The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose SMART, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks.
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Indistinguishability Prevents Scheduler Side Channels in Real-Time Systems
Scheduler side-channels can leak critical information in real-time systems, thus posing serious threats to many safety-critical applications. The main culprit is the inherent determinism in the runtime timing behavior of such systems, e.g., the (expected) periodic behavior of critical tasks. In this paper, we introduce the notion of "schedule indistinguishability/", inspired by work in differential privacy, that introduces diversity into the schedules of such systems while offering analyzable security guarantees. We achieve this by adding a sufficiently large (controlled) noise to the task schedules in order to break their deterministic execution patterns. An "epsilon-Scheduler" then implements schedule indistinguishability in real-time Linux. We evaluate our system using two real applications: (a) an autonomous rover running on a real hardware platform (Raspberry Pi) and (b) a video streaming application that sends data across large geographic distances. Our results show that the epsilon-Scheduler offers better protection against scheduler side-channel attacks in real-time systems while still maintaining good performance and quality-of-service(QoS) requirements.
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- Award ID(s):
- 1718952
- PAR ID:
- 10313430
- Date Published:
- Journal Name:
- Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose \name{}, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks.more » « less
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This paper presents a new Single Source Shortest Path (SSSP) algorithm for GPUs. Our key advancement is an improved work scheduler, which is central to the performance of SSSP algorithms. Previous GPU solutions for SSSP use simple work schedulers that can be implemented efficiently on GPUs but that produce low quality schedules. Such solutions yield poor work efficiency and can underutilize the hardware due to a lack of parallelism. Our solution introduces a more sophisticated work scheduler---based on a novel highly parallel approximate priority queue---that produces high quality schedules while being efficiently implementable on GPUs. To evaluate our solution, we use 226 graph inputs from the Lonestar 4.0 benchmark suite and the SuiteSparse Matrix Collection, and we find that our solution outperforms the previous state-of-the-art solution by an average of 2.9×, showing that an efficient work scheduling mechanism can be implemented on GPUs without sacrificing schedule quality. While this paper focuses on the SSSP problem, it has broader implications for the use of GPUs, illustrating that seemingly ill-suited data structures, such as priority queues, can be efficiently implemented for GPUs if we use the proper software structure.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3460120.3484769
