448 research outputs found
Low-complexity Location-aware Multi-user Massive MIMO Beamforming for High Speed Train Communications
Massive Multiple-input Multiple-output (MIMO) adaption is one of the primary
evolving objectives for the next generation high speed train (HST)
communication system. In this paper, we consider how to design an efficient
low-complexity location-aware beamforming for the multi-user (MU) massive MIMO
system in HST scenario. We first put forward a low-complexity beamforming based
on location information, where multiple users are considered. Then, without
considering inter-beam interference, a closed-form solution to maximize the
total service competence of base station (BS) is proposed in this MU HST
scenario. Finally, we present a location-aid searching-based suboptimal
solution to eliminate the inter-beam interference and maximize the BS service
competence. Various simulations are given to exhibit the advantages of our
proposed massive MIMO beamforming method.Comment: This paper has been accepted for future publication by VTC2017-Sprin
Performance analysis of massive multiple input multiple output for high speed railway
This paper analytically reviews the performance of massive multiple input multiple output (MIMO) system for communication in highly mobility scenarios like high speed Railways. As popularity of high speed train increasing day by day, high data rate wireless communication system for high speed train is extremely required. 5G wireless communication systems must be designed to meet the requirement of high speed broadband services at speed of around 500 km/h, which is the expected speed achievable by HSR systems, at a data rate of 180 Mbps or higher. Significant challenges of high mobility communications are fast time-varying fading, channel estimation errors, doppler diversity, carrier frequency offset, inter carrier interference, high penetration loss and fast and frequent handovers. Therefore, crucial requirement to design high mobility communication channel models or systems prevails. Recently, massive MIMO techniques have been proposed to significantly improve the performance of wireless networks for upcoming 5G technology. Massive MIMO provide high throughput and high energy efficiency in wireless communication channel. In this paper, key findings, challenges and requirements to provide high speed wireless communication onboard the high speed train is pointed out after thorough literature review. In last, future research scope to bridge the research gap by designing efficient channel model by using massive MIMO and other optimization method is mentioned
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
Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance
The pervasive nature of wireless telecommunication has made it the foundation
for mainstream technologies like automation, smart vehicles, virtual reality,
and unmanned aerial vehicles. As these technologies experience widespread
adoption in our daily lives, ensuring the reliable performance of cellular
networks in mobile scenarios has become a paramount challenge. Beamforming, an
integral component of modern mobile networks, enables spatial selectivity and
improves network quality. However, many beamforming techniques are iterative,
introducing unwanted latency to the system. In recent times, there has been a
growing interest in leveraging mobile users' location information to expedite
beamforming processes. This paper explores the concept of contextual
beamforming, discussing its advantages, disadvantages and implications.
Notably, the study presents an impressive 53% improvement in signal-to-noise
ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared
to scenarios without beamforming. It further elucidates how MRT contributes to
contextual beamforming. The importance of localization in implementing
contextual beamforming is also examined. Additionally, the paper delves into
the use of artificial intelligence schemes, including machine learning and deep
learning, in implementing contextual beamforming techniques that leverage user
location information. Based on the comprehensive review, the results suggest
that the combination of MRT and Zero forcing (ZF) techniques, alongside deep
neural networks (DNN) employing Bayesian Optimization (BO), represents the most
promising approach for contextual beamforming. Furthermore, the study discusses
the future potential of programmable switches, such as Tofino, in enabling
location-aware beamforming
Robust Location-Aided Beam Alignment in Millimeter Wave Massive MIMO
Location-aided beam alignment has been proposed recently as a potential
approach for fast link establishment in millimeter wave (mmWave) massive MIMO
(mMIMO) communications. However, due to mobility and other imperfections in the
estimation process, the spatial information obtained at the base station (BS)
and the user (UE) is likely to be noisy, degrading beam alignment performance.
In this paper, we introduce a robust beam alignment framework in order to
exhibit resilience with respect to this problem. We first recast beam alignment
as a decentralized coordination problem where BS and UE seek coordination on
the basis of correlated yet individual position information. We formulate the
optimum beam alignment solution as the solution of a Bayesian team decision
problem. We then propose a suite of algorithms to approach optimality with
reduced complexity. The effectiveness of the robust beam alignment procedure,
compared with classical designs, is then verified on simulation settings with
varying location information accuracies.Comment: 24 pages, 7 figures. The short version of this paper has been
accepted to IEEE Globecom 201
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