This paper presents an analytical framework for evaluating beam misalignment in 3GPP mmWave NR systems implementing analog beamforming. Our approach captures the interaction between user mobility, beam sweeping mechanisms, and deployment configurations, focusing on long-term average performance metrics. Specifically, we model the beam misalignment rates at both the base station (BS) and user equipment (UE) as Poisson processes and derive expressions for the expected misalignment duration, misalignment fraction, and overall beamforming gain. The framework accounts for practical constraints in NR such as Synchronization Signal Blocks (SSB) periodicity, TDD frame structures, and SSB overhead. Through numerical evaluation based on 3GPP mmWave parameters, we identify key trade-offs between beam counts, user mobility, and SSB timing, providing actionable design insights for robust and efficient beam management in future high-frequency networks.

Reliable wireless communication is crucial for the interconnected manufacturing systems of Industry 4.0. However, the dense distribution of stations (STAs) and obstacles such as concrete walls and machinery can adversely affect radio performance. Our study investigates the performance of Legacy Wi-Fi and Wi-Fi 6 in a factory through simulations. A ray-tracing-based propagation model is used to account for the challenging radio environment. Based on this, we apply Access Point (AP) grouping strategies to assign the constrained Wi-Fi spectrum in the factory. Multiple traffic types are simulated within the digital twin factory model to evaluate the key performance indicators (KPIs). Our findings show that Wi-Fi 6 significantly reduces Round Trip Time (RTT) compared to Legacy Wi-Fi, though increased bandwidth can lead to higher co-channel interference. The analysis also indicates the difficulty of optimizing Wi-Fi performance in industrial scenarios due to the multiplicity of influencing factors and the complexity of their correlation.

With the advent of 5G, communication in the mmW ave band (24-100 GHz) became one of the cornerstones of wireless communication research in the last years due to the large amount of available spectrum in said band, enabling the usage of bandwidths of up to 400 MHz [1]. Notably, operation in such bands entails a cell coverage shrinkage due to the worsening of the propagation conditions, partially compensated via beamforming techniques. Now, in advancing beyond 5G (B5G), the shift towards subTHz (90-300 GHz) and THz(0.3-10 THz) bands seems inevitable in order to harvest even broader bandwidths. This transition will further worsen channel conditions and shrink cell coverage, calling for Ultra-Dense Cell (UDC) deployments. In this work, we forecast frequency bands, communication bandwidths, antenna array sizes, and spatial multiplexing capabilities for B5G deployments to assess their impact on cell range and fronthaul (FH) capacity. Our work advances the state of the art in that it analyzes potential B5G configurations in terms of bandwidth, antenna, and layer numbers, as well as exploring non-standard functional splits (e.g., splits 7a, 7c, 7e). Fig. 1 shows the achievable signal-to-noise-ratio (SNR) against the cell coverage range for the downlink (DL) of a cellular system operating at bands 3.5-90 GHz, with path-loss model in [2]. By example, to ensure a target SNR=10 dB, densification requirements increase from a cell range (distance between base station, BS, and user equipment, UE) of 800 m(3.5 GHz) to 150 m(90 GHz).

The phenomenal growth of the Internet of Things (IoT) and popularity of the mobile stations have rapidly increased the demand of WLAN network (known as IEEE 802.11 and WiFi). WLAN is a low-cost alternative of the cellular network and being an unlicensed spectrum to build the master plan of embedding the Internet in everything-&-anywhere. Although the advent of the I\oT is providing a great number of supportive services, there are the numerous number of incident reports in which IoT has been used to launch massive spoofing attacks against major clouds around the world. In addition, based on the number of researches and analyses on inbound traffic towards the major clouds, almost half of cloud providers are experiencing the Distributed Denial-of-Service (DDoS) attack which employ spoofing technique to bypass the detection systems. In this research work, we will review the existing solutions which have been developed to protect both traditional and softwarized network paradigms. Then, we discuss the strategies of our proposed solution which enable networks protected against potential attack; we deploy the Software-defined Wireless Networking (SDWN) within the wide-scale WiFi network and apply our proposal to mitigate spoofing-oriented network attacks along with outbound network traffic. The proposed solution of this work is a practical mechanism to protect wide-scale IoT networks against launching network attacks to cloud infrastructure and its services. In addition, the proposed solution of this work provides the environmental sustainability feature by saving power consumption in networking devices of the IoT network.

As the standardization of network-assisted deviceto-device (D2D) communications by the 3 rd Generation Partnership Project progresses, the research community has started to explore the technology potential of new advanced features that will largely impact the performance of 5G networks. For 5G, D2D is becoming an integrative term of emerging technologies that take advantage of the proximity of communicating entities in licensed and unlicensed spectra. The European 5G research project Mobile and Wireless Communication Enablers for the 2020 Information Society (METIS) has identified advanced D2D as a key enabler for a variety of 5G services, including cellular coverage extension, social proximity and communicating vehicles. In this paper, we review the METIS D2D technology components in three key areas of proximal communications – network-assisted multi-hop, full-duplex, and multi-antenna D2D communications – and argue that the advantages of properly combining cellular and ad hoc technologies help to meet the challenges of the information society beyond 2020.

This unique text provides a comprehensive and systematic introduction to the theory and practice of mobile data networks. Covering basic design principles as well as analytical tools for network performance evaluation, and with a focus on system-level resource management, you will learn how state-of-the-art network design can enable you flexibly and efficiently to manage and trade-off various resources such as spectrum, energy, and infrastructure investments. Topics covered range from traditional elements such as medium access, cell deployment, capacity, handover, and interference management, to more recent cutting-edge topics such as heterogeneous networks, energy and cost-efficient network design, and a detailed introduction to LTE (4G). Numerous worked examples and exercises illustrate the key theoretical concepts and help you put your knowledge into practice, making this an essential resource whether you are a student, researcher, or practicing engineer.

In recent years, the use of wireless systems in industrial applications has experienced spectacular growth. Unfortunately, industrial environments often present impulsive noise which degrades the reliability of wireless systems. OFDM is an enhanced technology used in industrial communication to monitor the work and movement of employees using high quality video. However, OFDM is sensitive to high amplitude impulsive noise because the noise energy spreads among all OFDM sub-carriers. This paper proposes a receiver structure consisting of two stages: a detector stage combining Fisher’s Quadratic discriminant and Gaussian Hypothesis techniques, and a suppression stage optimized by setting well defined thresholds. The receiver structure has been tested by simulations and measurements providing an increment in the probability of detection and improving the system performance.

In this paper, a comparison between energy and maximum-minimum eigenvalue detectors is performed. The comparison has been made concerning the sensing complexity and the sensing accuracy in terms of the receiver operating characteristics curves. The impact of the signal bandwidth compared to the observation bandwidth is studied for each detector. For the energy detector, the probability of detection increases monotonically with the increase of the signal bandwidth. For the maximum-minimum eigenvalue detector, an optimal value of the ratio between the signal bandwidth and the observation bandwidth is found to be $0.5$ when reasonable values of the system dimensionality are used. Based on the comparison findings, a combined two-stage detector is proposed. The combined detector performance is evaluated based on simulations and measurements. The combined detector achieves better sensing accuracy than the two individual detectors with a complexity lies in between the two individual complexities. The combined detector is fully-blind and self-adapted as the maximum-minimum eigenvalue detector estimates the noise and feeds it back to the energy detector. The performance of the noise estimation process is evaluated in terms of the normalized mean square error.