Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN

The next-generation wireless networks are required to satisfy a variety of services and criteria concurrently. To address upcoming strict criteria, a new open radio access network (O-RAN) with distinguishing features such as flexible design, disaggregated virtual and programmable components, and intelligent closed-loop control was developed. O-RAN slicing is being investigated as a critical strategy for ensuring network quality of service (QoS) in the face of changing circumstances. However, distinct network slices must be dynamically controlled to avoid service level agreement (SLA) variation caused by rapid changes in the environment. Therefore, this paper introduces a novel framework able to manage the network slices through provisioned resources intelligently. Due to diverse heterogeneous environments, intelligent machine learning approaches require sufficient exploration to handle the harshest situations in a wireless network and accelerate convergence. To solve this problem, a new solution is proposed based on evolutionary-based deep reinforcement learning (EDRL) to accelerate and optimize the slice management learning process in the radio access network's (RAN) intelligent controller (RIC) modules. To this end, the O-RAN slicing is represented as a Markov decision process (MDP) which is then solved optimally for resource allocation to meet service demand using the EDRL approach. In terms of reaching service demands, simulation results show that the proposed approach outperforms the DRL baseline by 62.2%.Comment: This paper has been accepted for the 2022 IEEE Globecom Workshops (GC Wkshps

Autoencoder-based Radio Frequency Interference Mitigation For SMAP Passive Radiometer

Passive space-borne radiometers operating in the 1400-1427 MHz protected frequency band face radio frequency interference (RFI) from terrestrial sources. With the growth of wireless devices and the appearance of new technologies, the possibility of sharing this spectrum with other technologies would introduce more RFI to these radiometers. This band could be an ideal mid-band frequency for 5G and Beyond, as it offers high capacity and good coverage. Current RFI detection and mitigation techniques at SMAP (Soil Moisture Active Passive) depend on correctly detecting and discarding or filtering the contaminated data leading to the loss of valuable information, especially in severe RFI cases. In this paper, we propose an autoencoder-based RFI mitigation method to remove the dominant RFI caused by potential coexistent terrestrial users (i.e., 5G base station) from the received contaminated signal at the passive receiver side, potentially preserving valuable information and preventing the contaminated data from being discarded.Comment: To be published in IEEE IGARSS 202

SCC5G: A PQC-based Architecture for Highly Secure Critical Communication over Cellular Network in Zero-Trust Environment

5G made a significant jump in cellular network security by offering enhanced subscriber identity protection and a user-network mutual authentication implementation. However, it still does not fully follow the zero-trust (ZT) requirements, as users need to trust the network, 5G network is not necessarily authenticated in each communication instance, and there is no mutual authentication between end users. When critical communications need to use commercial networks, but the environment is ZT, specific security architecture is needed to provide security services that do not rely on any 5G network trusted authority. In this paper, we propose SCC5G Secure Critical-mission Communication over a 5G network in ZT setting. SCC5G is a post-quantum cryptography (PQC) security solution that loads an embedded hardware root of authentication (HRA), such as physically unclonable functions (PUF), into the users' devices, to achieve tamper-resistant and unclonability features for authentication and key agreement. We evaluate the performance of the proposed architecture through an exhaustive simulation of a 5G network in an ns-3 network simulator. Results verify the scalability and efficiency of SCC5G by showing that it poses only a few kilobytes of traffic overhead and adds only an order of $O(0.1)$ second of latency under the normal traffic load