409 research outputs found
Multiuser Precoding and Channel Estimation for Hybrid Millimeter Wave MIMO Systems
In this paper, we develop a low-complexity channel estimation for hybrid
millimeter wave (mmWave) systems, where the number of radio frequency (RF)
chains is much less than the number of antennas equipped at each transceiver.
The proposed channel estimation algorithm aims to estimate the strongest
angle-of-arrivals (AoAs) at both the base station (BS) and the users. Then all
the users transmit orthogonal pilot symbols to the BS via these estimated
strongest AoAs to facilitate the channel estimation. The algorithm does not
require any explicit channel state information (CSI) feedback from the users
and the associated signalling overhead of the algorithm is only proportional to
the number of users, which is significantly less compared to various existing
schemes. Besides, the proposed algorithm is applicable to both non-sparse and
sparse mmWave channel environments. Based on the estimated CSI, zero-forcing
(ZF) precoding is adopted for multiuser downlink transmission. In addition, we
derive a tight achievable rate upper bound of the system. Our analytical and
simulation results show that the proposed scheme offer a considerable
achievable rate gain compared to fully digital systems, where the number of RF
chains equipped at each transceiver is equal to the number of antennas.
Furthermore, the achievable rate performance gap between the considered hybrid
mmWave systems and the fully digital system is characterized, which provides
useful system design insights.Comment: 6 pages, accepted for presentation, ICC 201
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Massive MIMO Performance - TDD Versus FDD: What Do Measurements Say?
Downlink beamforming in Massive MIMO either relies on uplink pilot
measurements - exploiting reciprocity and TDD operation, or on the use of a
predetermined grid of beams with user equipments reporting their preferred
beams, mostly in FDD operation. Massive MIMO in its originally conceived form
uses the first strategy, with uplink pilots, whereas there is currently
significant commercial interest in the second, grid-of-beams. It has been
analytically shown that in isotropic scattering (independent Rayleigh fading)
the first approach outperforms the second. Nevertheless there remains
controversy regarding their relative performance in practice. In this
contribution, the performances of these two strategies are compared using
measured channel data at 2.6 GHz.Comment: Submitted to IEEE Transactions on Wireless Communications,
31/Mar/201
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
Millimeter Wave Systems for Wireless Cellular Communications
This thesis considers channel estimation and multiuser (MU) data transmission
for massive MIMO systems with fully digital/hybrid structures in mmWave
channels. It contains three main contributions. In this thesis, we first
propose a tone-based linear search algorithm to facilitate the estimation of
angle-of-arrivals of the strongest components as well as scattering components
of the users at the base station (BS) with fully digital structure. Our results
show that the proposed maximum-ratio transmission (MRT) based on the strongest
components can achieve a higher data rate than that of the conventional MRT,
under the same mean squared errors (MSE). Second, we develop a low-complexity
channel estimation and beamformer/precoder design scheme for hybrid mmWave
systems. In addition, the proposed scheme applies to both non-sparse and sparse
mmWave channel environments. We then leverage the proposed scheme to
investigate the downlink achievable rate performance. The results show that the
proposed scheme obtains a considerable achievable rate of fully digital
systems. Taking into account the effect of various types of errors, we
investigate the achievable rate performance degradation of the considered
scheme. Third, we extend our proposed scheme to a multi-cell MU mmWave MIMO
network. We derive the closed-form approximation of the normalized MSE of
channel estimation under pilot contamination over Rician fading channels.
Furthermore, we derive a tight closed-form approximation and the scaling law of
the average achievable rate. Our results unveil that channel estimation errors,
the intra-cell interference, and the inter-cell interference caused by pilot
contamination over Rician fading channels can be efficiently mitigated by
simply increasing the number of antennas equipped at the desired BS.Comment: Thesi
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