8,069 research outputs found
Waveforms for the Massive MIMO Downlink: Amplifier Efficiency, Distortion and Performance
In massive MIMO, most precoders result in downlink signals that suffer from
high PAR, independently of modulation order and whether single-carrier or OFDM
transmission is used. The high PAR lowers the power efficiency of the base
station amplifiers. To increase power efficiency, low-PAR precoders have been
proposed. In this article, we compare different transmission schemes for
massive MIMO in terms of the power consumed by the amplifiers. It is found that
(i) OFDM and single-carrier transmission have the same performance over a
hardened massive MIMO channel and (ii) when the higher amplifier power
efficiency of low-PAR precoding is taken into account, conventional and low-PAR
precoders lead to approximately the same power consumption. Since downlink
signals with low PAR allow for simpler and cheaper hardware, than signals with
high PAR, therefore, the results suggest that low-PAR precoding with either
single-carrier or OFDM transmission should be used in a massive MIMO base
station
Spatial Characteristics of Distortion Radiated from Antenna Arrays with Transceiver Nonlinearities
The distortion from massive MIMO (multiple-input--multiple-output) base
stations with nonlinear amplifiers is studied and its radiation pattern is
derived. The distortion is analyzed both in-band and out-of-band. By using an
orthogonal Hermite representation of the amplified signal, the spatial
cross-correlation matrix of the nonlinear distortion is obtained. It shows
that, if the input signal to the amplifiers has a dominant beam, the distortion
is beamformed in the same way as that beam. When there are multiple beams
without any one being dominant, it is shown that the distortion is practically
isotropic. The derived theory is useful to predict how the nonlinear distortion
will behave, to analyze the out-of-band radiation, to do reciprocity
calibration, and to schedule users in the frequency plane to minimize the
effect of in-band distortion
Development of a dc-ac power conditioner for wind generator by using neural network
This project present of development single phase DC-AC converter for wind
generator application. The mathematical model of the wind generator and Artificial
Neural Network control for DC-AC converter is derived. The controller is designed to
stabilize the output voltage of DC-AC converter. To verify the effectiveness of the
proposal controller, both simulation and experimental are developed. The simulation and
experimental result show that the amplitude of output voltage of the DC-AC converter
can be controlled
Adaptive Nonlinear RF Cancellation for Improved Isolation in Simultaneous Transmit-Receive Systems
This paper proposes an active radio frequency (RF) cancellation solution to
suppress the transmitter (TX) passband leakage signal in radio transceivers
supporting simultaneous transmission and reception. The proposed technique is
based on creating an opposite-phase baseband equivalent replica of the TX
leakage signal in the transceiver digital front-end through adaptive nonlinear
filtering of the known transmit data, to facilitate highly accurate
cancellation under a nonlinear TX power amplifier (PA). The active RF
cancellation is then accomplished by employing an auxiliary transmitter chain,
to generate the actual RF cancellation signal, and combining it with the
received signal at the receiver (RX) low noise amplifier (LNA) input. A
closed-loop parameter learning approach, based on the decorrelation principle,
is also developed to efficiently estimate the coefficients of the nonlinear
cancellation filter in the presence of a nonlinear TX PA with memory, finite
passive isolation, and a nonlinear RX LNA. The performance of the proposed
cancellation technique is evaluated through comprehensive RF measurements
adopting commercial LTE-Advanced transceiver hardware components. The results
show that the proposed technique can provide an additional suppression of up to
54 dB for the TX passband leakage signal at the RX LNA input, even at
considerably high transmit power levels and with wide transmission bandwidths.
Such novel cancellation solution can therefore substantially improve the TX-RX
isolation, hence reducing the requirements on passive isolation and RF
component linearity, as well as increasing the efficiency and flexibility of
the RF spectrum use in the emerging 5G radio networks.Comment: accepted to IEE
Low-Complexity Sub-band Digital Predistortion for Spurious Emission Suppression in Noncontiguous Spectrum Access
Noncontiguous transmission schemes combined with high power-efficiency
requirements pose big challenges for radio transmitter and power amplifier (PA)
design and implementation. Due to the nonlinear nature of the PA, severe
unwanted emissions can occur, which can potentially interfere with neighboring
channel signals or even desensitize the own receiver in frequency division
duplexing (FDD) transceivers. In this article, to suppress such unwanted
emissions, a low-complexity sub-band DPD solution, specifically tailored for
spectrally noncontiguous transmission schemes in low-cost devices, is proposed.
The proposed technique aims at mitigating only the selected spurious
intermodulation distortion components at the PA output, hence allowing for
substantially reduced processing complexity compared to classical linearization
solutions. Furthermore, novel decorrelation based parameter learning solutions
are also proposed and formulated, which offer reduced computing complexity in
parameter estimation as well as the ability to track time-varying features
adaptively. Comprehensive simulation and RF measurement results are provided,
using a commercial LTE-Advanced mobile PA, to evaluate and validate the
effectiveness of the proposed solution in real world scenarios. The obtained
results demonstrate that highly efficient spurious component suppression can be
obtained using the proposed solutions
Finding Structural Information of RF Power Amplifiers using an Orthogonal Non-Parametric Kernel Smoothing Estimator
A non-parametric technique for modeling the behavior of power amplifiers is
presented. The proposed technique relies on the principles of density
estimation using the kernel method and is suited for use in power amplifier
modeling. The proposed methodology transforms the input domain into an
orthogonal memory domain. In this domain, non-parametric static functions are
discovered using the kernel estimator. These orthogonal, non-parametric
functions can be fitted with any desired mathematical structure, thus
facilitating its implementation. Furthermore, due to the orthogonality, the
non-parametric functions can be analyzed and discarded individually, which
simplifies pruning basis functions and provides a tradeoff between complexity
and performance. The results show that the methodology can be employed to model
power amplifiers, therein yielding error performance similar to
state-of-the-art parametric models. Furthermore, a parameter-efficient model
structure with 6 coefficients was derived for a Doherty power amplifier,
therein significantly reducing the deployment's computational complexity.
Finally, the methodology can also be well exploited in digital linearization
techniques.Comment: Matlab sample code (15 MB):
https://dl.dropboxusercontent.com/u/106958743/SampleMatlabKernel.zi
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