Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Handoffs in User-Centric Cell-Free MIMO Networks: A POMDP Framework

Hussein A. Ammar, Raviraj Adve, Shahram Shahbazpanahi, Gary Boudreau, Kothapalli Venkata Srinivas

TL;DR

A partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading and the action space representing the association decisions of the user with the access points (APs) is formulated and a novel algorithm is developed to derive a HO policy for a mobile user based on current and future rewards.

Abstract

We study the problem of managing handoffs (HOs) in user-centric cell-free massive MIMO (UC-mMIMO) networks. Motivated by the importance of controlling the number of HOs and by the correlation between efficient HO decisions and the temporal evolution of the channel conditions, we formulate a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading and the action space representing the association decisions of the user with the access points (APs). We develop a novel algorithm that employs this model to derive a HO policy for a mobile user based on current and future rewards. To alleviate the high complexity of our POMDP, we follow a divide-and-conquer approach by breaking down the POMDP formulation into sub-problems, each solved separately. Then, the policy and the candidate pool of APs for the sub-problem that produced the best total expected reward are used to perform HOs within a specific time horizon. We then introduce modifications to our algorithm to decrease the number of HOs. The results show that half of the number of HOs in the UC-mMIMO networks can be eliminated. Namely, our novel solution can control the number of HOs while maintaining a rate guarantee, where a 47%-70% reduction of the cumulative number of HOs is observed in networks with a density of 125 APs per km2. Most importantly, our results show that a POMDP-based HO scheme is promising to control HOs.

Handoffs in User-Centric Cell-Free MIMO Networks: A POMDP Framework

TL;DR

A partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading and the action space representing the association decisions of the user with the access points (APs) is formulated and a novel algorithm is developed to derive a HO policy for a mobile user based on current and future rewards.

Abstract

We study the problem of managing handoffs (HOs) in user-centric cell-free massive MIMO (UC-mMIMO) networks. Motivated by the importance of controlling the number of HOs and by the correlation between efficient HO decisions and the temporal evolution of the channel conditions, we formulate a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading and the action space representing the association decisions of the user with the access points (APs). We develop a novel algorithm that employs this model to derive a HO policy for a mobile user based on current and future rewards. To alleviate the high complexity of our POMDP, we follow a divide-and-conquer approach by breaking down the POMDP formulation into sub-problems, each solved separately. Then, the policy and the candidate pool of APs for the sub-problem that produced the best total expected reward are used to perform HOs within a specific time horizon. We then introduce modifications to our algorithm to decrease the number of HOs. The results show that half of the number of HOs in the UC-mMIMO networks can be eliminated. Namely, our novel solution can control the number of HOs while maintaining a rate guarantee, where a 47%-70% reduction of the cumulative number of HOs is observed in networks with a density of 125 APs per km2. Most importantly, our results show that a POMDP-based HO scheme is promising to control HOs.
Paper Structure (30 sections, 60 equations, 6 figures, 2 tables, 3 algorithms)

This paper contains 30 sections, 60 equations, 6 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Aging Model
  4. Channel Estimation
  5. Downlink Signal Model and Lower-Bound for Achievable Rate
  6. Problem Formulation using POMDP
  7. Components of the POMDP
  8. State space $\mathcal{S}(|\mathcal{B}|)$
  9. Action space $\mathcal{A}(|\mathcal{B}|)$
  10. Observation Space $\Omega(|\mathcal{B}|)$
  11. Transition probability
  12. Observation distribution
  13. Belief
  14. Reward
  15. Time horizon $T_{\rm H}$
  16. ...and 15 more sections

Figures (6)

  • Figure 1: User-centric cell-free MIMO network depicting a moving user.
  • Figure 2: HO procedure as a POMDP model.
  • Figure 3: Transition probability diagram for the channel state at the beginning of decision cycle $t$.
  • Figure 4: Complexity of our solution compared to the conventional approach.
  • Figure 5: Performance using Algorithm \ref{['algorithm:Apply_Policy']} with different time horizon $T_{\rm H}$.
  • ...and 1 more figures

Theorems & Definitions (2)

  • proof
  • Remark 1