Improving queue stability in wireless multicast with network coding

Moghadam, Nadieh

doi:10.1109/icc.2015.7248847

Cited by 12 publications

(11 citation statements)

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“…Queuing and Power control in Multicast Systems: Multicast queuing and scheduling is studied in [3,[18][19][20]. The works in [3,18,19] propose schemes for network-coded multicast systems and analyze the stability of the proposed multicast queues. Unlike these works, we use, as in our previous work [7,8], a simple uncoded multicast queue, which is always stable.…”

Section: Related Workmentioning

confidence: 99%

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Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Raghu

Panju

Aggarwal

et al. 2021

Entropy

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Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.

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Section: Related Workmentioning

confidence: 99%

“…Additionally, the policy does not adapt to changing the system statistics, which in turn, depends on the power control policy. (3) The queuing schemes and power control are dealt with individually. This paper tries to overcome the above limitations of the scheme in [8].…”

Section: Introductionmentioning

confidence: 99%

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Raghu

Panju

Aggarwal

et al. 2021

Entropy

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“…A multicast queue with network coding is studied in [4], [5] with infinite library of files. The case of broadcast systems with one server transmitting to multiple users is studied in [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Based Power Control for Wireless Multicast Systems

Raghu

Upadhyaya

Panju

et al. 2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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We consider a multicast scheme recently proposed for a wireless downlink in [1]. It was shown earlier that power control can significantly improve its performance. However for this system, obtaining optimal power control is intractable because of a very large state space. Therefore in this paper we use deep reinforcement learning where we use function approximation of the Q-function via a deep neural network. We show that optimal power control can be learnt for reasonably large systems via this approach. The average power constraint is ensured via a Lagrange multiplier, which is also learnt. Finally, we demonstrate that a slight modification of the learning algorithm allows the optimal control to track the time varying system statistics.

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“…In chapter II, we developed a wireless multicast virtual queue model for the special case of two receivers [45], where the transmitter had one sub-queue for each receiver.…”

Section: A Introductionmentioning

confidence: 99%

“…However, the authors assume that the BS has only one file of N packets and there are new arriving files. Along a different line, [45] proposed a new queuing model to maximize the input rate with two multicast receivers. Additionally, stability properties were also evaluated in [19] for broadcast/multicast erasure channels with NC.…”

Section: A Introductionmentioning

confidence: 99%

Queue stability analysis in network coded wireless multicast.

Mohamadi-Moghadam¹

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In this dissertation queue stability in wireless multicast networks with packet erasure channels is studied. Our focus is on optimizing packet scheduling so as to maximize throughput.Specifically, new queuing strategies consisting of several sub-queues are introduced, where all newly arrived packets are first stored in the main sub-queue on a first-come-first-served basis. Using the receiver feedback, the transmitter combines packets from different sub-queues for transmission.Our objective is to maximize the input rate under the queue stability constraints.Two packet scheduling and encoding algorithms have been developed. First, the optimization problem is formulated as a linear programming (LP) problem, according to which a network coding based optimal packet scheduling scheme is obtained. Second, the Lyapunov optimization model is adopted and decision variables are defined to derive a network coding based packet scheduling algorithm, which has significantly less complexity and smaller queue backlog compared with the LP solution. Further, an extension of the proposed algorithm is derived to meet the requirements of time-critical data transmission, where each packet expires after a predefined deadline and then dropped from the system. To minimize the average transmission power, we further derive a v scheduling policy that simultaneously minimizes both power and queue size, where the transmitter may choose to be idle to save energy consumption. Moreover, a redundancy in the schedules is inadvertently revealed by the algorithm. By detecting and removing the redundancy we further reduce the system complexity.Finally, the simulation results verify the effectiveness of our proposed algorithms over existing works.vi

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Improving queue stability in wireless multicast with network coding

Cited by 12 publications

References 12 publications

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Deep Reinforcement Learning Based Power Control for Wireless Multicast Systems

Queue stability analysis in network coded wireless multicast.

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