The external power supply requirement of intelligent reflecting surfaces (IRS) restricts its deployment and application. To address this issue, self-sustainable IRS that can harvest energy from wireless signals significantly enhances the convenience and feasibility of IRS deployment. This paper investigates a novel IRS-aided simultaneous wireless information and power transfer (SWIPT) system, where IRS adopts power splitting (PS) protocol, time switching (TS) protocol or element splitting (ES) protocol to extract energy from the incident signals for sustaining its operation. Our aim is to maximize the weighted sum rate (WSR) while accounting for the constraints in terms of maximum transmit power, IRS reflection coefficients, the energy harvesting requirements of both users and the IRS. The WSR maximization problem is non-convex due to the coupling among variables. For the formulated problem, we propose an alternating optimization (AO) framework to transform it into several subproblems and solves them iteratively. Specifically, we utilize successive convex approximation (SCA) technique to tackle the non-convex optimization challenges in both transmit and reflective beamforming subproblems. Finally, simulation results demonstrate that employing a self-sustainable IRS together with the proposed algorithm yields a WSR improvement of 32%–60%.

The external power supply requirement of intelligent reflecting surfaces (IRS) restricts its deployment and application. To address this issue, self-sustainable IRS that can harvest energy from wireless signals significantly enhances the convenience and feasibility of IRS deployment. This paper investigates a novel IRS-aided simultaneous wireless information and power transfer (SWIPT) system, where the IRS adopts a power splitting (PS) protocol to extract energy from the incident signals for sustaining its operation. Our aim is to maximize the weighted sum rate (WSR) while considering the constraints in terms of maximum transmit power, IRS reflection, the harvesting energy requirements of users and IRS. The formulated problem of maximizing WSR is non-convex, which stems from the coupling among variables, making the problem complicated. To solve this problem, we propose an alternating optimization (AO) framework to transform the formulated problem into three subproblems, which enables an iterative solution approach. Specifically, we utilize successive convex approximation (SCA) technique to tackle the non-convex optimization challenges in both transmit and reflective beamforming subproblems. Simulation results demonstrate the efficacy of the proposed algorithm and substantiate the advantages of implementing self-sustained IRS for improving the WSR performance compared to other benchmark schemes.

In the domain of deep learning for visual tasks, ensemble models combine several less accurate models to form a more precise composite model, improving overall performance. Traditionally, majority voting and average probabilities have been the main decision-making techniques in ensemble learning, focusing only on the TOP1 Class of base models, hence overlooking other significant information. This article introduces a new algorithm, TOP2 hybrid decision (TOP2 HD), which enhances the TOP1 accuracy of the ensemble model. TOP2 HD categorizes base models into hierarchies based on their TOP1 Class and uses the TOP2 Class for ranking, leading to better performance. Extensive experiments across various models and datasets demonstrate that TOP2 HD not only surpasses traditional ensemble methods, such as majority voting, average probabilities, and stacking, but also exceeds many of the latest ensemble strategies in the image domain. In addition, our experiments revealed a functional relationship between the test accuracy of the ensemble model and the number of base models. This enables us to predict the upper limit of the ensemble model's performance using only a fraction of the models, providing a crucial reference for the performance after the deployment of the ensemble model.

The fifth-generation Internet of Things (5G-IoT) requires more capable channel coding methods to obtain low-latency and high-reliability communication systems. Hence, an improved ordered tree-based single-parity-check (OT-SPC) list decoder for polar code is designed in this article to reduce the complexity and keep the reliability. The proposed method introduces the ordered error pattern set of the single-parity-check (SPC) list decoder, which collects all the necessary error patterns. We first define a relationship between these patterns and present the concepts of the necessary error pattern and the ordered error pattern set. Then, we adapt the ordered tree data structure and the asynchronous tournament sorters to relieve the complexity increase of the ordered error pattern set’s introduction. The reliability relationships of different patterns are stored in an ordered tree, and the tournament sorting algorithm is modified separately for the log-likelihood ratio values of the received sequence and the path metrics of the decoding paths. In this scheme, the candidate codewords of the SPC list decoder are constructed in the descending order of their probability, which significantly reduces the calculation and comparison consumption. Eventually, the results show that the OT-SPC list decoder obtains a superior performance in decoding throughput and latency while preserving the error–correction performance.

In cognitive radio network, the licensed spectrum for the primary user can be accessed in an opportunistic manner by secondary user, or unlicensed user. As a key technology of cognitive radio, spectrum sensing has an irreplaceable position. In this paper, we proposed a parallel CNN-LSTM network based deep learning algorithms for spectrum sensing. As much modulated signals and noise data as possible are generated to train the model to accommodate detection of multiple types signal. Various experiments are performed to prove the effectiveness of proposed method, and requiring no prior knowledge about the information of licensed user or channel state. The simulation results show that the model can detect multiple modulation types under a large scale of SNRs, especially in low SNR.