3 research outputs found
Investigation of Channel Estimation Techniques with 1-bit Quantization and Oversampling for Multiple-Antenna Systems
Large-scale multiple-antenna systems have been identified as a promising
technology for the next generation of wireless systems. However, by scaling up
the number of receive antennas the energy consumption will also increase. One
possible solution is to use low-resolution analog-to-digital converters at the
receiver. This paper considers large-scale multiple-antenna uplink systems with
1-bit analog-to-digital converters on each receive antenna. Since oversampling
can partially compensate for the information loss caused by the coarse
quantization, the received signals are firstly oversampled by a factor M. We
then propose a low-resolution aware linear minimum mean-squared error channel
estimator for 1-bit oversampled systems. Moreover, we characterize analytically
the performance of the proposed channel estimator by deriving an upper bound on
the Bayesian Cram\'er-Rao bound. Numerical results are provided to illustrate
the performance of the proposed channel estimator.Comment:
Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals
In this work, we present an energy-efficient distributed learning framework
using low-resolution ADCs and coarsely quantized signals for Internet of Things
(IoT) networks. In particular, we develop a distributed quantization-aware
least-mean square (DQA-LMS) algorithm that can learn parameters in an
energy-efficient fashion using signals quantized with few bits while requiring
a low computational cost. We also carry out a statistical analysis of the
proposed DQA-LMS algorithm that includes a stability condition. Simulations
assess the DQA-LMS algorithm against existing techniques for a distributed
parameter estimation task where IoT devices operate in a peer-to-peer mode and
demonstrate the effectiveness of the DQA-LMS algorithm.Comment: 5 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:2012.1093
Study of Channel Estimation Algorithms for Large-Scale Multiple-Antenna Systems using 1-Bit ADCs and Oversampling
Large-scale multiple-antenna systems with large bandwidth are fundamental for
future wireless communications, where the base station employs a large antenna
array. In this scenario, one problem faced is the large energy consumption as
the number of receive antennas scales up. Recently, low-resolution
analog-to-digital converters (ADCs) have attracted much attention.
Specifically, 1-bit ADCs are suitable for such systems due to their low cost
and low energy consumption. This paper considers uplink large-scale
multiple-antenna systems with 1-bit ADCs on each receive antenna. We
investigate the benefits of using oversampling for channel estimation in terms
of the mean square error and symbol error rate performance. In particular,
low-resolution aware channel estimators are developed based on the Bussgang
decomposition for 1-bit oversampled systems and analytical bounds on the mean
square error are also investigated. Numerical results are provided to
illustrate the performance of the proposed channel estimation algorithms and
the derived theoretical bounds.Comment: 11 figures, 14 page