4,258 research outputs found
The Distributed MIMO Scenario: Can Ideal ADCs Be Replaced by Low-resolution ADCs?
This letter considers the architecture of distributed antenna system, which
is made up of a massive number of single-antenna remote radio heads (RRHs),
some with full-resolution but others with low-resolution analog-to-digital
converter (ADC) receivers. This architecture is greatly motivated by its high
energy efficiency and low-cost implementation. We derive the worst-case uplink
spectral efficiency (SE) of the system assuming a frequency-flat channel and
maximum-ratio combining (MRC), and reveal that the SE increases as the number
of quantization bits for the low-resolution ADCs increases, and the SE
converges as the number of RRHs with low-resolution ADCs grows. Our results
furthermore demonstrate that a great improvement can be obtained by adding a
majority of RRHs with low-resolution ADC receivers, if sufficient quantization
precision and an acceptable proportion of high-to-low resolution RRHs are used.Comment: 4 pages, to be published in IEEE Wireless Communications Letter
Sum Rate and Fairness Analysis for the MU-MIMO Downlink under PSK Signalling: Interference Suppression vs Exploitation
In this paper, we analyze the sum rate performance of multi-user
multiple-input multiple-output (MU-MIMO) systems, with a finite constellation
phase-shift keying (PSK) input alphabet. We analytically calculate and compare
the achievable sum rate in three downlink transmission scenarios: 1) without
precoding, 2) with zero forcing (ZF) precoding 3) with closed form constructive
interference (CI) precoding technique. In light of this, new analytical
expressions for the average sum rate are derived in the three cases, and Monte
Carlo simulations are provided throughout to validate the analysis.
Furthermore, based on the derived expressions, a power allocation scheme that
can ensure fairness among the users is also proposed. The results in this work
demonstrate that, the CI strictly outperforms the other two schemes, and the
performance gap between the considered schemes increases with increase in the
MIMO size. In addition, the CI provides higher fairness and the power
allocation algorithm proposed in this paper can achieve maximum fairness index
Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs
This paper considers a multiple-input multiple-output (MIMO) receiver with
very low-precision analog-to-digital convertors (ADCs) with the goal of
developing massive MIMO antenna systems that require minimal cost and power.
Previous studies demonstrated that the training duration should be {\em
relatively long} to obtain acceptable channel state information. To address
this requirement, we adopt a joint channel-and-data (JCD) estimation method
based on Bayes-optimal inference. This method yields minimal mean square errors
with respect to the channels and payload data. We develop a Bayes-optimal JCD
estimator using a recent technique based on approximate message passing. We
then present an analytical framework to study the theoretical performance of
the estimator in the large-system limit. Simulation results confirm our
analytical results, which allow the efficient evaluation of the performance of
quantized massive MIMO systems and provide insights into effective system
design.Comment: accepted in IEEE Transactions on Signal Processin
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