760 research outputs found
Massive MIMO systems for 5G: a systematic mapping study on antenna design challenges and channel estimation open issues
The next generation of mobile networks (5G) is expected to achieve high data rates, reduce latency, as well as improve the spectral and energy efficiency of wireless communication systems. Several technologies are being explored to be used in 5G systems. One of the main promising technologies that is seen to be the enabler of 5G is massive multiple-input multiple-output (mMIMO) systems. Numerous studies have indicated the utility of mMIMO in upcoming wireless networks. However, there are several challenges that needs to be unravelled. In this paper, the latest progress of research on challenges in mMIMO systems is tracked, in the context of mutual coupling, antenna selection, pilot contamination and feedback overhead. The results of a systematic mapping study performed on 63 selected primary studies, published between the year 2017 till the second quarter of 2020, are presented. The main objective of this secondary study is to identify the challenges regarding antenna design and channel estimation, give an overview on the state-of-the-art solutions proposed in the literature, and finally, discuss emerging open research issues that need to be considered before the implementation of mMIMO systems in 5G networks
Mobile node-aided localization and tracking in terrestrial and underwater networks
In large-scale wireless sensor networks (WSNs), the position information of individual
sensors is very important for many applications. Generally, there are a small number
of position-aware nodes, referred to as the anchors. Every other node can estimate its
distances to the surrounding anchors, and then employ trilateration or triangulation for
self-localization. Such a system is easy to implement, and thus popular for both terrestrial
and underwater applications, but it suffers from some major drawbacks. First, the density
of the anchors is generally very low due to economical considerations, leading to poor
localization accuracy. Secondly, the energy and bandwidth consumptions of such systems
are quite significant. Last but not the least, the scalability of a network based on fixed
anchors is not good. Therefore, whenever the network expands, more anchors should be
deployed to guarantee the required performance. Apart from these general challenges,
both terrestrial and underwater networks have their own specific ones. For example, realtime
channel parameters are generally required for localization in terrestrial WSNs. For
underwater networks, the clock skew between the target sensor and the anchors must
be considered. That is to say, time synchronization should be performed together with
localization, which makes the problem complicated.
An alternative approach is to employ mobile anchors to replace the fixed ones. For
terrestrial networks, commercial drones and unmanned aerial vehicles (UAVs) are very
good choices, while autonomous underwater vehicles (AUVs) can be used for underwater
applications. Mobile anchors can move along a predefined trajectory and broadcast beacon
signals. By listening to the messages, the other nodes in the network can localize themselves
passively. This architecture has three major advantages: first, energy and bandwidth consumptions can be significantly reduced; secondly, the localization accuracy can be much
improved with the increased number of virtual anchors, which can be boosted at negligible
cost; thirdly, the coverage can be easily extended, which makes the solution and the network
highly scalable.
Motivated by this idea, this thesis investigates the mobile node-aided localization and
tracking in large-scale WSNs. For both terrestrial and underwater WSNs, the system
design, modeling, and performance analyses will be presented for various applications,
including: (1) the drone-assisted localization in terrestrial networks; (2) the ToA-based
underwater localization and time synchronization; (3) the Doppler-based underwater localization;
(4) the underwater target detection and tracking based on the convolutional
neural network and the fractional Fourier transform. In these applications, different challenges
will present, and we will see how these challenges can be addressed by replacing
the fixed anchors with mobile ones. Detailed mathematical models will be presented, and
extensive simulation and experimental results will be provided to verify the theoretical
results. Also, we will investigate the channel estimation for the fifth generation (5G) wireless
communications. A pilot decontamination method will be presented for the massive
multiple-input-multiple-output communications, and the data-aided channel tracking will
be discussed for millimeter wave communications. We will see that the localization problem
is highly coupled with the channel estimation in wireless communications
Deep learning-based space-time coding wireless MIMO receiver optimization.
Doctoral Degree. University of KwaZulu-Natal, Durban.With the high demand for high data throughput and reliable wireless links to cater for real-time or low latency mobile application services, the wireless research community has developed wireless multiple-input multiple-output (MIMO) architectures that cater to these stringent quality of service (QoS) requirements. For the case of wireless link reliability, spatial diversity in wireless MIMO architectures is used to increase the link reliability. Besides increasing link reliability using spatial diversity, space-time block coding schemes may be used to further increase the wireless link reliability by adding time diversity to the wireless link. Our research is centered around the optimization of resources used in decoding space-time block coded wireless signals. There are two categories of space-time block coding schemes namely the orthogonal and non-orthogonal space-time block codes (STBC). In our research, we concentrate on two non-orthogonal STBC schemes namely the uncoded space-time labeling diversity (USTLD) and the Golden code. These two non-orthogonal STBC schemes exhibit some advantages over the orthogonal STBC called Alamouti despite their non-linear optimal detection. Orthogonal STBC schemes have the advantage of simple linear optimal detection relative to the more complex non-linear optimal detection of non-orthogonal STBC schemes. Since our research concentrates on wireless MIMO STBC transmission, for detection to occur optimally at the receiver side of a space-time block coded wireless MIMO link, we need to optimally perform channel estimation and decoding.
USTLD has a coding gain advantage over the Alamouti STBC scheme. This implies that the USTLD can deliver higher wireless link reliability relative to the Alamouti STBC for the same spectral efficiency. Despite this advantage of the USTLD, to the best of our knowledge, the literature has concentrated on USTLD wireless transmission under the assumption that the wireless receiver has full knowledge of the wireless channel without estimation errors. We thus perform research of the USTLD wireless MIMO transmission with imperfect channel estimation. The traditional least-squares (LS) and minimum mean squared error (MMSE) used in literature, for imperfect pilot-assisted channel estimation, require the full knowledge of the transmitted pilot symbols and/or wireless channel second order statistics which may not always be fully known. We, therefore, propose blind channel estimation facilitated by a deep learning model that makes it unnecessary to have prior knowledge of the wireless channel second order statistics, transmitted pilot symbols and/or average noise power. We also derive an optimal number of pilot symbols that maybe used for USTLD wireless MIMO channel estimation without compromising the wireless link reliability. It is shown from the Monte Carlo simulations that the error rate performance of the USTLD transmission is not compromised despite using only 20% of the required number of Zadoff-Chu sequence pilot symbols used by the traditional LS and MMSE channel estimators for both 16-QAM and 16-PSK baseband modulation.
The Golden code is a STBC scheme with spatial multiplexing gain over the Alamouti scheme. This implies that the Golden code can deliver higher spectral efficiencies for the same link reliability with the Alamouti scheme. The Alamouti scheme has been implemented in the modern wireless standards because it adds time diversity, with low decoding complexity, to wireless MIMO links. The Golden code adds time diversity and improves wireless MIMO spectral efficiency but at the cost of much higher decoding complexity relative to the Alamouti scheme. Because of the high decoding complexity, the Golden code is not widely adopted in the modern wireless standards. We, therefore, propose analytical and deep learning-based sphere-decoding algorithms to lower the number of detection floating-point operations (FLOPS) and decoding latency of the Golden code under low- and high-density M-ary quadrature amplitude modulation (M-QAM) baseband transmissions whilst maintaining the near-optimal error rate performance. The proposed sphere-decoding algorithms achieve at most 99% reduction in Golden code detection FLOPS, at low SNR, relative to the sphere-decoder with sorted detection subsets (SD-SDS) whilst maintaining the error rate performance. For the case of high-density M-QAM Golden code transmission, the proposed analytical and deep learning sphere-decoders reduce decoding latency by at most 70%, relative to the SD-SDS decoder, without diminishing the error rate performance
Bilinear Gaussian Belief Propagation for Massive MIMO Detection with Non-Orthogonal Pilots
Ito K., Takahashi T., Ibi S., et al. Bilinear Gaussian Belief Propagation for Massive MIMO Detection with Non-Orthogonal Pilots. IEEE Transactions on Communications , (2023); https://doi.org/10.1109/TCOMM.2023.3325479.We propose a novel joint channel and data estimation (JCDE) algorithm via bilinear Gaussian belief propagation (BiGaBP) for massive multi-user MIMO (MU-MIMO) systems with non-orthogonal pilot sequences. The contribution aims to reduce significantly the communication overhead required for channel acquisition by enabling the use of short non-orthogonal pilots, while maintaining multi-user detection (MUD) capability. Bilinear generalized approximate message passing (BiGAMP), which is systematically derived by extending approximate message passing (AMP) to the bilinear inference problem (BIP), provides computationally efficient approximate implementations of large-scale JCDE via sum-product algorithm (SPA); however, as the pilot length decreases, the estimation accuracy is severely degraded. To tackle this issue, the proposed BiGaBP algorithm generalizes BiGAMP by relaxing its dependence on the large-system limit approximation and leveraging the belief propagation (BP) concept. In addition, a novel belief scaling method complying with the data detection accuracy for each iteration step is designed to avoid the divergence behavior of iterative estimation in the early iterations due to the use of non-orthogonal pilots, especially in insufficient large-system conditions. Simulation results show that the proposed method outperforms the state-of-the-art schemes and approaches the performance of idealized (genie-aided) scheme in terms of mean square error (MSE) and bit error rate (BER) performances
Foundations of User-Centric Cell-Free Massive MIMO
Imagine a coverage area where each mobile device is communicating with a
preferred set of wireless access points (among many) that are selected based on
its needs and cooperate to jointly serve it, instead of creating autonomous
cells. This effectively leads to a user-centric post-cellular network
architecture, which can resolve many of the interference issues and
service-quality variations that appear in cellular networks. This concept is
called User-centric Cell-free Massive MIMO (multiple-input multiple-output) and
has its roots in the intersection between three technology components: Massive
MIMO, coordinated multipoint processing, and ultra-dense networks. The main
challenge is to achieve the benefits of cell-free operation in a practically
feasible way, with computational complexity and fronthaul requirements that are
scalable to enable massively large networks with many mobile devices. This
monograph covers the foundations of User-centric Cell-free Massive MIMO,
starting from the motivation and mathematical definition. It continues by
describing the state-of-the-art signal processing algorithms for channel
estimation, uplink data reception, and downlink data transmission with either
centralized or distributed implementation. The achievable spectral efficiency
is mathematically derived and evaluated numerically using a running example
that exposes the impact of various system parameters and algorithmic choices.
The fundamental tradeoffs between communication performance, computational
complexity, and fronthaul signaling requirements are thoroughly analyzed.
Finally, the basic algorithms for pilot assignment, dynamic cooperation cluster
formation, and power optimization are provided, while open problems related to
these and other resource allocation problems are reviewed. All the numerical
examples can be reproduced using the accompanying Matlab code.Comment: This is the authors' version of the manuscript: \"Ozlem Tugfe Demir,
Emil Bj\"ornson and Luca Sanguinetti (2021), "Foundations of User-Centric
Cell-Free Massive MIMO", Foundations and Trends in Signal Processing: Vol.
14, No. 3-4, pp 162-47
Low computational SLAM for an autonomous indoor aerial inspection vehicle
The past decade has seen an increase in the capability of small scale Unmanned
Aerial Vehicle (UAV) systems, made possible through technological advancements
in battery, computing and sensor miniaturisation technology. This has opened a new
and rapidly growing branch of robotic research and has sparked the imagination of
industry leading to new UAV based services, from the inspection of power-lines to
remote police surveillance.
Miniaturisation of UAVs have also made them small enough to be practically flown
indoors. For example, the inspection of elevated areas in hazardous or damaged
structures where the use of conventional ground-based robots are unsuitable. Sellafield
Ltd, a nuclear reprocessing facility in the U.K. has many buildings that require
frequent safety inspections. UAV inspections eliminate the current risk to personnel
of radiation exposure and other hazards in tall structures where scaffolding or hoists
are required.
This project focused on the development of a UAV for the novel application of
semi-autonomously navigating and inspecting these structures without the need for
personnel to enter the building. Development exposed a significant gap in knowledge
concerning indoor localisation, specifically Simultaneous Localisation and Mapping
(SLAM) for use on-board UAVs. To lower the on-board processing requirements
of SLAM, other UAV research groups have employed techniques such as off-board
processing, reduced dimensionality or prior knowledge of the structure, techniques
not suitable to this application given the unknown nature of the structures and the
risk of radio-shadows.
In this thesis a novel localisation algorithm, which enables real-time and threedimensional
SLAM running solely on-board a computationally constrained UAV in
heavily cluttered and unknown environments is proposed. The algorithm, based
on the Iterative Closest Point (ICP) method utilising approximate nearest neighbour
searches and point-cloud decimation to reduce the processing requirements has
successfully been tested in environments similar to that specified by Sellafield Ltd
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