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1. Deep Q-Network for 5G NR Downlink Scheduling

   https://doi.org/10.1109/ICCWorkshops53468.2022.9814547  

   AlQwider, Walaa; Rahman, Talha Faizur; Marojevic, Vuk (July 2022, Deep Q-Network for 5G NR Downlink Scheduling)

   The Third Generation Partnership Project (3GPP) introduced the fifth generation new radio (5G NR) specifications which offer much higher flexibility than legacy cellular communications standards to better handle the heterogeneous service and performance requirements of the emerging use cases. This flexibility, however, makes the resources management more complex. This paper therefore designs a data driven resource allocation method based on the deep Q-network (DQN). The objective of the proposed model is to maximize the 5G NR cell throughput while providing a fair resource allocation across all users. Numerical results using a 3GPP compliant 5G NR simulator demonstrate that the DQN scheduler better balances the cell throughput and user fairness than existing schedulers. 

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2. Delay-aware Cellular Traffic Scheduling with Deep Reinforcement Learning

   https://doi.org/10.1109/GLOBECOM42002.2020.9322560  

   Zhang, Ticao; Shen, Shuyi; Mao, Shiwen; Chang, Gee-Kung (December 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference)

   null (Ed.)

   Abstract: Radio access network (RAN) in 5G is expected to satisfy the stringent delay requirements of a variety of applications. The packet scheduler plays an important role by allocating spectrum resources to user equipments (UEs) at each transmit time interval (TTI). In this paper, we show that optimal scheduling is a challenging combinatorial optimization problem, which is hard to solve within the channel coherence time with conventional optimization methods. Rule-based scheduling methods, on the other hand, are hard to adapt to the time-varying wireless channel conditions and various data request patterns of UEs. Recently, integrating artificial intelligence (AI) into wireless networks has drawn great interest from both academia and industry. In this paper, we incorporate deep reinforcement learning (DRL) into the design of cellular packet scheduling. A delay-aware cell traffic scheduling algorithm is developed to map the observed system state to scheduling decision. Due to the huge state space, a recurrent neural network (RNN) is utilized to approximate the optimal action-policy function. Different from conventional rule-based scheduling methods, the proposed scheme can learn from the interactions with the environment and adaptively choosing the best scheduling decision at each TTI. Simulation results show that the DRL-based packet scheduling can achieve the lowest average delay compared with several conventional approaches. Meanwhile, the UEs' average queue lengths can also be significantly reduced. The developed method also exhibits great potential in real-time scheduling in delay-sensitive scenarios. 

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3. 5G NR Jamming, Spoofing, and Sniffing: Threat Assessment and Mitigation

   Lichtman, Marc; Rao, Raghunandan; Marojevic, Vuk; Reed, Jeffrey (July 2018, IEEE International Conference on Communications workshops)

   In December 2017, the Third Generation Partnership Project (3GPP) released the first set of specifications for 5G New Radio (NR), which is currently the most widely accepted 5G cellular standard. 5G NR is expected to replace LTE and previous generations of cellular technology over the next several years, providing higher throughput, lower latency, and a host of new features. Similar to LTE, the 5G NR physical layer consists of several physical channels and signals, most of which are vital to the operation of the network. Unfortunately, like for any wireless technology, disruption through radio jamming is possible. This paper investigates the extent to which 5G NR is vulnerable to jamming and spoofing, by analyzing the physical downlink and uplink control channels and signals. We identify the weakest links in the 5G NR frame, and propose mitigation strategies that should be taken into account during implementation of 5G NR chipsets and base stations. 

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4. NR-Scope: A Practical 5G Standalone Telemetry Tool

   https://doi.org/10.1145/3680121.3697808  

   Wan, Haoran; Cao, Xuyang; Marder, Alexander; Jamieson, Kyle (December 2024, ACM)

   NextG cellular networks are designed to meet Quality of Service requirements for various applications in and beyond smartphones and mobile devices. However, lacking introspection into the 5G Radio Access Network (RAN) application and transport layer designers are ill-poised to cope with the vagaries of the wireless last hop to a mobile client, while 5G network operators run mostly closed networks, limiting their potential for co-design with the wider internet and user applications. This paper presents NR-Scope, a passive, incrementally-deployable, and independently-deployable Standalone 5G network telemetry system that can stream fine-grained RAN capacity, latency, and retransmission information to application servers to enable better millisecond scale, application-level decisions on offered load and bit rate adaptation than end-to-end latency measurements or end-to-end packet losses currently permit. Our experimental evaluation on various 5G Standalone base stations demonstrates NR-Scope can achieve less than 0.1% throughput error estimation for every UE in a RAN. The code is available at https://github.com/PrincetonUniversity/NR-Scope. 

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5. A Co-Scheduling Framework for DNN Models on Mobile and Edge Devices with Heterogeneous Hardware

   https://doi.org/10.1109/TMC.2021.3107424  

   Xu, Zhiyuan; Yang, Dejun; Yin, Chengxiang; Tang, Jian; Wang, Yanzhi; Xue, Guoliang (August 2021, IEEE Transactions on Mobile Computing)

   With the emergence of more and more powerful chipsets and hardware and the rise of Artificial Intelligence of Things (AIoT), there is a growing trend for bringing Deep Neural Network (DNN) models to empower mobile and edge devices with intelligence such that they can support attractive AI applications on the edge in a real-time or near real-time manner. To leverage heterogeneous computational resources (such as CPU, GPU, DSP, etc) to effectively and efficiently support concurrent inference of multiple DNN models on a mobile or edge device, we propose a novel online Co-Scheduling framework based on deep REinforcement Learning (DRL), which we call COSREL. COSREL has the following desirable features: 1) it achieves significant speedup over commonly-used methods by efficiently utilizing all the computational resources on heterogeneous hardware; 2) it leverages emerging Deep Reinforcement Learning (DRL) to make dynamic and wise online scheduling decisions based on system runtime state; 3) it is capable of making a good tradeoff among inference latency, throughput and energy efficiency; and 4) it makes no changes to given DNN models, thus preserves their accuracies. To validate and evaluate COSREL, we conduct extensive experiments on an off-the-shelf Android smartphone with widely-used DNN models to compare it with three commonly-used baselines. Our experimental results show that 1) COSREL consistently and significantly outperforms all the baselines in terms of both throughput and latency; and 2) COSREL is generally superior to all the baselines in terms of energy efficiency. 

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