997 research outputs found
Symbol-level and Multicast Precoding for Multiuser Multiantenna Downlink: A Survey, Classification and Challenges
Precoding has been conventionally considered as an effective means of
mitigating the interference and efficiently exploiting the available in the
multiantenna downlink channel, where multiple users are simultaneously served
with independent information over the same channel resources. The early works
in this area were focused on transmitting an individual information stream to
each user by constructing weighted linear combinations of symbol blocks
(codewords). However, more recent works have moved beyond this traditional view
by: i) transmitting distinct data streams to groups of users and ii) applying
precoding on a symbol-per-symbol basis. In this context, the current survey
presents a unified view and classification of precoding techniques with respect
to two main axes: i) the switching rate of the precoding weights, leading to
the classes of block- and symbol-level precoding, ii) the number of users that
each stream is addressed to, hence unicast-/multicast-/broadcast- precoding.
Furthermore, the classified techniques are compared through representative
numerical results to demonstrate their relative performance and uncover
fundamental insights. Finally, a list of open theoretical problems and
practical challenges are presented to inspire further research in this area.Comment: Submitted to IEEE Communications Surveys & Tutorial
Electromagnetic Lens-focusing Antenna Enabled Massive MIMO: Performance Improvement and Cost Reduction
Massive multiple-input multiple-output (MIMO) techniques have been recently
advanced to tremendously improve the performance of wireless communication
networks. However, the use of very large antenna arrays at the base stations
(BSs) brings new issues, such as the significantly increased hardware and
signal processing costs. In order to reap the enormous gain of massive MIMO and
yet reduce its cost to an affordable level, this paper proposes a novel system
design by integrating an electromagnetic (EM) lens with the large antenna
array, termed the EM-lens enabled MIMO. The EM lens has the capability of
focusing the power of an incident wave to a small area of the antenna array,
while the location of the focal area varies with the angle of arrival (AoA) of
the wave. Therefore, in practical scenarios where the arriving signals from
geographically separated users have different AoAs, the EM-lens enabled system
provides two new benefits, namely energy focusing and spatial interference
rejection. By taking into account the effects of imperfect channel estimation
via pilot-assisted training, in this paper we analytically show that the
average received signal-to-noise ratio (SNR) in both the single-user and
multiuser uplink transmissions can be strictly improved by the EM-lens enabled
system. Furthermore, we demonstrate that the proposed design makes it possible
to considerably reduce the hardware and signal processing costs with only
slight degradations in performance. To this end, two complexity/cost reduction
schemes are proposed, which are small-MIMO processing with parallel receiver
filtering applied over subgroups of antennas to reduce the computational
complexity, and channel covariance based antenna selection to reduce the
required number of radio frequency (RF) chains. Numerical results are provided
to corroborate our analysis.Comment: 30 pages, 9 figure
A Survey on MIMO Transmission with Discrete Input Signals: Technical Challenges, Advances, and Future Trends
Multiple antennas have been exploited for spatial multiplexing and diversity
transmission in a wide range of communication applications. However, most of
the advances in the design of high speed wireless multiple-input multiple
output (MIMO) systems are based on information-theoretic principles that
demonstrate how to efficiently transmit signals conforming to Gaussian
distribution. Although the Gaussian signal is capacity-achieving, signals
conforming to discrete constellations are transmitted in practical
communication systems. As a result, this paper is motivated to provide a
comprehensive overview on MIMO transmission design with discrete input signals.
We first summarize the existing fundamental results for MIMO systems with
discrete input signals. Then, focusing on the basic point-to-point MIMO
systems, we examine transmission schemes based on three most important criteria
for communication systems: the mutual information driven designs, the mean
square error driven designs, and the diversity driven designs. Particularly, a
unified framework which designs low complexity transmission schemes applicable
to massive MIMO systems in upcoming 5G wireless networks is provided in the
first time. Moreover, adaptive transmission designs which switch among these
criteria based on the channel conditions to formulate the best transmission
strategy are discussed. Then, we provide a survey of the transmission designs
with discrete input signals for multiuser MIMO scenarios, including MIMO uplink
transmission, MIMO downlink transmission, MIMO interference channel, and MIMO
wiretap channel. Additionally, we discuss the transmission designs with
discrete input signals for other systems using MIMO technology. Finally,
technical challenges which remain unresolved at the time of writing are
summarized and the future trends of transmission designs with discrete input
signals are addressed.Comment: 110 pages, 512 references, submit to Proceedings of the IEE
Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink
This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index
Semidefinite Relaxation-Based PAPR-Aware Precoding for Massive MIMO-OFDM Systems
Massive MIMO requires a large number of antennas and the same amount of power
amplifiers (PAs), one per antenna. As opposed to 4G base stations, which could
afford highly linear PAs, next-generation base stations will need to use
inexpensive PAs, which have a limited region of linear amplification. One of
the research challenges is effectively handling signals which have high
peak-to-average power ratios (PAPRs), such as orthogonal frequency division
multiplexing (OFDM). This paper introduces a PAPR-aware precoding scheme that
exploits the excessive spatial degrees-of-freedom of large scale multiple-input
multipleoutput (MIMO) antenna systems. This typically requires finding a
solution to a nonconvex optimization problem. Instead of relaxing the problem
to minimize the peak power, we introduce a practical semidefinite relaxation
(SDR) framework that enables accurately and efficiently approximating the
theoretical PAPR-aware precoding performance for OFDM-based massive MIMO
systems. The framework allows incorporating channel uncertainties and intercell
coordination. Numerical results show that several orders of magnitude
improvements can be achieved w.r.t. state of the art techniques, such as
instantaneous power consumption reduction and multiuser interference
cancellation. The proposed PAPRaware precoding can be effectively handled along
with the multicell signal processing by the centralized baseband processing
platforms of next-generation radio access networks. Performance can be traded
for the computing efficiency for other platform
Massive MIMO and Waveform Design for 5th Generation Wireless Communication Systems
This article reviews existing related work and identifies the main challenges
in the key 5G area at the intersection of waveform design and large-scale
multiple antenna systems, also known as Massive MIMO. The property of
self-equalization is introduced for Filter Bank Multicarrier (FBMC)-based
Massive MIMO, which can reduce the number of subcarriers required by the
system. It is also shown that the blind channel tracking property of FBMC can
be used to address pilot contamination -- one of the main limiting factors of
Massive MIMO systems. Our findings shed light into and motivate for an entirely
new research line towards a better understanding of waveform design with
emphasis on FBMC-based Massive MIMO networks.Comment: 6 pages, 2 figures, 1st International Conference on 5G for Ubiquitous
Connectivit
Performance Analysis of Massive MIMO for Cell-Boundary Users
In this paper, we consider massive multiple-input multiple-output (MIMO)
systems for both downlink and uplink scenarios, where three radio units (RUs)
connected via one digital unit (DU) support multiple user equipments (UEs) at
the cell-boundary through the same radio resource, i.e., the same
time-frequency slot. For downlink transmitter options, the study considers
zero-forcing (ZF) and maximum ratio transmission (MRT), while for uplink
receiver options it considers ZF and maximum ratio combining (MRC). For the sum
rate of each of these, we derive simple closed-form formulas. In the simple but
practically relevant case where uniform power is allocated to all downlink data
streams, we observe that, for the downlink, vector normalization is better for
ZF while matrix normalization is better for MRT. For a given antenna and user
configuration, we also derive analytically the signal-to-noise-ratio (SNR)
level below which MRC should be used instead of ZF. Numerical simulations
confirm our analytical results.Comment: accepted at IEEE Transaction on Wireless Communicatio
Study of Opportunistic Cooperation Techniques using Jamming and Relays for Physical-Layer Security in Buffer-aided Relay Networks
In this paper, we investigate opportunistic relay and jammer cooperation
schemes in multiple-input multiple-output (MIMO) buffer-aided relay networks.
The network consists of one source, an arbitrary number of relay nodes,
legitimate users and eavesdroppers, with the constraints of physical layer
security. We propose an algorithm to select a set of relay nodes to enhance the
legitimate users' transmission and another set of relay nodes to perform
jamming of the eavesdroppers. With Inter-Relay interference (IRI) taken into
account, interference cancellation can be implemented to assist the
transmission of the legitimate users. Secondly, IRI can also be used to further
increase the level of harm of the jamming signal to the eavesdroppers. By
exploiting the fact that the jamming signal can be stored at the relay nodes,
we also propose a hybrid algorithm to set a signal-to-interference and noise
ratio (SINR) threshold at the node to determine the type of signal stored at
the relay node. With this separation, the signals with high SINR are delivered
to the users as conventional relay systems and the low SINR performance signals
are stored as potential jamming signals. Simulation results show that the
proposed techniques obtain a significant improvement in secrecy rate over
previously reported algorithms.Comment: 8 pages, 3 figure
Principal Component Analysis (PCA)-based Massive-MIMO Channel Feedback
Channel-state-information (CSI) feedback methods are considered, especially
for massive or very large-scale multiple-input multiple-output (MIMO) systems.
To extract essential information from the CSI without redundancy that arises
from the highly correlated antennas, a receiver transforms (sparsifies) a
correlated CSI vector to an uncorrelated sparse CSI vector by using a
Karhunen-Loeve transform (KLT) matrix that consists of the eigen vectors of
covariance matrix of CSI vector and feeds back the essential components of the
sparse CSI, i.e., a principal component analysis method. A transmitter then
recovers the original CSI through the inverse transformation of the feedback
vector. Herein, to obtain the covariance matrix at transceiver, we derive
analytically the covariance matrix of spatially correlated Rayleigh fading
channels based on its statistics including transmit antennas' and receive
antennas' correlation matrices, channel variance, and channel delay profile.
With the knowledge of the channel statistics, the transceiver can readily
obtain the covariance matrix and KLT matrix. Compression feedback error and
bit-error-rate performance of the proposed method are analyzed. Numerical
results verify that the proposed method is promising, which reduces
significantly the feedback overhead of the massive-MIMO systems with marginal
performance degradation from full-CSI feedback (e.g., feedback amount reduction
by 80%, i.e., 1/5 of original CSI, with spectral efficiency reduction by only
2%). Furthermore, we show numerically that, for a given limited feedback
amount, we can find the optimal number of transmit antennas to achieve the
largest spectral efficiency, which is a new design framework.Comment: 10 pages, 5 figure
Robust Geometry-Based User Scheduling for Large MIMO Systems Under Realistic Channel Conditions
The problem of user scheduling with reduced overhead of channel estimation in
the uplink of Massive multiple-input multiple-output (MIMO) systems has been
considered. A geometry-based stochastic channel model (GSCM), called the COST
2100 channel model has been used for realistic analysis of channels. In this
paper, we propose a new user selection algorithm based on knowledge of the
geometry of the service area and location of clusters, without having full
channel state information (CSI) at the base station (BS). The multi-user link
correlation in the GSCMs arises from the common clusters in the area. The
throughput depends on the position of clusters in the GSCMs and users in the
system. Simulation results show that although the BS does not require the
channel information of all users, by the proposed geometry-based user
scheduling algorithm the sum-rate of the system is only slightly less than the
well-known greedy weight clique scheme. Finally, the robustness of the proposed
algorithm to the inaccuracy of cluster localization is verified by the
simulation results.Comment: 4 figure
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