147 research outputs found
Downlink channel spatial covariance estimation in realistic FDD massive MIMO systems
The knowledge of the downlink (DL) channel spatial covariance matrix at the
BS is of fundamental importance for large-scale array systems operating in
frequency division duplexing (FDD) mode. In particular, this knowledge plays a
key role in the DL channel state information (CSI) acquisition. In the massive
MIMO regime, traditional schemes based on DL pilots are severely limited by the
covariance feedback and the DL training overhead. To overcome this problem,
many authors have proposed to obtain an estimate of the DL spatial covariance
based on uplink (UL) measurements. However, many of these approaches rely on
simple channel models, and they are difficult to extend to more complex models
that take into account important effects of propagation in 3D environments and
of dual-polarized antenna arrays. In this study we propose a novel technique
that takes into account the aforementioned effects, in compliance with the
requirements of modern 4G and 5G system designs. Numerical simulations show the
effectiveness of our approach.Comment: [v2] is the version accepted at GlobalSIP 2018. Only minor changes
mainly in the introductio
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
Blind CSI acquisition for multi-antenna interference mitigation in 5G networks
Future wireless communication networks are required to satisfy the increasing demands
of traffic and capacity. The upcoming fifth generation (5G) of the cellular
technology is expected to meet 1000 times the capacity that of the current fourth
generation (4G). These tight specifications introduce a new set of research challenges.
However, interference has always been the bottleneck in cellular communications.
Thus, towards the vision of the 5G, massive multi-input multi-output (mMIMO) and
interference alignment (IA) are key transmission technologies to fulfil the future requirements,
by controlling the residual interference.
By equipping the base-station (BS) with a large number of transmit antennas, e.g,
tens of hundreds of antennas, a mMIMO system can theoretically achieve significant
capacity with limited interference, where many user equipment (UEs) can be served
simultaneously at the same time and frequency resources. A mMIMO offers great
spatial degrees of freedom (DoFs), which boost the total network capacity without
increasing transmission power or bandwidth. However, the majority of the recent
mMIMO investigations are based on theoretical channels with independent and identically
distributed (i.i.d) Gaussian distribution, which facilitates the computation of
closed-form rate expressions. Nonetheless, practical channels are not spatially uncorrelated,
where the BS receives different power ratios across different spatial directions
between the same transmitting and receiving antennas. Thus, it is important to understand the behavior of such new technology with practical channel modeling.
Alternatively, IA is known to break the bottleneck between the capacity of the
network and the overall spectral efficiency (SE), where a performance degradation
is observed at a certain level of connected user capacity, due to the overwhelming
inter-user interference. Theoretically, IA guarantees a linear relationship between
half of the overall network SE and the online capacity by aligning interference from
all transmitters inside one spatial signal subspace, leaving the other subspace for
desired transmission. However, IA has tight feasibility conditions in practice including
high precision channel state information at transmitter (CSIT), which leads to severe
feedback overhead.
In this thesis, high-precision blind CSIT algorithms are developed under different
transmission technologies. We first consider the CSIT acquisition problem in MIMO
IA systems. Proposed spatial channel estimation for MIMO-IA systems (SCEIA)
shows great offered spatial degrees of freedom which contributes to approaching the
performance of the perfect-CSIT case, without the requirements of channel quantization
or user feedback overhead. In massive MIMO setups, proposed CSIT strategy
offered scalable performance with the number of the transmit antennas. The effect
of the non-stationary channel characteristics, which appears with very large antenna
arrays, is minimized due to the effective scanning precision of the proposed strategy.
Finally, we extend the system model to the full dimensional space, where users are distributed
across the two dimensions of the cell space (azimuthal/elevation). Proposed
directional spatial channel estimation (D-SCE) scans the 3D cell space and effectively
attains additional CSIT and beamforming gains. In all cases, a list of comparisons
with state-of-the-art schemes from academia and industry is performed to show the
performance improvement of the proposed CSIT strategies
Exploitation of Robust AoA Estimation and Low Overhead Beamforming in mmWave MIMO System
The limited spectral resource for wireless communications and dramatic proliferation of new applications and services directly necessitate the exploitation of millimeter wave (mmWave) communications. One critical enabling technology for mmWave communications is multi-input multi-output (MIMO), which enables other important physical layer techniques, specifically beamforming and antenna array based angle of arrival (AoA) estimation. Deployment of beamforming and AoA estimation has many challenges. Significant training and feedback overhead is required for beamforming, while conventional AoA estimation methods are not fast or robust. Thus, in this thesis, new algorithms are designed for low overhead beamforming, and robust AoA estimation with significantly reduced signal samples (snapshots).
The basic principle behind the proposed low overhead beamforming algorithm in time-division duplex (TDD) systems is to increase the beam serving period for the reduction of the feedback frequency. With the knowledge of location and speed of each candidate user equipment (UE), the codeword can be selected from the designed multi-pattern codebook, and the corresponding serving period can be estimated. The UEs with long serving period and low interference are selected and served simultaneously. This algorithm is proved to be effective in keeping the high data rate of conventional codebook-based beamforming, while the feedback required for codeword selection can be cut down.
A fast and robust AoA estimation algorithm is proposed as the basis of the low overhead beamforming for frequency-division duplex (FDD) systems. This algorithm utilizes uplink transmission signals to estimate the real-time AoA for angle-based beamforming in environments with different signal to noise ratios (SNR). Two-step neural network models are designed for AoA estimation. Within the angular group classified by the first model, the second model further estimates AoA with high accuracy. It is proved that these AoA estimation models work well with few signal snapshots, and are robust to applications in low SNR environments. The proposed AoA estimation algorithm based beamforming generates beams without using reference signals. Therefore, the low overhead beamforming can be achieved in FDD systems.
With the support of proposed algorithms, the mmWave resource can be leveraged to meet challenging requirements of new applications and services in wireless communication systems
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
Millimetre wave frequency band as a candidate spectrum for 5G network architecture : a survey
In order to meet the huge growth in global mobile data traffic in 2020 and beyond, the development of the 5th Generation (5G) system is required as the current 4G system is expected to fall short of the provision needed for such growth. 5G is anticipated to use a higher carrier frequency in the millimetre wave (mm-wave) band, within the 20 to 90 GHz, due to the availability of a vast amount of unexploited bandwidth. It is a revolutionary step to use these bands because of their different propagation characteristics, severe atmospheric attenuation, and hardware constraints. In this paper, we carry out a survey of 5G research contributions and proposed design architectures based on mm-wave communications. We present and discuss the use of mm-wave as indoor and outdoor mobile access, as a wireless backhaul solution, and as a key enabler for higher order sectorisation. Wireless standards such as IEE802.11ad, which are operating in mm-wave band have been presented. These standards have been designed for short range, ultra high data throughput systems in the 60 GHz band. Furthermore, this survey provides new insights regarding relevant and open issues in adopting mm-wave for 5G networks. This includes increased handoff rate and interference in Ultra-Dense Network (UDN), waveform consideration with higher spectral efficiency, and supporting spatial multiplexing in mm-wave line of sight. This survey also introduces a distributed base station architecture in mm-wave as an approach to address increased handoff rate in UDN, and to provide an alternative way for network densification in a time and cost effective manner
A survey on hybrid beamforming techniques in 5G : architecture and system model perspectives
The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain
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