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Sparse code multiple access (SCMA) is able to provide high spectral efficiency and massive connectivity, hence it is considered as a promising scheme for the fifth generation (5G) systems. This paper proposed a radio resource allocation scheme based on deep learning for SCMA systems, with the aim to automatically avoid the inter-cell interference. A long short term memory (LSTM) network is adopted to learn the past interference characteristics and predict the interference power in the current subframe. Radio resource blocks with less predicted interference power are then selected for users to transmit signals. Simulation results show that the proposed scheme outperforms the moving average prediction method and has significant gains over the random radio resource block allocation in terms of achievable bit error rate in SCMA systems.

The mega-low Earth orbit (LEO) satellite constellation is pivotal for the future of satellite Internet and 6G networks. In the mega-LEO satellite constellation system (MLSCS), which is the spatial distribution of satellites, global users, and their services, along with the utilization of global spectrum resources, significantly impacts resource allocation and scheduling. This paper addresses the challenge of effectively allocating system resources based on service and resource distribution, particularly in hotspot areas where user demand is concentrated, to enhance resource utilization efficiency. We propose a novel three-layer management architecture designed to implement scheduling strategies and alleviate the processing burden on the terrestrial Network Control Center (NCC), while providing real-time scheduling capabilities to adapt to rapid changes in network topology, resource distribution, and service requirements. The three layers of the resource management architecture—NCC, space base station (SBS), and user terminal (UT)—are discussed in detail, along with the functions and responsibilities of each layer. Additionally, we explore various resource scheduling strategies, approaches, and algorithms, including spectrum cognition, interference coordination, beam scheduling, multi-satellite collaboration, and random access. Simulations demonstrate the effectiveness of the proposed approaches and algorithms, indicating significant improvements in resource management in the MLSCS.

As a typical thin-walled workpiece with non-equal thickness and closed section, the steam turbine blade is easy to be deformed and to chatter in the machining process due to its low stiffness, which seriously affects the final machining quality. One effective way to solve this problem is to support the workpiece using an assisted robot simultaneously with five-axis machining. The core critical issue of the aforementioned machining strategy is to coordinate the cutter and the supporter by scheduling the path of the end-supporter during support machining. This paper aims at scheduling the path of the end-supporter. This is unconventional and significant because of the following: (1) Due to the “non-equal thickness” feature of the thin-walled parts, the supporter path is not the equal-distance offset of the existing cutter path; (2) due to the “closed section” feature of the thin-walled parts, cyclic cutter-location path is often adopted, which makes the interference among three bodies in terms of the workpiece, the cutter, and the supporter easy to occur. Therefore, to schedule harmonious and interference-free supporter path corresponding to the existing cutter-location path for support machining of the thin-walled parts, this paper proposes an equal chord-tangent angle method for generating the reasonable support path, followed by a spatial-mapping-based optimization method for generating the shortest interference-avoidance path. The support path and the interference-avoidance path formed the integral end-supporter path. Verification test demonstrates that the scheduled supporter path can not only coordinate with the original cutter, but also has no abrupt directional variation or spatial interferences.

This paper focuses on downlink power allocation for a cognitive radio-based non-orthogonal multiple access (CR-NOMA) system in a femtocell environment involving device-to-device (D2D) communication. The proposed power allocation scheme employs the greedy asynchronous distributed interference avoidance (GADIA) algorithm. This research aims to optimize the power allocation in the downlink transmission, considering the unique characteristics of the CR-NOMA-based femtocell D2D system. The GADIA algorithm is utilized to mitigate interference and effectively optimize power allocation across the network. This research uses a fairness index to present a novel fairness-constrained power allocation algorithm for a downlink non-orthogonal multiple access (NOMA) system. Through extensive simulations, the maximum rate under fairness (MRF) algorithm is shown to optimize system performance while maintaining fairness among users effectively. The fairness index is demonstrated to be adaptable to various user counts, offering a specified range with excellent responsiveness. The implementation of the GADIA algorithm exhibits promising results for sub-optimal frequency band distribution within the network. Mathematical models evaluated in MATLAB further confirm the superiority of CR-NOMA over optimum power allocation NOMA (OPA) and fixed power allocation NOMA (FPA) techniques.

The launch of the new mobile network technology has paved the way for advanced and more productive industrial applications based on high-speed and low latency services offered by 5G. One of the key success points of the 5G network is the available diversity of cell deployment modes and the flexibility in radio resources allocation based on user’s needs. The concept of Pico cells will become the future of 5G as they increase the capacity and improve the network coverage at a low deployment cost. In addition, the short-range wireless transmission of this type of cells uses little energy and will allow dense applications for the internet of things. In this contribution, we present the advantages of using Pico cells and the characteristics of this type of cells in 5G networks. Then, we will do a simulation study of the interferences impact in uplink transmission in the case of PICO cells densified deployment. Finally, we will propose a solution for interference avoidance between pico cells that also allows flexible management of bands allocated to the users in uplink according to user’s density and bandwidth demand.

In today’s Internet of Things (IoT) era, a wide range of wireless devices communicates together through wireless communication. Wi-Fi has been used for a large number of data transmissions due to its high range, high performance and universal IP accessibility. Wi-Fi is generally power consuming, and it may put a lot of stress on energy-constrained IoT devices and gateways. Conversely, ZigBee has now become a broadly used transmission technique in IoT because of its minimal price, low power consumption and ease of implementation. Many power saving management schemes have been designed to increase energy efficiency, but they do not perform well for all power constraint services. To deal with such situations, we have proposed an Energy-Efficient Scheduling (EES) using Wi-Fi and ZigBee and utilized the high transmission rate of Wi-Fi and low power consumption nature of ZigBee. While working on these two technologies of the same frequency band (2.4 GHz), we have resolved the interference problem using Interference Avoidance (IA) algorithm. Inet framework of Omnet++ simulator is used for the simulation. The implementation result shows a significant reduction in energy consumption on the device and gateway. In the presence of Wi-Fi, ZigBee functions better and the outcomes indicate better throughput while maintaining the energy consumption and interference level.

Industrial wireless applications often share the communication channel with other wireless technologies and communication protocols. This coexistence produces interferences and transmission errors which require appropriate mechanisms to manage retransmissions. Nevertheless, these mechanisms increase the network latency and overhead due to the retransmissions. Thus, the loss of data packets and the measures to handle them produce an undesirable drop in the QoS and hinder the overall robustness and energy efficiency of the network. Interference avoidance mechanisms, such as frequency hopping techniques, reduce the need for retransmissions due to interferences but they are often tailored to specific scenarios and are not easily adapted to other use cases. On the other hand, the total absence of interference avoidance mechanisms introduces a security risk because the communication channel may be intentionally attacked and interfered with to hinder or totally block it. In this paper we propose a method for supporting the design of communication solutions under dynamic channel interference conditions and we implement dynamic management policies for frequency hopping technique and channel selection at runtime. The method considers several standard frequency hopping techniques and quality metrics, and the quality and status of the available frequency channels to propose the best combined solution to minimize the side effects of interferences. A simulation tool has been developed and used in this work to validate the method.

With increasing use of millimeter-wave radars in driving safety applications, interference between vehicles becomes a significant issue. Moreover, oscillator imperfections and relative velocity effects induce inter-carrier interference (ICI) owing to frequency offset, leading to degradation of target detection. In this paper, time-frequency resources are divided into several orthogonal logical channels according to the time-frequency division (TFD) scheme. We propose a two-stage interference mitigation method. First, an interference avoidance technique is designed for each piece of radar equipment (RE) to select logical channels with the least ICI. Then, each RE reconstructs and cancels interference according to estimated parameters based on the proposed interference cancellation technique. Computer simulations reveal that the proposed interference avoidance technique can approximately achieve the performance of ground truth, especially when the number of interferers is small. In addition, noise enhancement effects can be effectively mitigated through the proposed cancellation technique.

This paper considers the internetwork interference problem in environments with multiple wireless body area networks (WBANs). We propose an asynchronous internetwork interference avoidance scheme (abbreviated as AIIA), which is based on the hybrid multiple access of carrier sense multiple access with collision avoidance (CSMA/CA) and time division multiple access (TDMA). In AIIA, the gateway device of each WBAN maintains a table, called an AIIA table, which includes the timing offset and TDMA transmission schedule information corresponding to the interfering WBANs. By referring to the table, the conflicting TDMA schedule can be checked and updated by itself, in asynchronous and distributed manners. Extensive simulations are conducted to demonstrate the feasibility and effectiveness of AIIA.

To design high-sensitivity sensors is one of the critical issues to be solved for ultra-high-frequency (UHF) partial discharge (PD) detection in substations. Commonly-used UHF sensors usually use ultra-wideband antennas for the frequency bands ranging from 300 MHz to 1.5 GHz. To avoid interference in the frequency bands, such as signals generated from mobile phones, a new multi-band UHF sensor is proposed based on the loop antenna theory and meandering technique, which reduces the sensor size, provides high sensitivity and exhibits omnidirectional performance. The sensor works in the bandwidth ranges of 480–520, 800–850 and 1100–1200 MHz, and has sensitivity of more than 10 mm. The PD detection platform was set up, three typical insulation defects, such as corona discharge, surface discharge and free metal particle discharge, were designed, and then the tests were performed to compare the performance of the multi-band sensor and broadband sensor. The results show that the multi-band sensor's bandwidth covers the main spectra of PD signals, thereby can be used for detecting most kinds of PD signals. The sensor's sensitivity is higher than that of the broadband sensor with its size occupying only 5% of the latter, meeting the requirements for detection of PD sources in substations.