15 research outputs found
Scheduling with Rate Adaptation under Incomplete Knowledge of Channel/Estimator Statistics
In time-varying wireless networks, the states of the communication channels
are subject to random variations, and hence need to be estimated for efficient
rate adaptation and scheduling. The estimation mechanism possesses inaccuracies
that need to be tackled in a probabilistic framework. In this work, we study
scheduling with rate adaptation in single-hop queueing networks under two
levels of channel uncertainty: when the channel estimates are inaccurate but
complete knowledge of the channel/estimator joint statistics is available at
the scheduler; and when the knowledge of the joint statistics is incomplete. In
the former case, we characterize the network stability region and show that a
maximum-weight type scheduling policy is throughput-optimal. In the latter
case, we propose a joint channel statistics learning - scheduling policy. With
an associated trade-off in average packet delay and convergence time, the
proposed policy has a stability region arbitrarily close to the stability
region of the network under full knowledge of channel/estimator joint
statistics.Comment: 48th Allerton Conference on Communication, Control, and Computing,
Monticello, IL, Sept. 201
Exploiting Hybrid Channel Information for Downlink Multi-User MIMO Scheduling
We investigate the downlink multi-user MIMO (MU-MIMO) scheduling problem in
the presence of imperfect Channel State Information at the transmitter (CSIT)
that comprises of coarse and current CSIT as well as finer but delayed CSIT.
This scheduling problem is characterized by an intricate `exploitation -
exploration tradeoff' between scheduling the users based on current CSIT for
immediate gains, and scheduling them to obtain finer albeit delayed CSIT and
potentially larger future gains. We solve this scheduling problem by
formulating a frame based joint scheduling and feedback approach, where in each
frame a policy is obtained as the solution to a Markov Decision Process. We
prove that our proposed approach can be made arbitrarily close to the optimal
and then demonstrate its significant gains over conventional MU-MIMO
scheduling.Comment: Expanded version: Accepted WiOpt 201
Exploiting channel memory for joint estimation and scheduling in downlink networks
We address the problem of opportunistic multiuser scheduling in downlink networks with Markov-modeled outage channels. We consider the scenario in which the scheduler does not have full knowledge of the channel state information, but instead estimates the channel state information by exploiting the memory inherent in the Markov channels along with ARQ-styled feedback from the scheduled users. Opportunistic scheduling is optimized in two stages: (1) Channel estimation and rate adaptation to maximize the expected immediate rate of the scheduled user; (2) User scheduling, based on the optimized immediate rate, to maximize the overall long term sum-throughput of the downlink. The scheduling problem is a partially observable Markov decision process with the classic ‘exploitation vs exploration ’ trade-off that is difficult to quantify. We therefore study the problem in the framework of restless multi-armed bandit processes and perform a Whit-tle’s indexability analysis. Whittle’s indexability is traditionally known to be hard to establish and the index policy derived based on Whittle’s indexability is known to have optimality properties in various settings. We show that the problem of downlink scheduling under imperfect channel state information is Whittle indexable and derive the Whittle’s index policy in closed form. Via extensive numerical experiments, we show that the index policy has near-optimal performance. Our work reveals that, under incomplete channel state infor-mation, exploiting channel memory for opportunistic scheduling can result in significant performance gains and that almost all of these gains can be realized using an easy-to-implement index policy
Low-complexity Optimal Scheduling over Correlated Fading Channels with ARQ Feedback
Abstract—We investigate the downlink scheduling problem under Markovian ON/OFF fading channels, where the instantaneous channel state information is not directly accessible, but is revealed via ARQ-type feedback. The scheduler can exploit the temporal correlation/channel memory inherent in the Markovian channels to improve network performance. However, designing low-complexity and throughput-optimal algorithms under temporal correlation is a challenging problem. In this paper, we find that under an average number of transmissions constraint, a low-complexity index policy is throughput-optimal. The policy uses Whittle’s index value, which was previously used to capture opportunistic scheduling under temporally correlated channels. Our results build on the interesting finding that, under the intricate queue length and channel memory evolutions, the importance of scheduling a user is captured by a simple multiplication of its queue length and Whittle’s index value. The proposed queue-weighted index policy has provably low complexity which is significantly lower than existing optimal solutions. I
Low-Complexity Optimal Scheduling over Time-Correlated Fading Channels with ARQ Feedback
Unsupervised Learning of Disentangled Location Embeddings
2020 International Joint Conference on Neural Networks (IJCNN 2020)United State