1,656 research outputs found
Multidimensional Index Modulation in Wireless Communications
In index modulation schemes, information bits are conveyed through indexing
of transmission entities such as antennas, subcarriers, times slots, precoders,
subarrays, and radio frequency (RF) mirrors. Index modulation schemes are
attractive for their advantages such as good performance, high rates, and
hardware simplicity. This paper focuses on index modulation schemes in which
multiple transmission entities, namely, {\em antennas}, {\em time slots}, and
{\em RF mirrors}, are indexed {\em simultaneously}. Recognizing that such
multidimensional index modulation schemes encourage sparsity in their transmit
signal vectors, we propose efficient signal detection schemes that use
compressive sensing based reconstruction algorithms. Results show that, for a
given rate, improved performance is achieved when the number of indexed
transmission entities is increased. We also explore indexing opportunities in
{\em load modulation}, which is a modulation scheme that offers power
efficiency and reduced RF hardware complexity advantages in multiantenna
systems. Results show that indexing space and time in load modulated
multiantenna systems can achieve improved performance
MMSE precoder for massive MIMO using 1-bit quantization
We propose a novel linear minimum-mean-squared-error (MMSE) precoder design
for a downlink (DL) massive multiple-input-multiple-output (MIMO) scenario. For
economical and computational efficiency reasons low resolution 1-bit
digital-to-analog (DAC) and analog-to-digital (ADC) converters are used. This
comes at the cost of performance gain that can be recovered by the large number
of antennas deployed at the base station (BS) and an appropiate precoder design
to mitigate the distortions due to the coarse quantization. The proposed
precoder takes the quantization non-linearities into account and is split into
a digital precoder and an analog precoder. We formulate the two-stage precoding
problem such that the MSE of the users is minimized under the 1-bit constraint.
In the simulations, we compare the new optimized precoding scheme with
previously proposed linear precoders in terms of uncoded bit error ratio (BER).Comment: Presented in ICASSP 2016, 20-25 March 2016, Shanghai, Chin
MMSE precoder for massive MIMO using 1-bit quantization
We propose a novel linear minimum-mean-squared-error (MMSE) precoder design
for a downlink (DL) massive multiple-input-multiple-output (MIMO) scenario. For
economical and computational efficiency reasons low resolution 1-bit
digital-to-analog (DAC) and analog-to-digital (ADC) converters are used. This
comes at the cost of performance gain that can be recovered by the large number
of antennas deployed at the base station (BS) and an appropiate precoder design
to mitigate the distortions due to the coarse quantization. The proposed
precoder takes the quantization non-linearities into account and is split into
a digital precoder and an analog precoder. We formulate the two-stage precoding
problem such that the MSE of the users is minimized under the 1-bit constraint.
In the simulations, we compare the new optimized precoding scheme with
previously proposed linear precoders in terms of uncoded bit error ratio (BER).Comment: Presented in ICASSP 2016, 20-25 March 2016, Shanghai, Chin
Gaussian Message Passing for Overloaded Massive MIMO-NOMA
This paper considers a low-complexity Gaussian Message Passing (GMP) scheme
for a coded massive Multiple-Input Multiple-Output (MIMO) systems with
Non-Orthogonal Multiple Access (massive MIMO-NOMA), in which a base station
with antennas serves sources simultaneously in the same frequency.
Both and are large numbers, and we consider the overloaded cases
with . The GMP for MIMO-NOMA is a message passing algorithm operating
on a fully-connected loopy factor graph, which is well understood to fail to
converge due to the correlation problem. In this paper, we utilize the
large-scale property of the system to simplify the convergence analysis of the
GMP under the overloaded condition. First, we prove that the \emph{variances}
of the GMP definitely converge to the mean square error (MSE) of Linear Minimum
Mean Square Error (LMMSE) multi-user detection. Secondly, the \emph{means} of
the traditional GMP will fail to converge when . Therefore, we propose and derive a new
convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the
LMMSE multi-user detection performance for any , and show that it
has a faster convergence speed than the traditional GMP with the same
complexity. Finally, numerical results are provided to verify the validity and
accuracy of the theoretical results presented.Comment: Accepted by IEEE TWC, 16 pages, 11 figure
On the Impact of Hardware Impairments on Massive MIMO
Massive multi-user (MU) multiple-input multiple-output (MIMO) systems are one
possible key technology for next generation wireless communication systems.
Claims have been made that massive MU-MIMO will increase both the radiated
energy efficiency as well as the sum-rate capacity by orders of magnitude,
because of the high transmit directivity. However, due to the very large number
of transceivers needed at each base-station (BS), a successful implementation
of massive MU-MIMO will be contingent on of the availability of very cheap,
compact and power-efficient radio and digital-processing hardware. This may in
turn impair the quality of the modulated radio frequency (RF) signal due to an
increased amount of power-amplifier distortion, phase-noise, and quantization
noise.
In this paper, we examine the effects of hardware impairments on a massive
MU-MIMO single-cell system by means of theory and simulation. The simulations
are performed using simplified, well-established statistical hardware
impairment models as well as more sophisticated and realistic models based upon
measurements and electromagnetic antenna array simulations.Comment: 7 pages, 9 figures, Accepted for presentation at Globe-Com workshop
on Massive MIM
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