Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11598
Title: Massive MIMO
Authors: Sun, H.
Ng, C.
Huo, Y.
Hu, R.Q.
Wang, N.
Chen, C.-M.
Demir, O.T.G.
Keywords: 5G
average signal-to-noise ratio per bit
beam optimization
beamforming
CFO estimation
channel estimation
energy efficiency
HetNet
hybrid architecture
Massive MIMO
mmWave
5G mobile communication systems
Beam forming networks
Beamforming
Bit error rate
Channel estimation
Energy efficiency
Frequency allocation
Heterogeneous networks
Maximum likelihood estimation
Millimeter waves
MIMO systems
Network layers
Spectrum efficiency
5g
Antenna element
Average signal-to-noise ratio per bit
Beam optimization
Carrier frequency offset estimation
Enabling technologies
Hybrid architectures
Massive MIMO
Mm waves
Performance
Signal to noise ratio
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: The use of a large number of antenna elements, known as Massive MIMO, is seen as a key enabling technology in the 5G and Beyond wireless ecosystem. The intelligent use of a multitude of antenna elements unleashes unprecedented flexibility and control on the physical channel of the wireless medium. Through Massive MIMO and other techniques, it is envisioned that the 5G and beyond wireless system will be able to support high throughput, high reliability (low bit-error-rate (BER)), high energy efficiency, low latency, and an internet-scale number of connected devices. Massive MIMO and related technologies will be deployed in the mid-band (sub 6 GHz) for coverage, all the way to mmWave bands to support large channel bandwidths. It is envisioned that Massive MIMO will be deployed in different environments: Frequency Division Duplex (FDD), (Time Division Duplex (TDD), indoor / outdoor, small cell, macro cell, and other heterogeneous networks (HetNet) configurations. Accurate and useful channel estimation remains a challenge in the efficient adoption of Massive MIMO techniques, and different performance-complexity tradeoffs may be supported by different Massive MIMO architectures such as digital, analog, and/or digital/analog hybrid. Carrier frequency offset (CFO), which arises due to the relative motion between the transmitter and receiver, is another important topic. Recently, maximum likelihood (ML) methods of CFO estimation have been proposed, that achieve very low root mean square (RMS) estimation errors, with a large scope for parallel processing and well suited for application with turbo codes. Massive MIMO opens up a whole new dimension of parameters where the wireless applications or other network layers may control or influence the operation and performance of the physical wireless channel. To fully reap the benefits of such flexibility, the latest advances in artificial intelligence (AI) and machine learning (ML) techniques will be leveraged to monitor and optimize the Massive MIMO sub-system. As such, a cross-layer open interface can facilitate exposing the programmability of Massive MIMO through techniques such as network slicing (NS) and network function virtualization (NFV). Finally, security needs to be integrated into the design of the system so the new functionality and performance of Massive MIMO can be utilized in a reliable manner. © 2023 IEEE.
Description: et al.;IEEE Antennas and Propagation Society (APS);IEEE Circuits and Systems Society (CAS);IEEE Communications Society (ComSoc);IEEE Electronics Packaging Society (EPS);IEEE Intelligent Transportation Systems Society (ITSS)
6th IEEE Future Networks World Forum, FNWF 2023 -- 13 November 2023 through 15 November 2023 -- 199497
URI: https://doi.org/10.1109/FNWF58287.2023.10520592
https://hdl.handle.net/20.500.11851/11598
ISBN: 9798350324587
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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