60 research outputs found
Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits
The use of large-scale antenna arrays can bring substantial improvements in
energy and/or spectral efficiency to wireless systems due to the greatly
improved spatial resolution and array gain. Recent works in the field of
massive multiple-input multiple-output (MIMO) show that the user channels
decorrelate when the number of antennas at the base stations (BSs) increases,
thus strong signal gains are achievable with little inter-user interference.
Since these results rely on asymptotics, it is important to investigate whether
the conventional system models are reasonable in this asymptotic regime. This
paper considers a new system model that incorporates general transceiver
hardware impairments at both the BSs (equipped with large antenna arrays) and
the single-antenna user equipments (UEs). As opposed to the conventional case
of ideal hardware, we show that hardware impairments create finite ceilings on
the channel estimation accuracy and on the downlink/uplink capacity of each UE.
Surprisingly, the capacity is mainly limited by the hardware at the UE, while
the impact of impairments in the large-scale arrays vanishes asymptotically and
inter-user interference (in particular, pilot contamination) becomes
negligible. Furthermore, we prove that the huge degrees of freedom offered by
massive MIMO can be used to reduce the transmit power and/or to tolerate larger
hardware impairments, which allows for the use of inexpensive and
energy-efficient antenna elements.Comment: To appear in IEEE Transactions on Information Theory, 28 pages, 15
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/massive-MIMO-hardware-impairment
Robust Transceiver Design for AF MIMO Relay Systems with Column Correlations
In this paper, we investigate the robust transceiver design for dual-hop
amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel
estimation errors. Aiming at maximizing the mutual information under imperfect
channel state information (CSI), source precoder at source and forwarding
matrix at the relay are jointly optimized. Using some elegant attributes of
matrix-monotone functions, the structures of the optimal solutions are derived
first. Then based on the derived structure an iterative waterfilling solution
is proposed. Several existing algorithms are shown to be special cases of the
proposed solution. Finally, the effectiveness of the proposed robust design is
demonstrated by simulation results.Comment: 6 Pages, 1 Figur
Robust joint design of linear relay precoder and destination equalizer for dual-hop amplify-and-forward MIMO relay systems
This paper addresses the problem of robust linear relay precoder and destination equalizer design for a dual-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system, with Gaussian random channel uncertainties in both hops. By taking the channel uncertainties into account, two robust design algorithms are proposed to minimize the mean-square error (MSE) of the output signal at the destination. One is an iterative algorithm with its convergence proved analytically. The other is an approximated closed-form solution with much lower complexity than the iterative algorithm. Although the closed-form solution involves a minor relaxation for the general case, when the column covariance matrix of the channel estimation error at the second hop is proportional to identity matrix, no relaxation is needed and the proposed closed-form solution is the optimal solution. Simulation results show that the proposed algorithms reduce the sensitivity of the AF MIMO relay systems to channel estimation errors, and perform better than the algorithm using estimated channels only. Furthermore, the closed-form solution provides a comparable performance to that of the iterative algorithm. © 2006 IEEE.published_or_final_versio
Robust Beamforming for Amplify-and-Forward MIMO Relay Systems Based on Quadratic Matrix Programming
In this paper, robust transceiver design based on minimum-mean-square-error
(MMSE) criterion for dual-hop amplify-and-forward MIMO relay systems is
investigated. The channel estimation errors are modeled as Gaussian random
variables, and then the effect are incorporated into the robust transceiver
based on the Bayesian framework. An iterative algorithm is proposed to jointly
design the precoder at the source, the forward matrix at the relay and the
equalizer at the destination, and the joint design problem can be efficiently
solved by quadratic matrix programming (QMP).Comment: Proceedings of IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICASSP'2010), U.S.
Joint robust weighted LMMSE transceiver design for dual-hop AF multiple-antenna relay systems
In this paper, joint transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel estimation errors in both two hops is investigated. Due to the fact that various linear transceiver designs can be transformed to a weighted linear minimum mean-square-error (LMMSE) transceiver design with specific weighting matrices, weighted mean square error (MSE) is chosen as the performance metric. Precoder matrix at source, forwarding matrix at relay and equalizer matrix at destination are jointly designed with channel estimation errors taken care of by Bayesian philosophy. Several existing algorithms are found to be special cases of the proposed solution. The performance advantage of the proposed robust design is demonstrated by the simulation results. © 2011 IEEE.published_or_final_versionThe 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011), Beijing, China, 5-9 December 2011. In Globecom. IEEE Conference and Exhibition, 2011, p. 1-
Matrix-Monotonic Optimization for MIMO Systems
For MIMO systems, due to the deployment of multiple antennas at both the
transmitter and the receiver, the design variables e.g., precoders, equalizers,
training sequences, etc. are usually matrices. It is well known that matrix
operations are usually more complicated compared to their vector counterparts.
In order to overcome the high complexity resulting from matrix variables, in
this paper we investigate a class of elegant multi-objective optimization
problems, namely matrix-monotonic optimization problems (MMOPs). In our work,
various representative MIMO optimization problems are unified into a framework
of matrix-monotonic optimization, which includes linear transceiver design,
nonlinear transceiver design, training sequence design, radar waveform
optimization, the corresponding robust design and so on as its special cases.
Then exploiting the framework of matrix-monotonic optimization the optimal
structures of the considered matrix variables can be derived first. Based on
the optimal structure, the matrix-variate optimization problems can be greatly
simplified into the ones with only vector variables. In particular, the
dimension of the new vector variable is equal to the minimum number of columns
and rows of the original matrix variable. Finally, we also extend our work to
some more general cases with multiple matrix variables.Comment: 37 Pages, 5 figures, IEEE Transactions on Signal Processing, Final
Versio
Robust Transceiver with Tomlinson-Harashima Precoding for Amplify-and-Forward MIMO Relaying Systems
In this paper, robust transceiver design with Tomlinson-Harashima precoding
(THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output
(MIMO) relaying systems is investigated. At source node, THP is adopted to
mitigate the spatial intersymbol interference. However, due to its nonlinear
nature, THP is very sensitive to channel estimation errors. In order to reduce
the effects of channel estimation errors, a joint Bayesian robust design of THP
at source, linear forwarding matrices at relays and linear equalizer at
destination is proposed. With novel applications of elegant characteristics of
multiplicative convexity and matrix-monotone functions, the optimal structure
of the nonlinear transceiver is first derived. Based on the derived structure,
the transceiver design problem reduces to a much simpler one with only scalar
variables which can be efficiently solved. Finally, the performance advantage
of the proposed robust design over non-robust design is demonstrated by
simulation results.Comment: IEEE Journal on Selected Areas in Communications - Special Issue on
Theories and Methods for Advanced Wireless Relays The final version and
several typos have been correcte
Full-duplex MU-MIMO systems under the effects of non-ideal transceivers: performance analysis and power allocation optimization
Modern Technologies, particularly connectivity, increasingly support many facets of everyday life. The next generation of wireless communication systems aims to provide new
advanced services and support new demands. These services are required to serve a massive number of devices and achieve higher spectral and energy efficiency, ultra-low latency,
and reliable communication. The research community around the globe is still working on
finding novel technologies to meet these requirements. Full duplex (FD) communications
have been recognized as one of the promising wireless transmission candidates and gamechangers for the future of wireless communication and networking technologies, thanks to
their ability to greatly improve spectral efficiency (SE) and dramatically enhance energy
efficiency (EE). In this thesis, first, the influence of hardware impairment (HWI) on singleinput single-output (SISO) FD access point (AP) is studied. More precisely, the SE and
EE when the system’s terminals have impaired transceivers are analyzed. Optimization
problem for EE maximization is formulated to fulfill quality of service (QoS) and power
budget constraints. An algorithm to solve the optimization problem by using the fractional
programming theory and Karush–Kuhn–Tucker (KKT) conditions technique is proposed. [...
Unified Joint Matrix-Monotonic Optimization of MIMO Training Sequences and Transceivers
Channel estimation and transmission constitute the most fundamental
functional modules of multiple-input multiple-output (MIMO) communication
systems. The underlying key tasks corresponding to these modules are training
sequence optimization and transceiver optimization. Hence, we jointly optimize
the linear transmit precoder and the training sequence of MIMO systems using
the metrics of their effective mutual information (MI), effective mean squared
error (MSE), effective weighted MI, effective weighted MSE, as well as their
effective generic Schur-convex and Schur-concave functions. Both statistical
channel state information (CSI) and estimated CSI are considered at the
transmitter in the joint optimization. A unified framework termed as joint
matrix-monotonic optimization is proposed. Based on this, the optimal precoder
matrix and training matrix structures can be derived for both CSI scenarios.
Then, based on the optimal matrix structures, our linear transceivers and their
training sequences can be jointly optimized. Compared to state-of-the-art
benchmark algorithms, the proposed algorithms visualize the bold explicit
relationships between the attainable system performance of our linear
transceivers conceived and their training sequences, leading to implementation
ready recipes. Finally, several numerical results are provided, which
corroborate our theoretical results and demonstrate the compelling benefits of
our proposed pilot-aided MIMO solutions.Comment: 29 pages, 7 figure
Optimization of Massive Full-Dimensional MIMO for Positioning and Communication
Massive Full-Dimensional multiple-input multiple-output (FD-MIMO) base
stations (BSs) have the potential to bring multiplexing and coverage gains by
means of three-dimensional (3D) beamforming. Key technical challenges for their
deployment include the presence of limited-resolution front ends and the
acquisition of channel state information (CSI) at the BSs. This paper
investigates the use of FD-MIMO BSs to provide simultaneously high-rate data
communication and mobile 3D positioning in the downlink. The analysis
concentrates on the problem of beamforming design by accounting for imperfect
CSI acquisition via Time Division Duplex (TDD)-based training and for the
finite resolution of analog-to-digital converter (ADC) and digital-to-analog
converter (DAC) at the BSs. Both \textit{unstructured beamforming} and a
low-complexity \textit{Kronecker beamforming} solution are considered, where
for the latter the beamforming vectors are decomposed into separate azimuth and
elevation components. The proposed algorithmic solutions are based on Bussgang
theorem, rank-relaxation and successive convex approximation (SCA) methods.
Comprehensive numerical results demonstrate that the proposed schemes can
effectively cater to both data communication and positioning services,
providing only minor performance degradations as compared to the more
conventional cases in which either function is implemented. Moreover, the
proposed low-complexity Kronecker beamforming solutions are seen to guarantee a
limited performance loss in the presence of a large number of BS antennas.Comment: 30 pages, 6 figure
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