Yi Chu

Cell-free (CF) MIMO has emerged as a promising next-generation technology, primarily due to its ability to provide uniformly high-quality service to all user equipment (UEs), regardless of their location. While existing research has extensively explored various aspects of CF systems—including scalability, clustering strategies, power control, and precoding designs—there remains a notable gap in the literature concerning the physical-layer performance of 5G New Radio (NR) within CF architectures. This paper addresses this gap by focusing on the Physical Uplink Shared Channel (PUSCH) transmission over frequency-selective channels. We develop a comprehensive, 3GPP-compliant link-level simulator to evaluate the performance of CF MIMO under realistic propagation conditions. First, we generate results for selected modulation and coding schemes (MCSs) to confirm the simulator’s alignment with expected performance. Then, the effects of key physical-layer parameters—such as subcarrier spacing (SCS), the number of distributed radio units (RUs), and the number of RU antennas—are evaluated using Block Error Rate (BLER) as the primary performance metric. We also compare the results of the CF-MIMO system with a co-located antenna scenario, serving as the baseline for a traditional MIMO system, and confirm that the CF-MIMO system achieves superior performance due to its spatial diversity advantages. The results also show that employing higher SCS values effectively exploits frequency diversity, particularly when the signal bandwidth exceeds the channel’s coherence bandwidth. As expected, increasing the number of RUs significantly improves BLER due to enhanced spatial diversity and reduced UE-RU path loss. We further examine the impact of practical channel estimation by evaluating four different DMRS configurations, confirming that Type 1 with length 2 provides superior performance under the tested conditions. Finally, we investigate the effect of carrier frequency, showing that higher frequencies lead to increased path loss and degraded performance. The findings offer valuable insights into spatial, frequency, and estimation-related interactions in CF 5G NR, while guiding MCS selection for target BLER-SNR levels and enabling PHY abstraction for higher-layer simulations.

Rapidly deployable mobile networks are in demand to support connectivity in underserved areas and on occasions where the existing infrastructure becomes unavailable. Aerial platforms are ideal for delivering such networks benefitting from their flexibility and large coverage. However, compared with the terrestrial networks (TNs), the non-terrestrial networks (NTNs) have significantly different features in terms of energy consumption, network capacity and hardware design. This paper adapts the framework of Open Radio Access Network (O-RAN) and presents the potential of including NTNs into the O-RAN architecture. This gives the NTNs the access to the O-RAN’s core intelligence, the RAN Intelligent Controller (RIC) which allows the optimisation of the network against various targets. This paper also presents a compact neutral host 5G network testbed using software defined radio (SDR) and open-source software. The small form factor of such networks allows them to operate with ground and aerial vehicles, therefore providing coverage at flexible locations without relying on existing infrastructure.

Falls have always been one of the major threats to the health and well-being of elderly people, particularly for those living alone. Both wearable and non-wearable fall detection systems have already been developed. However, the fall detection systems using WiFi channel state information (CSI) have attracted a significant interest from researchers due to their non-intrusive and low-cost nature. There are existing machine learning (ML) based fall detection systems using WiFi CSI; however, most systems trained with comprehensive datasets tend to achieve relatively lower accuracy compared to that of the systems trained with less inclusive datasets. To address these issues, we propose a novel, deep learning based fall detection technique. First, we implement different WiFi CSI collection tools and evaluate their potential for fall detection. To develop a highly accurate fall detection technique, we construct a comprehensive dataset, which consists of over 700 CSI samples including different types of falls and other daily activities, performed in four different indoor environments on and off the dominant paths. With this dataset, we then develop a deep learning based classifier using an image classification algorithm. The proposed technique, unlike the other fall detection systems, only requires down sampling and reshaping in pre-processing. The proposed fall detection system is evaluated with the constructed dataset, and it outperforms two other existing systems. It achieves over 96% accuracy for CSI collected in all four environments and 99% accuracy for CSI collected in certain combinations of the environments.

We present the proof-of-concept pointing, acquisition and tracking system designed for steering and stabilising an optical beam from a high altitude platform to an optical ground station. We demonstrate the performance on a rotating platform.

Wireless network coverage planning is crucial for mobile network operators and fixed wireless network providers to estimate the performance of their networks and plan future antenna mast deployments. To generate accurate coverage maps for target buildings, traditional wireless coverage planning tools either require manual input of Customer-Premises Equipment (CPE) antenna locations or need to compute received signal strength from nearby Access Points (APs) to all geolocations in the area of interest which consumes computational resource unnecessarily. In this paper we propose a Deep Learning (DL) based universal enhancement to wireless coverage planning tools which automatically extracts potential CPE antenna locations from aerial images of the target buildings. We evaluate the performance of the pixel level object detection provided by Mask Region-based Convolutional Neural Network (Mask R-CNN) trained on an image dataset with suburban and rural residential properties across North Yorkshire, UK. We also demonstrate a complete task flow to generate informative building coverage reports while combining the DL based building detection with the WISDM industrial wireless coverage planning system.

This paper presents the feasibility study of deploying quantum key distribution (QKD) from high altitude platforms (HAPs), as a way of securing future communications applications and services. The paper provides a thorough review of the state of the art HAP technologies and summarises the benefits that HAPs can bring to the QKD services. A detailed link budget analysis is presented in the paper to evaluate the feasibility of delivering QKD from stratospheric HAPs flying at 20 km altitude. The results show a generous link budget under most operating conditions which brings the possibility of using diverged beams, thereby simplifying the pointing, acquisition and tracking of the optical system on the HAPs and ground, potentially widening the range of future use cases where QKD could be a viable solution.

With the fast growing number of wireless devices and demand of user data, the backhaul load becomes a bottleneck in wireless networks. Physical‐layer network coding (PNC) allows access points to relay‐compressed, network‐coded user data, therefore reducing the backhaul traffic. In this paper, an implementation of uplink network‐coded modulation (NetCoM) with PNC is presented. A five‐node prototype NetCoM system is established using Universal Software Radio Peripherals, and a practical PNC scheme designed for binary systems is utilized. An orthogonal frequency‐division multiplexing waveform implementation and the practical challenges (eg, device synchronization and clock drift) of applying orthogonal frequency‐division multiplexing to NetCoM are discussed. To the best of our knowledge, this is the first PNC implementation in an uplink scenario in radio access networks, and our prototype provides an industrially applicable implementation of the proposed NetCoM with PNC approach.