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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
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- Book:
- 5G Mobile and Wireless Communications Technology
- Published online:
- 05 June 2016
- Print publication:
- 02 June 2016, pp 208-247
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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.
Decision-directed phase noise compensation for millimeter-wave single carrier systems with iterative frequency-domain equalization
- Satoshi Suyama, Junichi Onodera, Hiroshi Suzuki, Kazuhiko Fukawa
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- Journal:
- International Journal of Microwave and Wireless Technologies / Volume 2 / Issue 3-4 / August 2010
- Published online by Cambridge University Press:
- 08 July 2010, pp. 399-408
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This paper proposes a receiver that repeats iterative frequency-domain equalization (FDE) and decision-directed phase noise compensation (DD-PNC) to alleviate degradation due to the phase noise for millimeter-wave single carrier (SC) systems. High bit-rate SC-FDE transceivers based on the single-chip Si RF-CMOS IC technology in the 60-GHz millimeter-wave band have been extensively studied for wireless personal area network (WPAN) systems, and the relatively large phase noise in a phase-locked loop (PLL) synthesizer severely degrades transmission performance. In an initial processing of the proposed receiver, a cyclic prefix (CP)-based phase noise compensator (CP-PNC) removes the phase noise from a time-domain received signal by using CP, which is known to the receiver, and the channel is equalized by the iterative FDE using the conventional minimum mean-square-error (MMSE) weight. In an iterative processing, DD-PNC estimates the phase noise each symbol by exploiting an output of a channel decoder, and then compensates the time-domain received signal for the phase noise by using the estimate. In order to equalize the compensated received signal, the iterative FDE performs both the MMSE filtering and residual inter-symbol interference cancelation using the decoder output. Computer simulations following the 60-GHz WPAN standard demonstrate that in the 64QAM with the coding rate of 3/4, the proposed receiver with three iterations can drastically remove the phase noise of −85 dBc/Hz at 1 MHz offset, and that it can achieve excellent transmission performance.