Large MIMO systems

Chockalingam, A.; Rajan, B. Sundar

doi:10.1017/cbo9781139208437.003

Cited by 88 publications

(154 citation statements)

References 14 publications

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“…As a result, the number of computation for massive MIMO is reasonable even if the number of antennas is quite huge. Actually, in this case, very good performances are reported [28], and it is especially good when the operated spectrum is in the range of 24.25 GHz or more because quite a large number of antenna element can be included in a specific area of antenna module [29]- [31]. When operated spectrum is selected from below 6 GHz band, on the other hand, because antenna distance in a MIMO antenna module is not short, the number of included antenna elements tends to be below 100, in which case it is quite hard to approximate Gram matrix for the channel as a diagonal matrix [32].…”

Section: Massive Mimo and Its Applications To Beamforming Antenna To mentioning

confidence: 69%

“…For above 6 GHz band, WRC-15 decided to invite studies of sharing and compatibility between 5G and existing services in the band 24.25-86 GHz [13]. In massive MIMO [27] cases, when the number of antenna elements is quite high, 100 or more, due to channel hardening effect [28], Gram matrix for the channel can be approximated by a diagonal matrix, thereby suppressing computation complexity to calculate inverse-matrix of Gram matrix used in the MIMO signal separation. As a result, the number of computation for massive MIMO is reasonable even if the number of antennas is quite huge.…”

Section: Massive Mimo and Its Applications To Beamforming Antenna To mentioning

confidence: 99%

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Development of Wireless Access and Flexible Networking Technologies for 5G Cellular Systems

Sampei

2017

IEICE Trans. Commun.

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Section: Massive Mimo and Its Applications To Beamforming Antenna To mentioning

confidence: 69%

Section: Massive Mimo and Its Applications To Beamforming Antenna To mentioning

confidence: 99%

Development of Wireless Access and Flexible Networking Technologies for 5G Cellular Systems

Sampei

2017

IEICE Trans. Commun.

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“…Table 3 compares Spatial Multiplexing, Space-Time Coding, and Spatial Modulation. MIMO encoding using known CSI at the transmitter is known as precoding [5]. Table 3.…”

Section: Encoding Techniquesmentioning

confidence: 99%

“…Since the elements of the transmitted vector belong to a predefined discrete alphabet, detection is harder when the alphabet is bigger. Some detection algorithms produce soft values of the estimate of the transmitted symbols while the others produce hard values [5]. The estimated soft values are fed into the channel decoders in coded systems.…”

Section: Detection Of Encoded Signals (An Open Area Of Research)mentioning

confidence: 99%

“…Channel estimation quality per antenna also improves with the number of BS antennas especially in the presence of high correlation among the antennas which is very typical [4]. In addition, the eigenvalue histogram of a single implementation converges to the average asymptotic eigenvalue distribution [5]. This leads to the possibility of employing simple low complexity detection techniques while preserving an excellent performance.…”

Section: Introductionmentioning

confidence: 98%

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Massive MIMO Wireless Networks: An Overview

Hassan

Fernando

2017

Electronics

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Massive multiple-input-multiple-output (MIMO) systems use few hundred antennas to simultaneously serve large number of wireless broadband terminals. It has been incorporated into standards like long term evolution (LTE) and IEEE802.11 (Wi-Fi). Basically, the more the antennas, the better shall be the performance. Massive MIMO systems envision accurate beamforming and decoding with simpler and possibly linear algorithms. However, efficient signal processing techniques have to be used at both ends to overcome the signaling overhead complexity. There are few fundamental issues about massive MIMO networks that need to be better understood before their successful deployment. In this paper, we present a detailed review of massive MIMO homogeneous, and heterogeneous systems, highlighting key system components, pros, cons, and research directions. In addition, we emphasize the advantage of employing millimeter wave (mmWave) frequency in the beamforming, and precoding operations in single, and multi-tier massive MIMO systems.

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Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

Datta

Mandloi

Bhatia

2019

Int J Communication

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Summary Massive multiple‐input multiple‐output (MIMO) plays a crucial role in realizing the demand for higher data rates and improved quality of service for 5G and beyond communication systems. Reliable detection of transmitted information bits from all the users is one of the challenging tasks for practical implementation of massive‐MIMO systems. The conventional linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) achieve near‐optimal bit error rate (BER) performance. However, ZF and MMSE require large dimensional matrix inversion which induces high computational complexity for symbol detection in such systems. This motivates for devising alternate low‐complexity near‐optimal detection algorithms for uplink massive‐MIMO systems. In this work, we propose an ordered sequential detection algorithm that exploits the concept of reliability feedback for achieving near‐optimal performance in uplink massive‐MIMO systems. In the proposed algorithm, symbol corresponding to each user is detected in an ordered sequence by canceling the interference from all the other users, followed by reliability feedback‐based decision. Incorporation of the sequence ordering and the reliability feedback‐based decision enhances the interference cancellation, which reduces the error propagation in sequential detection, and thus, improves the BER performance. Simulation results show that the proposed algorithm significantly outperforms recently reported massive‐MIMO detection techniques in terms of BER performance. In addition, the computational complexity of the proposed algorithm is substantially lower than that of the existing algorithms for the same BER. This indicates that the proposed algorithm exhibits a desirable trade‐off between the complexity and the performance for massive‐MIMO systems.

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Large MIMO systems

Cited by 88 publications

References 14 publications

Development of Wireless Access and Flexible Networking Technologies for 5G Cellular Systems

Development of Wireless Access and Flexible Networking Technologies for 5G Cellular Systems

Massive MIMO Wireless Networks: An Overview

Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

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