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1. Fairness and Delay in Heterogeneous Half- and Full-Duplex Wireless Networks

   https://doi.org/10.1109/ACSSC.2018.8645117  

   Chen, Tingjun; Diakonikolas, Jelena; Ghaderi, Javad; Zussman, Gil (October 2018, Conference record - Asilomar Conference on Signals, Systems, & Computers)

   Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. In [1], we focused on the design and performance evaluation of scheduling algorithms for heterogeneous HD-FD networks and presented the distributed Hybrid-Greedy Maximal Scheduling (H-GMS) algorithm. H-GMS combines the centralized Greedy Maximal Scheduling (GMS) and a distributed queue-based random-access mechanism, and is throughput-optimal. In this paper, we analyze the delay performance of H-GMS by deriving two lower bounds on the average queue length. We also evaluate the fairness and delay performance of H-GMS via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves$$16-30\times$$ better delay performance and improves fairness between FD and HD users by up to 50% compared with the fully decentralized Q-CSMA algorithm. 

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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. Fairness and Delay in Heterogeneous Half- and Full-Duplex Wireless Networks

   Tingjun Chen, Jelena Diakonikolasy (January 2018, Asilomar Conference on Signals, Systems, and Computers)

   Abstract—Full-duplex (FD) wireless is an attractive commu- nication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite sig- nificant progress on the physical layer development, the chal- lenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. In [1], we focused on the design and performance evaluation of scheduling algorithms for heterogeneous HD-FD networks and presented the distributed Hybrid-Greedy Maximal Scheduling (H-GMS) algorithm. H-GMS combines the central- ized Greedy Maximal Scheduling (GMS) and a distributed queue- based random-access mechanism, and is throughput-optimal. In this paper, we analyze the delay performance of H-GMS by deriving two lower bounds on the average queue length. We also evaluate the fairness and delay performance of H-GMS via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves 16–30× better delay performance and improves fairness between FD and HD users by up to 50% compared with the fully decentralized Q-CSMA algorithm. 

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4. Hybrid Scheduling in Heterogeneous Half- and Full-Duplex Wireless Networks

   https://doi.org/1801.01108v2  

   Tingjun Chen, Jelena Diakonikolas (January 2018, ArXiv.org)

   Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. Therefore, we focus on the design and performance evaluation of scheduling algorithms for infrastructure-based heterogeneous HD-FD networks (composed of HD and FD users). We first show that centralized GreedyMaximal Scheduling (GMS) is throughput-optimal in heterogeneous HD-FD networks. We propose the Hybrid-GMS (H-GMS) algorithm, a distributed implementation of GMS that combines GMS and a queue-based random-access mechanism. We prove that H-GMS is throughputoptimal. Moreover, we analyze the delay performance of H-GMS by deriving lower bounds on the average queue length. We further demonstrate the benefits of upgrading HD nodes to FD nodes in terms of throughput gains for individual nodes and the whole network. Finally, we evaluate the performance of HGMS and its variants in terms of throughput, delay, and fairness between FD and HD users via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves 16–30× better delay performance and improves fairness between HD and FD users by up to 50% compared with the fully decentralized Q-CSMA algorithm. 

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5. Hybrid scheduling in heterogeneous half- and full-duplex wireless networks

   Chen, T.; Diakonikolas, J.; Ghaderi, J.; Zussman, G. (January 2020, IEEE/ACM Transactions on Networking)

   Abstract—Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. Therefore, we focus on the design and performance evaluation of scheduling algorithms for infrastructure-based heterogeneous HD-FD networks (composed of HD and FD users). We first show that centralized Greedy Maximal Scheduling (GMS) is throughput-optimal in heterogeneous HD-FD networks. We propose the Hybrid-GMS (H-GMS) algorithm, a distributed implementation of GMS that combines GMS and a queue-based random-access mechanism. We prove that H-GMS is throughputoptimal. Moreover, we analyze the delay performance of H-GMS by deriving lower bounds on the average queue length. We further demonstrate the benefits of upgrading HD nodes to FD nodes in terms of throughput gains for individual nodes and the whole network. Finally, we evaluate the performance of HGMS and its variants in terms of throughput, delay, and fairness between FD and HD users via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves 16–30× better delay performance and improves fairness between HD and FD users by up to 50% compared with the fully decentralized Q-CSMA algorithm. 

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