Smart scheduling and feedback allocation over non-stationary wireless channels

Karaca, Mehmet; Alpcan, Tansu; Erçetin, Özgür

doi:10.1109/icc.2012.6364878

Cited by 10 publications

(11 citation statements)

References 12 publications

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“…This improvement is due to the fact that we consider the input as a tenth order polynomial of the connectivity duration instead of order one. In addition, for the purpose of comparison, a Gaussian process regression (GPR)-based channel estimation method proposed in [21] is adopted to predict consecutive non-blocking durations, which is referred to as the "GPR" baseline. Fig.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Predictive Ultra-Reliable Communication: A Survival Analysis Perspective

et al. 2021

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Ultra-reliable communication (URC) is a key enabler for supporting immersive and mission-critical 5G applications. Meeting the strict reliability requirements of these applications is challenging due to the absence of accurate statistical models tailored to URC systems. In this letter, the wireless connectivity over dynamic channels is characterized via statistical learning methods. In particular, model-based and data-driven learning approaches are proposed to estimate the non-blocking connectivity statistics over a set of training samples with no knowledge on the dynamic channel statistics. Using principles of survival analysis, the reliability of wireless connectivity is measured in terms of the probability of channel blocking events. Moreover, the maximum transmission duration for a given reliable non-blocking connectivity is predicted in conjunction with the confidence of the inferred transmission duration. Results show that the accuracy of detecting channel blocking events is higher using the model-based method for low to moderate reliability targets requiring low sample complexity. In contrast, the data-driven method yields a higher detection accuracy for higher reliability targets at the cost of 100× sample complexity.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Predictive Ultra-Reliable Communication: A Survival Analysis Perspective

et al. 2021

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“…k∈K . For accurate CSI predictions, it is essential to acquire as much information about the CSI over the network [9]. In this view, we maximize k j † k (t)λ k (t) at each t while minimizing the loss F (w, D).…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…This yields that the minimization of ε(T ) can be achieved by minimizing its upper bound defined in (9). Henceforth, the equivalent form of (4) is given as follows:…”

Section: A Decoupling (4) Via Dual Formulationmentioning

confidence: 99%

Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation

Wadu

Samarakoon

Bennis

2020

2020 IEEE Wireless Communications and Networking Conference (WCNC)

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In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to a centralized training-based solution, under imperfect channel state information (CSI). First, the problem is cast as a stochastic optimization problem over a predefined training duration and solved using the Lyapunov optimization framework. In order to learn and track the wireless channel, a Gaussian process regression (GPR)-based channel prediction method is leveraged and incorporated into the scheduling decision. The proposed scheduling policies are evaluated via numerical simulations, under both perfect and imperfect CSI. Results show that the proposed method reduces the loss of accuracy up to 25.8 % compared to state-of-the-art client scheduling and RB allocation methods.

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“…In [19] and [20], the authors proposed joint channel estimation and opportunistic scheduling algorithm by assuming independent and identically distributed (iid) and timecorrelated channel processes, respectively. In [21], the authors proposed to estimate the channel statistics by using some portion of the time slots for observation slot with some probability over iid channels.…”

Section: Related Workmentioning

confidence: 99%

Joint Opportunistic Scheduling and Selective Channel Feedback

Karaca

Sarıkaya

Erçetin

et al. 2013

IEEE Trans. Wireless Commun.

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Abstract-It is well known that Max-Weight type scheduling algorithms are throughput optimal since they achieve the maximum throughput while maintaining the network stability. However, the majority of existing works employing Max-Weight algorithm require the complete channel state information (CSI) at the scheduler without taking into account the associated overhead. In this work, we design a Scheduling and Selective Feedback algorithm (SSF) taking into account the overhead due to acquisition of CSI. SSF algorithm collects CSI from only those users with sufficiently good channel quality so that it always schedules the user with the highest queue backlog and channel rate product at every slot. We characterize the achievable rate region of SSF algorithm by showing that SSF supports 1 + fraction of the rate region when CSI from all users are collected. We also show that the value of depends on the expected number of users which do not send back their CSI to the base station. For homogenous and heterogeneous channel conditions, we determine the minimum number of users that must be present in the network so that the rate region is expanded, i.e., > 0. We also demonstrate numerically in a realistic simulation setting that this rate region can be achieved by collecting CSI from only less than 50% of all users in a CDMA based cellular network utilizing high data rate (HDR) protocol.

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Smart scheduling and feedback allocation over non-stationary wireless channels

Cited by 10 publications

References 12 publications

Predictive Ultra-Reliable Communication: A Survival Analysis Perspective

Predictive Ultra-Reliable Communication: A Survival Analysis Perspective

Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation

Joint Opportunistic Scheduling and Selective Channel Feedback

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