proposed, which uses the gradient information of error function in the process of iteration as a new updating direction, instead of the matrix inversion process. Then, a multi-step quasi-Newton MUD (MSQN-MUD) algorithm is proposed to improve the precision of detection while maintaining low complexity. In the algorithm, high-order information in the process of adjacent iteration is used effectively to update data values more accurately. Moreover, the convergence and complexity analysis of both algorithms are derived. The analysis shows that both proposed algorithms have lower computational consumption than most of the state-of-the-art greedy-based MUD algorithms. It is worth noting that in comparison to most existing CS-based MUD algorithms, the two proposed algorithms do not require the exact user sparsity level and, thus, reduce the dependence on prior knowledge. The numerical experiments demonstrate that the proposed algorithms have better real-time performance than existing greedy-based MUD algorithms with similar symbol error rate performance.

The paper introduced related concepts, development overview and application of artificial intelligence technology in development of coal industry, and pointed out that at present, application of artificial intelligence technology in mine is only a combination of point and shallow degree, and deep fusion between artificial intelligence technology and a certain production or management system of mine is not realized. The development process of intelligent mine was summarized. Intelligent mine is considered as a deep fusion of artificial intelligence technology, big data technology, Internet of things technology and physical mine, it combined intelligent communication, intelligent control and intelligent computing technology to realize calculation and processing of digital mine, and construct digital twin mine, uses intelligent interactive evolution of digital twin mine and physical mine to achieve coal mine safety, high efficiency, green production control. A three-layer structure of intelligent mine which integrates artificial intelligence technology with mine was constructed, including equipment layer, intelligent layer and application layer. The application layer is at the highest level of the intelligent mine, in which the digital twin mine sub-layer is equivalent to 'digital brain' to realize intelligent control of the highest level of the mine. The agent in the intelligent layer requires the subsystem not only to use artificial intelligence technology to process the data generated by the subsystem, but also to integrate intelligent computing, intelligent communication and intelligent control in the architecture. The development trend of intelligent mine construction was prospected: intelligent mine need to strengthen in-depth study of the artificial intelligence technology and the mine fusion, existing fault detection and diagnosis based on artificial intelligence and advanced intervention technology are applied to robot system, inspection robot integrated intelligent computing, intelligent communication, intelligent control will be one of the earliest underground intelligent agent; it is necessary to further strengthen the research on the modeling technology of complex giant system in the intelligent mine. Only by establishing the complex giant system model of the mine, the collaborative interaction between mining activities and the environment can be realized, and the accurate control of coal mining activities can be realized. The lack of complex giant system model will be an urgent problem to be solved in intelligent mine construction in the future.

WiFi-based indoor localization techniques are critical for location-based services. Among them, fingerprint-based method gains considerable interest due to its high accuracy and low equipment requirement. One of the major challenges faced by fingerprint-based position system is that in some places there are not enough access points (AP) to provide features for accurate location. To address that, we propose a novel fingerprint-based system using only a single AP. We propose a novel phase decomposition method to obtain the phase of multipath provided by a AP and use the decomposed phase as a fingerprint after the feature exaction by principal component analysis (PCA). Performance in the laboratory, meeting room, and corridor is investigated, and our system is also compared with a RSSI-based and a CSI-based fingerprint localization system. As the experimental results suggest, the minimum mean distance error is 0.6m in the laboratory, 0.45m in the meeting room, and 1.08m in the corridor, outperforming the other two systems.

In order to solve the high computational complexity problem of gradient pursuit in compressed sensing, we applied multi-step quasi-Newton method to gradient pursuit and proposed a gradient pursuit algorithm based on multi-step quasi-Newton method (MSQN-GP). The MSQN-GP algorithm uses the pre-multiple gradient information to approximate the inverse matrix of the Hessian matrix of the objective function, and this scheme is applied to the gradient pursuit algorithm to efficiently solve the update direction problem, while avoiding complex matrix inversion operations. Firstly, we give a detailed theoretical derivation, which proved that the algorithm has both the decent property of the steepest descent method and the second-order convergence of the Newton method. Then, we present extensive simulation results to show that our proposed MSQN-GP algorithm can achieve significant reduction in complexity on the basis of ensuring reconstruction accuracy compared with the existing gradient pursuit algorithms.

The iterative precoding scheme is a common algorithm for downlink massive MIMO systems. Due to the large number of system antennas, traditional linear precoding schemes are usually involve the large-scale matrix inversion and leads to high computational complexity. The complexity of the linear precoding algorithm greatly reduced when the iterative algorithm is proposed, but it caused a decline in Bit Error Rate (BER) performance. Improving the BER performance of the iterative algorithm and ensuring the convergence rate has always been the focus of attentions. Currently, there are many iterative methods that only have mathematical theory and not applied to precoding yet. To solve the aforementioned problem, we proposes a precoding scheme based on Symmetric Accelerated Over Relaxation (SAOR) method, which achieve the enhancement BER performance compared to other iterative algorithms. Combined with the actual system, the selection of optimal acceleration factor and relaxation factor are discussed, which is only related to system parameters. The simulation results show that SAOR-based precoding can achieve good BER performance with less iterations and guarantee the convergence rate.