The increasing demand for high-speed, ultra-reliable and low-latency communications in 5G and beyond networks has led to a significant increase in power consumption, particularly within the Radio Access Network (RAN). This growing energy demand raises operational and sustainability challenges for mobile network operators, requiring novel solutions to enhance energy efficiency while maintaining Quality of Service (QoS). 5G networks are evolving towards disaggregated, programmable, and intelligent architectures, with Open Radio Access Network (O-RAN) spearheaded by the O-RAN Alliance, enabling greater flexibility, interoperability, and cost-effectiveness. However, this disaggregated approach introduces new complexities, especially in terms of power consumption across different network components, including Open Radio Units (RUs), Open Distributed Units (DUs) and Open Central Units (CUs). Understanding the power efficiency of different O-RAN functional splits is crucial for optimising energy consumption and network sustainability. In this paper, we present a comprehensive measurement study of power consumption in RUs, DUs and CUs under varying network loads, specifically analysing the impact of Physical resource block (PRB) utilisation in Split 8 and Split 7.2b. The measurements were conducted on both software-defined radio (SDR)-based RUs and commercial indoor and outdoor RU, as well as their corresponding DU and CU. By evaluating real-world hardware deployments under different operational conditions, this study provides empirical insights into the power efficiency of various O-RAN configurations. The results highlight that power consumption does not scale significantly with network load, suggesting that a large portion of energy consumption remains constant regardless of traffic demand.

This paper explores the application of uplink fronthaul compression techniques within Open RAN (ORAN) to mitigate fronthaul load in decentralized distributed MIMO (DD-MIMO) systems. With the ever-increasing demand for high data rates and system scalability, the fronthaul load becomes a critical bottleneck. Our method uses ORAN compression techniques to efficiently compress the fronthaul signals. The goal is to greatly lower the fronthaul load while having little effect on the overall system performance, as shown by Block Error Rate (BLER) curves. Through rigorous link-level simulations, we compare our quantization strategies against a benchmark scenario with no quantization, providing insights into the trade-offs between fronthaul data rate reduction and link performance integrity. The results demonstrate that our proposed quantization techniques not only lower the fronthaul load but also maintain a competitive link quality, making them a viable solution for enhancing the efficiency of next-generation wireless networks. This study underscores the potential of quantization in O-RAN contexts to achieve optimal balance between system capacity and performance, paving the way for more scalable and robust DD-MIMO deployments.

Effective channel estimation (CE) is critical for optimizing the performance of 5G New Radio (NR) systems, particularly in dynamic environments where traditional methods struggle with complexity and adaptability. This paper introduces GraphNet, a novel, lightweight Graph Neural Network (GNN)-based estimator designed to enhance CE in 5G NR. Our proposed method utilizes a GNN architecture that minimizes computational overhead while capturing essential features necessary for accurate CE. We evaluate GraphNet across various channel conditions, from slow-varying to highly dynamic environments, and compare its performance to ChannelNet, a well-known deep learning-based CE method. GraphNet not only matches ChannelNet’s performance in stable conditions but significantly outperforms it in high-variation scenarios, particularly in terms of Block Error Rate. It also includes built-in noise estimation that enhances robustness in challenging channel conditions. Furthermore, its significantly lighter computational footprint makes GraphNet highly suitable for real-time deployment, especially on edge devices with limited computational resources. By underscoring the potential of GNNs to transform CE processes, GraphNet offers a scalable and robust solution that aligns with the evolving demands of 5G technologies, highlighting its efficiency and performance as a next-generation solution for wireless communication systems.

The advent of 5G networks necessitates more flexible and intelligent architectures, prompting a shift from conventional models to Open Radio Access Networks (0-RAN) augmented with integrated network slicing (NS). The combination of O-RAN with a cell-free architecture improves both coverage and performance, while NS facilitates dynamic resource allocation to support diverse services, such as ultra-reliable low-latency communications (uRLLC) and enhanced mobile broadband (eMBB). This paper introduces a novel NS-enabled, cell-free O-RAN framework designed to optimize resource allocation and power control. In contrast to traditional methods, we adopt a Deep Reinforcement Learning (DRL) approach, leveraging the Soft Actor-Critic (SAC) algorithm to dynamically allocate power and resources across distributed access points (APs), while simultaneously ensuring the efficient management of network slices. The proposed framework aims to maximize the number of admitted slices while minimizing network costs, ensuring optimal data rates for eMBB and low latency for uRLLC services. Through this approach, we demonstrate enhanced flexibility, scalability, and performance in dynamic 5G wireless environments.

This paper explores the vulnerability of downlink cell-free massive MIMO systems to passive and active eaves-dropping, focusing on a 5G New Radio framework. To enhance the security of downlink transmissions over the Physical Downlink Shared Channel (PDSCH) against eavesdropping threats, we propose two novel methods based on cooperative artificial noise (AN). The first approach, called cooperative artificial noise (CAN), involves all access points (APs) broadcasting AN in the null space of the users' channel matrix to confuse potential eavesdroppers. The second approach, named partial artificial noise (PAN), divides the APs into two groups: one group cooperatively transmits AN, while the other group serves the legitimate users. Additionally, we implement three different precoding schemes for legitimate users: maximum ratio transmission, zero-forcing, and minimum mean square error. We conduct link-level simulations of wiretap channels under various frequency-selective fading scenarios and noise conditions, using tapped delay line channel models as defined by the 3GPP TR 38.901 standard. The system's security performance is evaluated by analyzing the block error rate of legitimate users and the block success rate of eavesdroppers. Despite the limitation of having only one antenna per access point, our findings demonstrate that AN can be strategically designed through the cooperation of APs. By designing appropriate groups of APs specifically for generating AN, our second approach, PAN, significantly reduces the block successive rate of eavesdroppers, lowering it from 0.2 without AN to 0.1 with CAN and further down to 0.025 with PAN.

We consider a decentralized distributed massive MIMO (DD-mMIMO) system with limited fronthaul capacity. This is a novel architecture of scalable cell-free massive MIMO (CF-mMIMO). In previous studies, the input signal of the quantizer in CF-mMIMO has been assumed to follow the Gaussian distribution based on the central limit theorem. However, this assumption may not hold when the number of variables is not large. In this paper, we derive the probability distribution function (pdf) for the sum of many products of two Gaussian-distributed variables plus an additional Gaussian-distributed variable. Using the actual pdf, we derive closed-form Bussgang decomposition coefficients and determine the optimum step interval by solving a maximization problem. Additionally, we derive an expression for the spectral efficiency (SE) under quantization. Our results indicate that the Gaussian distribution can be appropriately applied in Bussgang decomposition when the number of quantization bits is limited. However, there exists a 7.8% gap between the Gaussian method and our proposed Non-Gaussian method in terms of mean square error of channel estimates when using 8-bit quantization. Furthermore, the SE is significantly influenced by the choice of the number of quantization bits.

This paper contributes by providing a comprehensive set of block error rate (BLER) vs. signal-to-noise ratio (SNR) curves under additive white Gaussian noise (AWGN) channel conditions for 5G new radio (NR) modulation and coding schemes (MCS) belonging to the 3GPP 5G NR TS 38.214 standard, with low-density parity check (LDPC) coded scenario according to TS 38.212. To enhance practical relevance in the context of O-RAN networks, this paper also introduces the effect of optimum quantization and compares the results without quantization, showing that despite system degradation, the performance remains very close to the unquantized case. By providing this comprehensive dataset, the paper offers valuable insights to support the selection of the most appropriate MCS depending on the required BLER-SNR scenario, serving as a guide in the design of 5G communication systems, for the scheduler, and as lookup tables for the physical layer (PHY) abstraction in link-level simulators (LLS).

A cell-free massive multiple-input multiple-output (MIMO) uplink is investigated in this paper. We address a power allocation design problem that considers two conflicting metrics, namely the sum rate and fairness. Different weights are allocated to the sum rate and fairness of the system, based on the requirements of the mobile operator. The knowledge of the channel statistics is exploited to optimize power allocation. We propose to employ large scale-fading (LSF) coefficients as the input of a twin delayed deep deterministic policy gradient (TD3). This enables us to solve the non-convex sum rate fairness trade-off optimization problem efficiently. Then, we exploit a use-and-then-forget (UatF) technique, which provides a closed-form expression for the achievable rate. The sum rate fairness trade-off optimization problem is subsequently solved through a sequential convex approximation (SCA) technique. Numerical results demonstrate that the proposed algorithms outperform conventional power control algorithms in terms of both the sum rate and minimum user rate. Furthermore, the TD3-based approach can increase the median of sum rate by 16%-46% and the median of minimum user rate by 11%-60% compared to the proposed SCA-based technique. Finally, we investigate the complexity and convergence of the proposed scheme.