2,119 research outputs found
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
One-Bit Massive MIMO: Channel Estimation and High-Order Modulations
We investigate the information-theoretic throughout achievable on a fading
communication link when the receiver is equipped with one-bit analog-to-digital
converters (ADCs). The analysis is conducted for the setting where neither the
transmitter nor the receiver have a priori information on the realization of
the fading channels. This means that channel-state information needs to be
acquired at the receiver on the basis of the one-bit quantized channel outputs.
We show that least-squares (LS) channel estimation combined with joint pilot
and data processing is capacity achieving in the single-user,
single-receive-antenna case.
We also investigate the achievable uplink throughput in a massive
multiple-input multiple-output system where each element of the antenna array
at the receiver base-station feeds a one-bit ADC. We show that LS channel
estimation and maximum-ratio combining are sufficient to support both multiuser
operation and the use of high-order constellations. This holds in spite of the
severe nonlinearity introduced by the one-bit ADCs
Performance Analysis for Time-of-Arrival Estimation with Oversampled Low-Complexity 1-bit A/D Conversion
Analog-to-digtial (A/D) conversion plays a crucial role when it comes to the
design of energy-efficient and fast signal processing systems. As its
complexity grows exponentially with the number of output bits, significant
savings are possible when resorting to a minimum resolution of a single bit.
However, then the nonlinear effect which is introduced by the A/D converter
results in a pronounced performance loss, in particular for the case when the
receiver is operated outside the low signal-to-noise ratio (SNR) regime. By
trading the A/D resolution for a moderately faster sampling rate, we show that
for time-of-arrival (TOA) estimation under any SNR level it is possible to
obtain a low-complexity -bit receive system which features a smaller
performance degradation then the classical low SNR hard-limiting loss of
( dB). Key to this result is the employment of a lower bound for
the Fisher information matrix which enables us to approximate the estimation
performance for coarsely quantized receivers with correlated noise models in a
pessimistic way
Performance Analysis for Time-of-Arrival Estimation with Oversampled Low-Complexity 1-bit A/D Conversion
Analog-to-digtial (A/D) conversion plays a crucial role when it comes to the
design of energy-efficient and fast signal processing systems. As its
complexity grows exponentially with the number of output bits, significant
savings are possible when resorting to a minimum resolution of a single bit.
However, then the nonlinear effect which is introduced by the A/D converter
results in a pronounced performance loss, in particular for the case when the
receiver is operated outside the low signal-to-noise ratio (SNR) regime. By
trading the A/D resolution for a moderately faster sampling rate, we show that
for time-of-arrival (TOA) estimation under any SNR level it is possible to
obtain a low-complexity -bit receive system which features a smaller
performance degradation then the classical low SNR hard-limiting loss of
( dB). Key to this result is the employment of a lower bound for
the Fisher information matrix which enables us to approximate the estimation
performance for coarsely quantized receivers with correlated noise models in a
pessimistic way
Joint Channel-and-Data Estimation for Large-MIMO Systems with Low-Precision ADCs
The use of low precision (e.g., 1-3 bits) analog-to-digital convenors (ADCs)
in very large multiple-input multiple-output (MIMO) systems is a technique to
reduce cost and power consumption. In this context, nevertheless, it has been
shown that the training duration is required to be {\em very large} just to
obtain an acceptable channel state information (CSI) at the receiver. A
possible solution to the quantized MIMO systems is joint channel-and-data (JCD)
estimation. This paper first develops an analytical framework for studying the
quantized MIMO system using JCD estimation. In particular, we use the
Bayes-optimal inference for the JCD estimation and realize this estimator
utilizing a recent technique based on approximate message passing. Large-system
analysis based on the replica method is then adopted to derive the asymptotic
performances of the JCD estimator. Results from simulations confirm our
theoretical findings and reveal that the JCD estimator can provide a
significant gain over conventional pilot-only schemes in the quantized MIMO
system.Comment: 7 pages, 4 figure
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