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15 - Massive MIMO Scheduling Protocols

from Part III - Network Protocols, Algorithms, and Design

Published online by Cambridge University Press:  28 April 2017

By
Giuseppe Caire
Edited by
Vincent W. S. Wong ,
Robert Schober ,
Derrick Wing Kwan Ng  and
Li-Chun Wang
Giuseppe Caire
Affiliation:
Technical University of Berlin, Germany
Vincent W. S. Wong
Affiliation:
University of British Columbia, Vancouver
Robert Schober
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Derrick Wing Kwan Ng
Affiliation:
University of New South Wales, Sydney
Li-Chun Wang
Affiliation:
National Chiao Tung University, Taiwan
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Summary

In this chapter, we present a general approach to data-oriented downlink scheduling in a wireless network, possibly formed from multiple base stations and users. Following commonly used acronyms, base stations will be denoted by “BS” (base station) and user devices by “UE” (user equipment). Specifically, we consider the case where the BSs have a large number of antennas and serve a given number of downlink data streams using multiuser multiple-input multiple-output (MIMO) spatial multiplexing. When the number of antennas is large and is significantly larger than the number of downlink data streams, such systems are referred to as “massive MIMO.” As we shall see, a network operating in the massive MIMO regime has several advantages, not only in terms of achievable spectral efficiency per cell but also in terms of simplified signal processing, rate allocation, and user scheduling. This nontrivial system simplification is due to the fact that the large number of antennas and not-so-large number of simultaneously transmitted data streams has the consequence that the signal-to-interference-plus-noise ratio (SINR) at each UE becomes an almost deterministic quantity that depends only on the distance-dependent path loss and large-scale fading (shadowing) of the propagation channel between the UE and the serving BS, and not on the small-scale multipath fading. Since distance-dependent path loss and shadowing are relatively slowly varying in time and frequency nonselective, in contrast to the time- and frequency-selective small-scale fading, it follows that the scheduling protocol can learn quite accurately the rate at which each user can be served from each BS. Based on this knowledge, a scheduling protocol can decide dynamically which subset of users should be served from which BS. In this chapter, we will see how such a dynamic scheduling policy with given optimality performance guarantees can be systematically designed.

Introduction

Wireless data traffic has grown dramatically in recent years. Unlike traditional voice-oriented interactive communications, wireless data is typically asymmetric (the downlink traffic is much higher than the uplink traffic) and more delay tolerant. For example, a typical killer application is represented by on-demand video streaming, which is predicted to account for 75% of the total mobile data traffic by 2019 [1]. The streaming process requires that video frames arrive at the receiver within their playback deadlines.

Type
Chapter
Information
Key Technologies for 5G Wireless Systems , pp. 335 - 357
Publisher: Cambridge University Press
Print publication year: 2017

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