RAN slicing in Open RAN (O-RAN) architectures requires dynamic and intelligent resource allocation to ensure both high resource efficiency and strict compliance with Service Level Agreements (SLAs). However, conventional reinforcement learning (RL) methods struggle in this setting due to their unsafe exploration behavior, often resulting in frequent SLA violations. In this paper, we propose a safe and sample-efficient model-based RL (MBRL) framework tailored for SLA-aware RAN slicing. Our agent leverages an online kernelized quantile regressor to estimate the lower quantile of key performance indicators (KPIs), enabling direct modeling of SLA violation risk. To ensure safety during online adaptation, the policy incorporates an uncertainty-aware safety margin, promoting conservative decisions under high model uncertainty. Extensive simulations demonstrate that our method reduces SLA violations to under 1%, while maintaining high resource efficiency and achieving fast, stable convergence.

In the evolving landscape of Open Radio Access Networks (O-RAN), achieving both high energy efficiency and computational acceleration is crucial for optimizing network performance. Low-Density Parity-Check (LDPC) decoding, a computationally intensive process widely used in wireless communication, is commonly accelerated by hardware to meet the stringent demands of real-time processing. However, balancing this acceleration with energy efficiency remains a significant challenge. In this paper, we propose a Deep Reinforcement Learning (DRL)-based smart offloading strategy to achieve low-latency, energy-efficient LDPC acceleration using an FPGA-based hardware accelerator. We introduce a Quantile Regression Forest (QRF)-based method for predicting latency and energy consumption in real time, enabling per-block decision-making, alongside a Proximal Policy Optimization (PPO)-based algorithm for dynamically adapting offloading weights to optimize energy efficiency. The proposed strategy is implemented on the OpenAir-Interface5G (OAI) and FlexRIC platforms to demonstrate its feasibility within an O-RAN testbed. Evaluation results show that the method adapts dynamically to varying network conditions in under 5 seconds, making offloading decisions in less than 5 µs, resulting in up to 20% energy savings and 14% latency reduction.

As Radio Access Networks (RANs) evolve towards more intelligent and software-defined paradigms, an increasing number of workloads are being offloaded to hardware accelerators. A significant challenge in this context is to minimize CPU consumption while ensuring low and stable latency in processing acceleration. Tailoring load allocation to meet varying latency requirements in different application scenarios is essential. In this paper, we present an application of RF Network on Chip (RFNoC) technology for FPGA-based acceleration of Low-Density Parity-Check (LDPC) code and Polar code. We conduct a comparative analysis of CPU and FPGA processing performance in OpenAinInterface (OAI) platform, with and without the Data Plane Development Kit (DPDK), to determine optimal load allocation for diverse data requirements. The findings indicate that RFNoC serves as an effective FPGA accelerator for the LDPC process, offering up to a fivefold increase in acceleration and significantly reducing processing delay jitter. Furthermore, the experimental outcomes provide a foundation for future research endeavors, specifically in the realm of efficient load optimization strategies.

Network slicing is considered one of the key technologies of the fifth-generation (5G) mobile networks. Especially in Local 5G networks, to ensure that one slice does not affect other slices, narrowband spectrum allocation appears to be particularly important. Band Pass Filter (BPF) is one of the most important components to isolate the spectrum resource of each Local 5G RAN slice. However, it is difficult to buy a BPF with a narrow band frequency on the market. Moreover, due to complicated manufacturing procedures, the price of BPF is usually very expensive, which increases the cost of establishing a Local 5G base station for small enterprises. In our design, we present a simple waveguide filter based on the theoretical principles of a low-pass prototype of Chebyshev approximation. The waveguide filter is designed with a center frequency at 4.75 GHz and narrow bandwidth of 100MHz. After simply forging and evaluation, experimental results prove the feasibility of our method in the processing of low-cost narrowband BPFs.

Private mobile networks such as private LTE/local 5G, which support flexibly configured and empowered innovative technologies that are not feasible in closed public mobile networks recently, have been catching much attention both in academia and in industries. In this paper, we design and implement the automatic check-in service as an example of value-added services of private mobile networks utilizing the flexibility of softwarization. To alleviate the inherent coverage problem of a private mobile network, we integrate our private mobile network with a public LTE by sharing the subscriber database so that a user can use the automatic check-in services deployed in various private mobile networks with only one SIM issued by a public network. We perform field tests in a private mobile network and also a private-public hybrid mobile network and disclose that the users' check-out behavior is predictable through numerical analyses. Based on the finding, we introduce two machine learning-based inference mechanisms that can predict a user's check-out behavior at an inference accuracy of 83% and 93% in a private network and a hybrid one separately. We believe this paper can provide valuable experience for those who are developing their private mobile networks.