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Optimizing Information Freshness in Uplink Multiuser MIMO Networks with Partial Observations

Jingwei Liu, Qian Wang, He Chen

TL;DR

The paper addresses AoI optimization in an uplink multiuser MIMO network with stochastic arrivals and partial observations. It models scheduling as a POMDP and proves that device belief updates are independent and that feasible beliefs can be represented by a finite three-dimensional vector, enabling tractable policy design. A Dynamic Scheduling (DS) policy based on Lyapunov drift is developed, with a computable upper bound on performance, and a convex optimization guides parameter configuration; in symmetric networks a closed-form bound leads to a low-complexity Fixed Scheduling (FS) policy. An action-space reduction further lowers computational burden, and numerical results show that DS/FS with reduction perform nearly as well as their full-action counterparts and outperform baseline policies, highlighting practical impact for information freshness in MIMO uplinks.

Abstract

This paper investigates a multiuser scheduling problem within an uplink multiple-input multi-output (MIMO) status update network, consisting of a multi-antenna base station (BS) and multiple single-antenna devices. The presence of multiple antennas at the BS introduces spatial degrees-of-freedom, enabling concurrent transmission of status updates from multiple devices in each time slot. Our objective is to optimize network-wide information freshness, quantified by the age of information (AoI) metric, by determining how the BS can best schedule device transmissions, while taking into account the random arrival of status updates at the device side.To address this decision-making problem, we model it as a partially observable Markov decision process (POMDP) and establish that the evolution of belief states for different devices is independent.We also prove that feasible belief states can be described by finite-dimensional vectors. Building on these observations, we develop a dynamic scheduling (DS) policy to solve the POMDP, and then derive an upper bound of its AoI performance, which is used to optimize the parameter configuration. To gain more design insights, we investigate a symmetric network, and put forth a fixed scheduling (FS) policy with lower computational complexity. An action space reduction strategy is applied to further reduce the computational complexity of both DS and FS policies. Our numerical results validate our analyses and indicate that the DS policy with the reduced action space performs almost identically to the original DS policy, and both outperform the baseline policies.

Optimizing Information Freshness in Uplink Multiuser MIMO Networks with Partial Observations

TL;DR

The paper addresses AoI optimization in an uplink multiuser MIMO network with stochastic arrivals and partial observations. It models scheduling as a POMDP and proves that device belief updates are independent and that feasible beliefs can be represented by a finite three-dimensional vector, enabling tractable policy design. A Dynamic Scheduling (DS) policy based on Lyapunov drift is developed, with a computable upper bound on performance, and a convex optimization guides parameter configuration; in symmetric networks a closed-form bound leads to a low-complexity Fixed Scheduling (FS) policy. An action-space reduction further lowers computational burden, and numerical results show that DS/FS with reduction perform nearly as well as their full-action counterparts and outperform baseline policies, highlighting practical impact for information freshness in MIMO uplinks.

Abstract

This paper investigates a multiuser scheduling problem within an uplink multiple-input multi-output (MIMO) status update network, consisting of a multi-antenna base station (BS) and multiple single-antenna devices. The presence of multiple antennas at the BS introduces spatial degrees-of-freedom, enabling concurrent transmission of status updates from multiple devices in each time slot. Our objective is to optimize network-wide information freshness, quantified by the age of information (AoI) metric, by determining how the BS can best schedule device transmissions, while taking into account the random arrival of status updates at the device side.To address this decision-making problem, we model it as a partially observable Markov decision process (POMDP) and establish that the evolution of belief states for different devices is independent.We also prove that feasible belief states can be described by finite-dimensional vectors. Building on these observations, we develop a dynamic scheduling (DS) policy to solve the POMDP, and then derive an upper bound of its AoI performance, which is used to optimize the parameter configuration. To gain more design insights, we investigate a symmetric network, and put forth a fixed scheduling (FS) policy with lower computational complexity. An action space reduction strategy is applied to further reduce the computational complexity of both DS and FS policies. Our numerical results validate our analyses and indicate that the DS policy with the reduced action space performs almost identically to the original DS policy, and both outperform the baseline policies.
Paper Structure (29 sections, 9 theorems, 73 equations, 7 figures, 2 tables)

This paper contains 29 sections, 9 theorems, 73 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Network Model
  4. Information Freshness Metric and Problem Formulation
  5. POMDP Formulation and Analysis
  6. POMDP Components
  7. Independence of the Belief Updates of All Devices
  8. Belief State Simplification
  9. Dynamic Scheduling Policy
  10. The Implementation of the DS policy
  11. Upper Bound
  12. Action Space Reduction
  13. Policies for Symmetric Networks
  14. The Upper Bound in the Symmetric Network
  15. Fixed Scheduling Policy
  16. ...and 14 more sections

Key Result

Proposition 1

Given an initial $\bm{b}_1$ such that $b_1(\bm{d}_1)=\prod^N_{i=1}b_{1,i}(d_{1,i})$, then we have In addition, bu1 can be rewritten as

Figures (7)

  • Figure 1: EWSAoI performance versus packet arrival rate $\lambda$ with $N=10$, $M=5$, $\omega_i=1$, $\forall i$.
  • Figure 2: EWSAoI performance versus the packet arrival rate $\lambda$ in two setups, where $N=12$, $M=4$, $\omega_i=1,\forall i$, and $\text{SNR}=20$dB.
  • Figure 3: EWSAoI performance versus the number of antennas $M$, where $N=30$, $\lambda_i=0.7$, $\omega_i=1,\forall i$, and $\text{SNR}=20$dB.
  • Figure 4: EWSAoI performance versus the number of devices $N$, where $M=6$, $\lambda_i=0.7$, $\omega_i=1,\forall i$, and $\text{SNR}=20$dB.
  • Figure 5: EWSAoI performance versus packet arrival rate in two setups, where $N=10$, $M=5$, $\omega_i=1,\forall i$, and SNR=$15$dB
  • ...and 2 more figures

Theorems & Definitions (23)

  • Proposition 1
  • proof
  • Remark 1
  • Definition 1
  • Proposition 2
  • proof
  • Corollary 1
  • proof
  • Remark 2
  • Theorem 1
  • ...and 13 more