Linear transmit processing in MIMO communications systems

Joham, Michael; Utschick, Wolfgang; Nossek, Josef A.

doi:10.1109/tsp.2005.850331

Cited by 731 publications

(598 citation statements)

References 51 publications

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“…The optimal precoder is based on dirty paper coding (DPC), but due to its high complexity it is not well suited for practical implementations [10]. There are many types of precoders, both linear precoders [11] as well as non-linear precoders. Linear precoders have been shown to be optimal under certain conditions.…”

Section: Downlink Precodingmentioning

confidence: 99%

A scalable architecture for massive MIMO base stations using distributed processing

Bertilsson

Gustafsson

Larsson

2016

2016 50th Asilomar Conference on Signals, Systems and Computers

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Massive MIMO is an emerging technology for future wireless systems that has received much attention from both academia and industry recently. The most prominent feature of Massive MIMO is that the base station is equiped with a large number of antennas. It is therefore important to create scalable architectures to enable simple deployment in different configurations.In this thesis, a distributed architecture for performing the baseband processing in a massive OFDM MU-MIMO system is proposed and analyzed. The proposed architecture is based on connecting several identical nodes in a K-ary tree. It is shown that, depending on the chosen algorithms, all or most computations can be performed in a distrbuted manner. Also, the computational load of each node does not depend on the number of nodes in the tree (except for some timing issues) which implies simple scalability of the system.It is shown that it should be enough that each node contains one or two complex multipliers and a few complex adders running at a couple of hundres MHz to support specifications similar to LTE. Additionally the nodes must communicate with each other over links with data rates in the order of some Gbps.Finally, a VHDL implementation of the system is proposed. The implementation is parameterized such that a system can be generated from a given specification. Nyckelord KeywordsElektroteknik, ASIC, Massiv MIMO AbstractMassive MIMO is an emerging technology for future wireless systems that has received much attention from both academia and industry recently. The most prominent feature of Massive MIMO is that the base station is equiped with a large number of antennas. It is therefore important to create scalable architectures to enable simple deployment in different configurations.In this thesis, a distributed architecture for performing the baseband processing in a massive OFDM MU-MIMO system is proposed and analyzed. The proposed architecture is based on connecting several identical nodes in a K-ary tree. It is shown that, depending on the chosen algorithms, all or most computations can be performed in a distrbuted manner. Also, the computational load of each node does not depend on the number of nodes in the tree (except for some timing issues) which implies simple scalability of the system.It is shown that it should be enough that each node contains one or two complex multipliers and a few complex adders running at a couple of hundres MHz to support specifications similar to LTE. Additionally the nodes must communicate with each other over links with data rates in the order of some Gbps.Finally, a VHDL implementation of the system is proposed. The implementation is parameterized such that a system can be generated from a given specification.iii

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Section: Downlink Precodingmentioning

confidence: 99%

A scalable architecture for massive MIMO base stations using distributed processing

Bertilsson

Gustafsson

Larsson

2016

2016 50th Asilomar Conference on Signals, Systems and Computers

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“…Moreover, because the number of UTs K is significantly less than the number of antennas at the BS M (Figure 1), the overhead during period B can be decreased using the frame format shown in Figure 1. Originally, an adaptive array utilizing the channel reciprocity was proposed in order to avoid interference through use of the uplink channel [29,30]. Channel reciprocity means that the uplink and downlink share the same frequency band in TDD systems and that the receive weight created by the uplink channel can be utilized for the transmit weight [30].…”

Section: Ibfmentioning

confidence: 99%

“…Originally, an adaptive array utilizing the channel reciprocity was proposed in order to avoid interference through use of the uplink channel [29,30]. Channel reciprocity means that the uplink and downlink share the same frequency band in TDD systems and that the receive weight created by the uplink channel can be utilized for the transmit weight [30]. In order to realize IBF, a calibration technique for the transmitters and receivers is essential [22][23][24].…”

Section: Ibfmentioning

confidence: 99%

Effectiveness of Implicit Beamforming with Large Number of Antennas Using Calibration Technique in Multi-User MIMO System

et al. 2017

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This paper examines the effectiveness of implicit beamforming (IBF), which enables transmission without channel state information (CSI) feedback in multi-user multiple-input multiple-output (MU-MIMO) systems with a large number of antennas. First, we explain why CSI feedback from terminal stations to the base station produces a very large overhead. A calibration technique is then introduced, which compensates for the difference between the complex amplitudes of the transmitters and receivers to facilitate CSI-feedback-free beamforming; this technique is called IBF. The efficacy of this calibration technique is demonstrated by measuring the amplitude and phase errors obtained using a 16-element array testbed and by performing a channel capacity evaluation. Finally, the throughput under IEEE802.11ac-based massive MIMO transmission, both with and without CSI feedback, is obtained in terms of the medium access control efficiency.

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“…The common approach to handle spatio-temporal interference in MIMO systems, involves either linear or non-linear transmit and receive signal processing, which job is to transform the original MIMO system into a »virtual« MIMO system, where large amounts of spatio-temporal interference have been removed [48]. All state of the art MIMO signal processing techniques have in common that they assume that either the receiver, the transmitter, or both, have access to, or can generate signals with arbitrary precision.…”

Section: Communication Theoretical Limits Coding and Signal Processingmentioning

confidence: 99%

Chip-to-Chip and On-Chip Communications

Nossek¹,

Russer²,

Noll³

et al. 2013

Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

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Linear transmit processing in MIMO communications systems

Cited by 731 publications

References 51 publications

A scalable architecture for massive MIMO base stations using distributed processing

A scalable architecture for massive MIMO base stations using distributed processing

Effectiveness of Implicit Beamforming with Large Number of Antennas Using Calibration Technique in Multi-User MIMO System

Chip-to-Chip and On-Chip Communications

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