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Opportunistic Scheduling Using Statistical Information of Wireless Channels

Zhouyou Gu, Wibowo Hardjawana, Branka Vucetic

TL;DR

This paper forms a weight optimization problem using the mean and variance of users’ signal-to-noise ratios (SNRs) to construct constraints bounding users’ feasible average rates and develops an iterative solver for the problem and proves that it finds the optimal weights.

Abstract

This paper considers opportunistic scheduler (OS) design using statistical channel state information~(CSI). We apply max-weight schedulers (MWSs) to maximize a utility function of users' average data rates. MWSs schedule the user with the highest weighted instantaneous data rate every time slot. Existing methods require hundreds of time slots to adjust the MWS's weights according to the instantaneous CSI before finding the optimal weights that maximize the utility function. In contrast, our MWS design requires few slots for estimating the statistical CSI. Specifically, we formulate a weight optimization problem using the mean and variance of users' signal-to-noise ratios (SNRs) to construct constraints bounding users' feasible average rates. Here, the utility function is the formulated objective, and the MWS's weights are optimization variables. We develop an iterative solver for the problem and prove that it finds the optimal weights. We also design an online architecture where the solver adaptively generates optimal weights for networks with varying mean and variance of the SNRs. Simulations show that our methods effectively require $4\sim10$ times fewer slots to find the optimal weights and achieve $5\sim15\%$ better average rates than the existing methods.

Opportunistic Scheduling Using Statistical Information of Wireless Channels

TL;DR

This paper forms a weight optimization problem using the mean and variance of users’ signal-to-noise ratios (SNRs) to construct constraints bounding users’ feasible average rates and develops an iterative solver for the problem and proves that it finds the optimal weights.

Abstract

This paper considers opportunistic scheduler (OS) design using statistical channel state information~(CSI). We apply max-weight schedulers (MWSs) to maximize a utility function of users' average data rates. MWSs schedule the user with the highest weighted instantaneous data rate every time slot. Existing methods require hundreds of time slots to adjust the MWS's weights according to the instantaneous CSI before finding the optimal weights that maximize the utility function. In contrast, our MWS design requires few slots for estimating the statistical CSI. Specifically, we formulate a weight optimization problem using the mean and variance of users' signal-to-noise ratios (SNRs) to construct constraints bounding users' feasible average rates. Here, the utility function is the formulated objective, and the MWS's weights are optimization variables. We develop an iterative solver for the problem and prove that it finds the optimal weights. We also design an online architecture where the solver adaptively generates optimal weights for networks with varying mean and variance of the SNRs. Simulations show that our methods effectively require times fewer slots to find the optimal weights and achieve better average rates than the existing methods.
Paper Structure (30 sections, 6 theorems, 69 equations, 10 figures, 1 table, 1 algorithm)

This paper contains 30 sections, 6 theorems, 69 equations, 10 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Notations
  3. System Model and Problem Formulation of Opportunistic Scheduling
  4. System Model
  5. MWS Design Problem Formulation
  6. Rate Estimation Using Full Knowledge of Statistical CSI
  7. Rate Estimation Using Mean and Variance of SNRs
  8. Bounding the Feasible Rate Region $\mathcal{F}$
  9. The Proposed Rate Estimation for MWSs
  10. Proposed MVWO for MWS Design
  11. Issue of Existing Iterative Solvers for the BLOP
  12. Proposed Iterative Solver for MVWO
  13. Initialization of the Proposed Iterative Process
  14. Low-Complexity Iterative Updates of Weights
  15. Termination of the Proposed Iterative Process
  16. ...and 15 more sections

Key Result

Corollary 1

For any MWS $\mu(\cdot|\mathbf{w})$ defined in eq:max_weight_scheduler, its achieved average rates in eq:defi:average_mws_rate satisfy

Figures (10)

  • Figure 1: (a) Illustration of a wireless scheduler, (b) illustration of the application scenarios.
  • Figure 2: The proposed online MVWO architecture.
  • Figure 3: The average rates achieved by $\mu(\cdot|\mathbf{w})$ and their estimated values in \ref{['eq:prob:estimation_mws_r_g']} for different $\mathbf{w}$.
  • Figure 4: Evaluation of the convergence of Algorithm \ref{['alg:DWO_algorithm']} when $K=5$ or $10$ and $\hat{\epsilon}=10^{-4}$.
  • Figure 5: The probability that the convergence of Algorithm \ref{['alg:DWO_algorithm']} occurs in given iterations.
  • ...and 5 more figures

Theorems & Definitions (15)

  • Corollary 1
  • proof
  • Corollary 2
  • proof
  • Corollary 3
  • proof
  • Lemma 1
  • proof
  • Theorem 1
  • proof
  • ...and 5 more