Matthew David Higgins

Connected braking can reduce fatal collisions in connected and autonomous vehicles (CAVs) by using reliable, low-latency 5G New Radio (NR) links, especially NR Sidelink Vehicle-to-Everything (V2X). In rural areas, road side units are sparse and power-constrained, so energy efficiency must be considered alongside safety. This paper studies how three communication control factors including subcarrier spacing (SCS), modulation and coding scheme (MCS), and transmit power ( ${\boldsymbol{P}}_{\text {t}}$ ) should be configured to balance safety and energy consumption in rural scenarios in light and heavy traffic scenarios. Safety is quantified by the packet receive ratio (PRR) against the minimum communication distance ${\boldsymbol{D}}_{\text {comm}}$ , defined as the distance that the vehicle travels during the transmission of the safety message. Results show that, under heavy traffic, increasing ${\boldsymbol{P}}_{\text {t}}$ and selecting a low-rate MCS at SCS = 30 kHz sustains high PRR at ${\boldsymbol{D}}_{\text {comm}}$ , albeit with higher energy cost. In light traffic, maintaining lower ${\boldsymbol{P}}_{\text {t}}$ with low MCS levels achieves a favorable reliability-energy trade-off while preserving acceptable PRR at ${\boldsymbol{D}}_{\text {comm}}$ . These findings demonstrate the necessity of adaptive, energy-aware strategy to guarantee both safety and energy efficiency in rural V2X systems.

The next generation of wireless systems will leverage the millimeter-wave (mmWave) bands to meet the increasing traffic volume and high data rate requirements of emerging applications (e.g., ultra HD streaming, metaverse, and holographic telepresence). In this paper, we address the joint optimization of beamforming, power control, and interference management in multi-cell mmWave networks. We propose novel reinforcement learning algorithms, including a single-agent-based method (BPC-SA) for centralized settings and a multi-agent-based method (BPC-MA) for distributed settings. To tackle the high-variance rewards caused by narrow antenna beamwidths, we introduce a selective exploration method to guide the agent towards more intelligent exploration. Our proposed algorithms are well-suited for scenarios where beamforming vectors require control in either a discrete domain, such as a codebook, or in a continuous domain. Furthermore, they do not require channel state information, extensive feedback from user equipments, or any searching methods, thus reducing overhead and enhancing scalability. Numerical results demonstrate that selective exploration improves per-user spectral efficiency by up to 22.5% compared to scenarios without it. Additionally, our algorithms significantly outperform existing methods by 50% in terms of per-user spectral effciency and achieve 90% of the per-user spectral efficiency of the exhaustive search approach while requiring only 0.1% of its computational runtime.

This paper considers wireless power transfer (WPT) for powering low-power devices in massive Machine Type Communication (mMTC) using a distributed massive multiple-input multiple-output (MIMO) system. Each Internet of Things (IoT) device can be served by one or more access points (APs) which is equipped with a massive antenna array. During each time slot, each IoT device transmits pilot sequences to enable APs to perform channel estimation. This process is followed by the WPT using conjugate beamforming. The approach to transmission power control is formulated as a non-convex optimization problem aiming to maximize the total accumulated power achieved by all IoT devices while taking into account the power weights at the APs, pilot power control at the IoT devices, and the non-linearity of practical energy harvesting circuits. An alternating optimization approach is adopted to solve it iteratively, achieving convergence within just a few iterations. Furthermore, since the number of IoT devices might be enormous in mMTC networks, we propose a pilot sharing algorithm allowing IoT devices to reuse pilot sequences effectively. Numerical results are provided to validate the effectiveness of the proposed power control algorithms and the pilot sharing scheme. It is shown that by allowing IoT devices to share the pilot sequences instead of employing the orthogonal pilots, the per-user accumulated performance is enhanced considerably, especially when the number of IoT devices is large relative to the coherence interval. The advantage of using distributed massive MIMO compared to its collocated counterpart is demonstrated in terms of the per-user accumulated power.

Advances in digitalization, particularly those regarding cyber-physical systems (CPS) have stimulated the adoption of digital capabilities such as Industrial IoT, machine learning, cloud services, and the use of digital twins. The increased digital sophistication of CPS is not without risk, particularly regarding the potential for information/cyber security incidents. Whilst the need for security of enterprise information security is not new, A significant challenge is understanding what security standards may be available and applicable when developing security controls and technical measures to protect CPS. This paper explores what research is available regarding the choice and comparison of information/cyber security standards. It provides a snapshot of the security standards landscape at the start of 2024. Issues relating to development and adoption of security standards are examined, illustrated using inconsistencies in language regarding three key terms: availability, integrity, and confidentiality.

Private 5G networks for industrial users are emerging as one of the leading advanced 5G use cases. This timely work presents a comprehensive experimental analysis of a private 5G network conducted in sparse and dense industrial environments at sub-6 GHz. Measured results of the over-the-air error vector magnitude (EVM) are provided, considering signal-to-noise ratio (SNR) for different 5G new radio modulation and coding schemes (MCSs), bandwidths (BWs) and numerologies (subcarrier spacings) using omnidirectional or directional antenna configurations at the transmitter (TX) and the receiver (RX). Channel sounding measurements are also conducted to characterise the channels in terms of root mean square (RMS) delay spread. The measurement results show that channels in the dense industrial environment have greater RMS delay spreads than in the sparse industrial environment due to strong reflected or scattered multipath components with significant delays. This results in higher EVMs and bit error rates (BERs), i.e., as the RMS delay spread increases, a higher SNR is required to meet the EVM limits. It is also observed that using directional antennas at the TX and RX in both environments reduces the RMS delay spread and hence the inter-symbol interference and the EVM. This allows higher MCS modes (e.g., 64 QAM and 256 QAM) to be used for reliable data transmission, significantly improving the bandwidth efficiency and reducing the latency. When evaluating system performance for different BWs and numerologies, using a lower BW and numerology provides a better system performance (lower EVMs and BERs), especially in dense industrial environments.

Factory automation design engineers building the Smart Factory can use wireless 5G broadband networks for added design flexibility. 5G New Radio builds upon previous cellular communications standards to include technology for “massive machine-type communication” and “ultra-reliable and low-latency communication”. In this work, the authors augment an automated guided vehicle with 5G for additional capabilities (e.g., streaming high-resolution video and enabling long-distance teleoperation), increasing the mobile applications for industrial equipment. Such use cases will provide valuable knowledge to engineers examining 5G for novel smart manufacturing solutions. Our 5G private network testbed is a platform for wireless performance research in industrial locations and provides a development mule for flexible smart manufacturing systems. The rival wireless technology to 5G in industrial settings is Wi-Fi and it is included in the testbed. Furthermore, the authors noted challenges, often unconsidered, facing the move to digital manufacturing technologies. Therefore, the authors summarise the emerging challenges when implementing new digital factory systems, including challenges linked to societal concerns around sustainability and supply chain resilience. The new Smart Factory technologies, including 5G communications, will have their roles to play in alleviating these challenges and ensuring economies have resilient future factories.

Millimeter-wave (mmWave) carriers are an essential building block of fifth-generation (5G) systems. Satisfactory performance of the communications over the mmWave spectrum requires an alignment between the signal beam of the transmitter and receiver, achieved via beam training protocols. Nevertheless, beam training is vulnerable to jamming attacks, where the attacker intends to send jamming signals over different spatial directions to confuse legitimate nodes. This paper focuses on defending against this attack in smart factories where a moving Automated Guided Vehicle (AGV) communicates with a base station via a mmWave carrier. We introduce a defensive strategy to cope with jamming attacks, including two stages: jamming detection and jamming mitigation. Developed based on autoencoders, both algorithms can learn the characteristics/features of the received signals at the AGV. They can be employed consecutively before performing the downlink data transmission. In particular, once a jamming attack is identified, the jamming mitigation can be utilized to retrieve the corrupted received signal strength vector, allowing a better decision during the beam training operation. In addition, the proposed algorithm is straightforward and fully compliant with the existing beam training protocols in 5G New Radio. The numerical results show that not only the proposed defensive strategy can capture more than 80% of attack events, but it also improves the average signal-to-interference-plus-noise-ratio significantly, i.e., up to 15 dB.