With the access of massive terminals of the Internet of Things (IoT), the low-power wide-area networks (LPWAN) applications represented by Long Range Radio (LoRa) will grow extensively in the future. The specific Long Range Wide Area Network (LoRaWAN) protocol within the LoRa network considers both low power consumption and long-range communication. It can optimize data transmission to achieve low communication latency, ensuring a responsive system and a favorable user experience. However, due to the limited resources in LoRa networks, if certain terminals have heavy traffic loads, it may result in unfair impacts on other terminals, leading to increased data transmission latency and disrupted operations for other terminals. Therefore, effectively optimizing resource allocation in LoRa networks has become a key issue in enhancing LoRa transmission performance. In this paper, a Mixed Integer Linear Programming (MILP) model is proposed to minimize network energy consumption under the maximization of user fairness as the optimization goal, which considers the constraints in the system to achieve adaptive resource allocation for spreading factor and transmission power. In addition, an efficient algorithm is proposed to solve this optimization problem by combining the Gurobi mathematical solver and heuristic genetic algorithm. The numerical results show that the proposed algorithm can significantly reduce the number of packet collisions, effectively minimize network energy consumption, as well as offering favorable fairness among terminals.

LoRa is widely used in low-power Internet of Things (IoT) applications due to its low-power consumption, high-connection density, and wide coverage. How to optimize the physical layer parameter configuration of LoRa edge nodes is a bottleneck issue that restricts network performance. To reduce the energy consumption of the distributed LoRa networks, a reinforcement learning strategy based on link prior knowledge is adopted, and the resource allocation of the edge node of the LoRa networks is transformed into a multiarmed bandit problem. On this basis, a dynamic parameter selection algorithm suitable for LoRa edge nodes is proposed, namely, the link-weight-EXP3 (LI-WEX) algorithm. By defining the weight factors of parameters and rewards for nodes, the energy consumption factor in decision-making is enhanced, and the policy space at the node is processed based on link knowledge, thereby improving energy efficiency by selecting the optimal combination of parameters. The simulation results show that the LI-WEX algorithm not only converges faster and is robust in different scenarios, but also can effectively reduce network energy consumption.

In the intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) system, it is critical to improve the system performance by jointly designing the transmit beamforming of base station and IRS. According to this beamforming design problem, the actual phase-related amplitude model is established that is affected by the nonlinear energy loss of IRS, and then a multiparameter joint optimization problem is established to maximize the rate sum of the devices. Furthermore, a PER-SAC algorithm is proposed to solve this problem, which is based on the soft actor–critic (SAC) principle and uses prioritized experience replay (PER) to improve training efficiency. The results show that the PER-SAC algorithm has better stability and convergence effect than other algorithms in this scenario, and the sum rate obtained by the PER-SAC algorithm is also higher than that of the random phase-shift scheme and the random power splitting ratio scheme.

As the sharp increase in data traffic, except for security, home network and data usage will become two important issues in smart home in the future. On the one hand, aiming to solve the problem of wall jack and room coverage in smart home networks, we develop a set of dual-mode data acquisition device, which supports wired power line communication (PLC) and wireless dual-mode communication. Our PLC module is with the advantage of being pluggable on the debugging board, which can be replaced with a module that supports different PLC protocols as required, such as broadband PLC or narrowband PLC. On the other hand, aiming to solve massive data usage problem acquired by smart home facilities, we propose a data analysis method that integrates the automatic encoder scheme and the $K$ -means algorithm. On this basis, we propose a Conv-LSTM-Attn algorithm that is a prediction model of convolutional long- and short-term memory network based on attention mechanism. Through the field test, it illustrates that our system can realize accurate long-term data acquisition and recording, dual-mode control from indoor to long-distance away, the autonomous backhaul of the collected data with low-latency and long-distance transmission. Besides, it can highly flexibly configure various sensor modules to achieve smart home management and control. Moreover, through the analysis of actual sampling data, the proposed Conv-LSTM-Attn algorithm outperforms the existing machine learning schemes in smart home in terms of achieving the prediction values.

Considering different types of base stations (BSs) in future cellular networks are overlapping deployment with the status of dense, multi‐tier and heterogeneous in general, how to optimize the real BS deployment becomes a complicated problem. Based on it, repulsive BS dataset and clustering BS dataset are statically characterized with various types of spatial point processes. It shows that the improvement of coverage probability between 1‐tier and 2‐tier network fluctuates as the signal‐to‐interference ratio (SIR) threshold increases for different datasets. The authors' proposed hybrid model fits well with repulsive BS dataset, while the log‐Gaussian Cox point process (LGCP) and Cauchy model are reasonable models for clustering BS dataset. Besides, in order to dynamically analyze the coverage problem affected by adding new BSs, a cell boundary constructed by an irregular circle is introduced under an equal SIR constraint, and a BS placement scheme is proposed to place new BSs at the points of minimum interference. Numerical results show that the coverage probability may increase after adding BSs in the target area of heterogeneous network by using the proposed scheme. However, as the density of femto BS reaches a certain value, its coverage may remain unchanged even after adding more femto BSs.

In the intelligent reflecting surface (IRS)-assisted MIMO systems, optimizing the passive beamforming of the IRS to maximize spectral efficiency is crucial. However, due to the unit-modulus constraint of the IRS, the design of an optimal passive beamforming solution becomes a challenging task. The feature input of existing schemes often neglects to exploit channel state information (CSI), and all input data are treated equally in the network, which cannot effectively pay attention to the key information and features in the input. Also, these schemes usually have high complexity and computational cost. To address these issues, an effective three-channel data input structure is utilized, and an attention mechanism-assisted unsupervised learning scheme is proposed on this basis, which can better exploit CSI. It can also better exploit CSI by increasing the weight of key information in the input data to enhance the expression and generalization ability of the network. The simulation results show that compared with the existing schemes, the proposed scheme can effectively improve the spectrum efficiency, reduce the computational complexity, and converge quickly.