Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

An Optimization-Based User Scheduling Framework for Multiuser MIMO Systems

Victoria Palhares, Christoph Studer

TL;DR

The efficacy of this UE scheduling framework for millimeter-wave massive MU-MIMO and sub-6-GHz cell-free massive MU-MIMO systems is demonstrated, and it is shown that it outperforms existing scheduling algorithms while approaching the performance of an exhaustive search.

Abstract

Resource allocation is a key factor in multiuser (MU) multiple-input multiple-output (MIMO) wireless systems to provide high quality of service to all user equipments (UEs). In congested scenarios, UE scheduling enables UEs to be distributed over time, frequency, or space in order to mitigate inter-UE interference. Many existing UE scheduling methods rely on greedy algorithms, which fail at treating the resource-allocation problem globally. In this work, we propose a UE scheduling framework for MU-MIMO wireless systems that approximately solves a nonconvex optimization problem that treats scheduling globally. Our UE scheduling framework determines subsets of UEs that should transmit simultaneously in a given resource slot and is flexible in the sense that it (i) supports a variety of objective functions (e.g., post-equalization mean squared error, capacity, and achievable sum rate) and (ii) enables precise control over the minimum and maximum number of resources the UEs should occupy. We demonstrate the efficacy of our UE scheduling framework for millimeter-wave massive MU-MIMO and sub-6-GHz cell-free massive MU-MIMO systems, and we show that it outperforms existing scheduling algorithms while approaching the performance of an exhaustive search.

An Optimization-Based User Scheduling Framework for Multiuser MIMO Systems

TL;DR

The efficacy of this UE scheduling framework for millimeter-wave massive MU-MIMO and sub-6-GHz cell-free massive MU-MIMO systems is demonstrated, and it is shown that it outperforms existing scheduling algorithms while approaching the performance of an exhaustive search.

Abstract

Resource allocation is a key factor in multiuser (MU) multiple-input multiple-output (MIMO) wireless systems to provide high quality of service to all user equipments (UEs). In congested scenarios, UE scheduling enables UEs to be distributed over time, frequency, or space in order to mitigate inter-UE interference. Many existing UE scheduling methods rely on greedy algorithms, which fail at treating the resource-allocation problem globally. In this work, we propose a UE scheduling framework for MU-MIMO wireless systems that approximately solves a nonconvex optimization problem that treats scheduling globally. Our UE scheduling framework determines subsets of UEs that should transmit simultaneously in a given resource slot and is flexible in the sense that it (i) supports a variety of objective functions (e.g., post-equalization mean squared error, capacity, and achievable sum rate) and (ii) enables precise control over the minimum and maximum number of resources the UEs should occupy. We demonstrate the efficacy of our UE scheduling framework for millimeter-wave massive MU-MIMO and sub-6-GHz cell-free massive MU-MIMO systems, and we show that it outperforms existing scheduling algorithms while approaching the performance of an exhaustive search.
Paper Structure (55 sections, 39 equations, 2 figures, 4 tables)

This paper contains 55 sections, 39 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Contributions
  3. Notation
  4. Paper Outline
  5. Relevant Prior Art
  6. Greedy Scheduling Algorithms
  7. UE-to-Beam Association in mmWave
  8. UE-Centric Cell-Free Networks
  9. Other Scheduling Algorithms
  10. Resource Allocation
  11. Cost Functions
  12. All-Digital Basestation Architectures
  13. Prerequisites
  14. System Model
  15. UE Scheduling
  16. ...and 40 more sections

Figures (2)

  • Figure 1: BER (a), HMI (b), MSE (c), and per-UE achievable rate (d) performance for a mmWave massive MU-MIMO system in Scenario S1: $B=16$ receive antennas, $L = 1$ AP, $n_{\text{AP}} = 16$ antennas per AP, $U=16$ UEs, $T=2$ time slots, $U_{\text{min}} = 8$ UEs, $U_{\text{max}} = 10$ UEs, $T_{\text{min}} = 1$ time slot, and $T_{\text{max}} = 2$ time slots.
  • Figure 2: BER (a), HMI (b), MSE (c), and per-UE achievable rate (d) performance for a cell-free MU-MIMO system in Scenario S4: $B=16$ receive antennas, $L = 8$ APs, $n_{\text{AP}} = 2$ antennas per AP, $U=16$ UEs, $T=2$ time slots, $U_{\text{min}} = 8$ UEs, $U_{\text{max}} = 10$ UEs, $T_{\text{min}} = 1$ time slot, and $T_{\text{max}} = 2$ time slots.