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9 - Coordinated multi-point transmission in 5G
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- By Roberto Fantini, Telecom Italia, Wolfgang Zirwas, Nokia, Lars Thiele, Ericsson, Danish Aziz, Alcatel-Lucent (now Nokia), Paolo Baracca, Alcatel-Lucent (now Nokia)
- Edited by Afif Osseiran, Jose F. Monserrat, Patrick Marsch
- Foreword by Mischa Dohler, King's College London, Takehiro Nakamura
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- Book:
- 5G Mobile and Wireless Communications Technology
- Published online:
- 05 June 2016
- Print publication:
- 02 June 2016, pp 248-276
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Summary
Introduction
The performance of a wireless network strongly depends on the user positions in a cell. More precisely, the UEs (User Equipments) at the cell border typically experience much lower throughput than those nearer to the transmitting Base Station (BS). This is mainly due to the presence of inter-cell interference, generated by concurrent transmissions in other cells. Inter-cell interference is particularly relevant for modern wireless communication systems like Universal Mobile Telecommunications System (UMTS) or Long-Term Evolution (LTE), and also 5G, where the frequency reuse factor is one or very close to one. In such scenario the system is primarily interference limited, and the performance cannot be improved by simply increasing the transmitted power. Hence, techniques are necessary in order to (1) target inter-cell interference and (2) reduce the gap between the cell edge and average throughput. Consequently, these alternative techniques allow a more even user experience throughout the whole network.
In principle, the following techniques can be pursued to tackle inter-cell interference:
• Interference can simply be treated as white noise. This is clearly suboptimal, as it ignores properties of the interfering signals that could be exploited in order to improve signal reception quality.
• Interference can be avoided through statically leaving some transmit resources in some cells muted (e.g. fractional frequency reuse), or otherwise constraining the usage of resources, or through coordinated scheduling among cells, as investigated in Chapter 11.
• The impact of interference can be alleviated at the receiver side through e.g. Interference Rejection Combining (IRC), where multiple receive antennas and subsequent receive filters are used to attenuate the interference to a certain extent.
• Interference may be decoded and cancelled, a technique that is for instance studied in 3GPP in the context of Network-Assisted Interference Cancelation (NAIC).
• At the transmitter side, interference can also be partially avoided by performing interference-aware precoding, i.e. applying precoding such that the interference caused toward adjacent cells is reduced.
• Ultimately, signals from other cells can in fact be treated as a useful signal energy instead of interference, if (in the downlink) multiple nodes jointly transmit signals that coherently overlap at the intended receiver, and destructively overlap at interfered receivers. In the uplink (UL), multiple nodes can jointly receive and decode the signals from multiple UEs, and in this form also exploit interference rather than seeing it as a burden.
8 - Massive multiple-input multiple-output (MIMO) systems
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- By Antti Tölli, University of Oulu, Lars Thiele, Fraunhofer Heinrich Hertz Institute, Satoshi Suyama, NTT DOCOMO, Gabor Fodor, Ericsson, Nandana Rajatheva, University of Oulu, Elisabeth De Carvalho, Aalborg University, Wolfgang Zirwas, Nokia, Jesper Hemming Sorensen, Aalborg University
- Edited by Afif Osseiran, Jose F. Monserrat, Patrick Marsch
- Foreword by Mischa Dohler, King's College London, Takehiro Nakamura
-
- Book:
- 5G Mobile and Wireless Communications Technology
- Published online:
- 05 June 2016
- Print publication:
- 02 June 2016, pp 208-247
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- Chapter
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Summary
Introduction
As stated in Chapter 2, one of the main 5G requirements [1] is to support 1000 times larger capacity per area compared with current Long Term Evolution (LTE) technology, but with a similar cost and energy dissipation per area as in today's cellular systems. In addition, an increase in capacity will be possible if all three factors that jointly contribute to system capacity are increased: More spectrum, a larger number of base stations per area, and an increased spectral efficiency per cell.
Massive or large Multiple-Input Multiple-Output (MIMO) systems are considered essential in contributing to the last stated factor, as they promise to provide a substantially increased spectral efficiency per cell. A massive MIMO system is typically defined as a system that utilizes a large number, i.e. 100 or more, of individually controllable antenna elements at least at one side of a wireless communications link, typically at the Base Station (BS) side [2][3]. An example of such usage of massive MIMO at the BS side is shown in Figure 8.1. A massive MIMO network exploits the many spatial Degrees of Freedom (DoF) provided by the many antennas to multiplex messages for several users on the same time-frequency resource (referred to as spatial multiplexing), and/or to focus the radiated signal toward the intended receivers and inherently minimize intra-cell and inter-cell interference [4]–[7]. Such focusing of radiated signals in a particular direction is possible by transmitting the same signal from multiple antenna points, but with a different phase shift applied to each of the antennas (and possibly a different phase shift for different parts of the system bandwidth), such that the signals overlap coherently at the intended target location. Note that in the remainder of the chapter, the term beamforming is used when applying the same phase shift at individual transmit antennas over the entire system bandwidth, while the term precoding is used when applying different phase shifts for different parts of the system bandwidth to tackle small-scale fading effects, for instance by applying phase shifts in frequency domain. With this definition, beamforming can be seen as a subclass of precoding algorithms. Regardless of whether precoding or beamforming is applied, the gain of obtaining a coherent overlap of signals at the receive point is commonly referred to as array gain.
9 - Channel Knowledge
- from Part III - Challenges Connected to CoMP
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- By Wolfgang Zirwas, Munich, Germany, Lars Thiele, Fraunhofer Institute for Telecommunications, Tobias Weber, University of Rostock, Nico Palleit, University of Rostock, Volker Jungnickel, Institute, Berlin
- Edited by Patrick Marsch, Gerhard P. Fettweis, Technische Universität, Dresden
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- Book:
- Coordinated Multi-Point in Mobile Communications
- Published online:
- 05 August 2012
- Print publication:
- 21 July 2011, pp 193-218
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Summary
In this chapter, we address the issue how channel knowledge - referring to both desired channels and the channels towards interferers - needed for various CoMP schemes can be made available where it is needed. We first investigate channel estimation techniques at the receiver side in Section 9.1, and then discuss how the obtained channel knowledge can be efficiently fed back to the transmitter side in Section 9.2, which is for example a crucial requirement for the downlink CoMP schemes investigated in Sections 6.3 and 6.4. The chapter shows that standard channel estimation and feedback concepts can principally be extended to enable CoMP in general. However, it also becomes apparent that large CoMP cooperation sizes may be considered questionable in practice, due to the fact that weak links cannot be estimated accurately, and the involved pilot and channel state information (CSI) feedback overhead may become prohibitive.
Channel Estimation for CoMP
One of the main challenges for CoMP schemes like joint transmission (JT) is to obtain accurate channel information in a multi-cell mobile radio environment with acceptable overhead for pilot signals.
The section is structured as follows. In Subsection 9.1.1, main characteristics of the mobile radio channel and state-of-the-art estimation and interpolation techniques like Wiener filtering will be introduced, with a special focus on channel prediction. For CoMP, the analysis then has to be extended to multiple channel components and multi-cell scenarios, which will be done in Subsections 9.1.2 and 9.1.3, respectively.