Massive MIMO with Blind Channel Estimation Algorithm.
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Saudi Digital Library
Abstract
One of the core technology of fifth and sixth generations(5G and 6G) cellular distributive
networks is "Massive MIMO," which stand for Massive multiple-input-multiple-output. There
are two types of MIMO designs, single-user MIMO (SU-MIMO) and multi-user MIMO (MU-
MIMO), where a base station (BS) is equipped with many antennas those antennas serve
many users (single-antenna or multiple-antennas) in the same time-frequency resource.
Massive MIMO is one form of multiuser MIMO. Communication channels have many
impairments such as interference, noise and fading. Massive MIMO can overcome most of
those impairments. It requires that the receiver (BS) has a good channel knowledge on the
uplink and the downlink because Massive MIMO operates in TDD mode, which depends on
reciprocity between the downlink and uplink channels to estimate the CSI for both the uplink
and downlink channels. Massive MIMO has instructed a considerable number of antennas
with a large degree of freedom to provide a very high data rate. The most exciting thing is that
it can provide high communication reliability with simple linear processing such as
maximum-ratio combining (MRC) or zero-forcing (ZF) and minimum mean square error
(MMSE). The main parameter used by the users to decode the received data is knowledge of
their effective channel gain. Traditionally, by virtue of channel hardening, it is assumed that
the instantaneous gain is close to its average. Hence, users can depend on knowledge of that
average (also known as statistical channel information). However, channel hardening does not
exist in some propagation channels, such as keyhole channels. In this report, a performance of
a blind algorithm to estimate the effective channel gain at each user that does not require any
downlink pilots proposed has been discussed.
Description
There has been a significant growth of the wireless communication industry in the past decades,
which has become an essential part of our lives [1]. If we look in our homes, offices, and
schools, we notice that the wireless product penetrates every field. The essential requirements of
any communication system are to improve the link reliability and increase the overall system
capacity. The multiple antennas employed at both the transmitter and receiver enables the so-
called Multiple-Input Multiple-Output (MIMO) technologies will achieve the very high
performance (reliability and capacity) of the system .there are a various cellular wireless
standard which MIMO has been used with it, such as the third-generation (3G) and fourth-
generation (4G) wireless systems.
Conventionally MIMO systems are employed with 2-8 antennas .massive MIMO systems will
design with an extensive array of antennas to exploit the potentially significant capacity gains.
The communication systems have different methods to deal with a multipath signal. One of
them is to use multiple antennas to capture the strongest signal at each moment. Another
method adds delays to back align the signals. A common strategy to deal with multipath signals
is to ignore weaker signals by which the energy they contain is wasted. The multipath signals
are very destructive and harmful, and the system must have the ability to treat its effects [2].
Massive MIMO becomes very important in wireless communication systems by increasing the
capacity and canceling the interference. Conventionally, MIMO (the old version) has the
advantage of utilizing the multipath propagation signals. Instead of applying several techniques
to handle the multipath propagation signals, MIMO takes advantage of the multipath signal by
sending and receiving more than one data signal in the same system resources (time and
frequency). This is done by applying multiple antennas in the transmitter and receiver.
Currently, the terminals need broadband in networks, and the system must be energy-efficient,
robust, secure, supporting mobility, and frequency efficient. Massive MIMO( Massive multiple-
input multiple-output) has appeared, which achieve most of the communication system
2
requirements by the implementation of the considerable number of small and intelligent
antennas (several hundred) to multiplex data signals and send (or receive) it at the same time
and frequency band at the same time it minimizes to canceling the interferences (intra- and
inter-cell interference)by focusing the radiated power to intended directions. Massive MIMO
requires a significant change to implement the system. This is one of the fundamental
challenges of developing the systems.
Massive MIMO design to implement a vast number of antennas that serve a smaller number of
terminals .to serve the terminals simultaneously. The system needs to know each terminal's CSI
(channel state information). The number of terminals is limited because of the inability to
acquire CSI of the unlimited number of terminals [3].
Several techniques that used to estimate the channel state between the users and BS.
Conventionally each user can estimate its instantaneous channel gain by its mean. These
techniques will work well in Rayleigh fading because these types of channels are assumed to be
hardened (the practical channel gains become nearly deterministic). The precedent techniques
take advantage to conserve the system resources (time and frequency band); however, for some
propagation channels (as keyhole channel), since the hardening state does not achieve, and for
small or moderate numbers of antennas, the channel gain may deviate significantly from its
mean. This will result in poor performance of the system.
The second technique used by the users to estimate their effective channel gain is using the
downlink pilots. These pilots beamformed along with the downlink data, and they must be
orthogonal between users. To estimate the channel gain, the users may use, for example, linear
minimum mean-square error (MMSE) techniques. Maximum-ratio (MR), zero-forcing (ZF),
and minimum mean-square error (MMSE) precoders for downlink. This estimation technique
appears to achieve a good performance in low-mobility environments (where the coherence
interval is long). However, they do not work well in low-mobility environments since the
required overhead (downlink training resources ) is proportional to the number of multiplexed
users. So there is a need for a new way to estimate the effective channel gain, which requires
fewer resources than the transmission of downlink pilots, and good performance.
3
The third technique is the blind channel estimation method, which focuses on the report. In this
report, the Massive MIMO downlink with TDD operation has been considered. The BS
acquires channel state information through the reception of uplink pilot signals transmitted by
the users – in a conventional manner. When transmitting data to the users, it applies MR, ZF, or
MMSE processing with slow time-scale power control. A simple blind method for estimating
the effective gain for this system has been assumed that each user should independently
perform, which does not require any downlink pilots [4].
Keywords
Massive MIMO with Blind Channel Estimation Algorithm, Massive MIMO, MIMO with Blind Channel Estimation Algorithm