8 research outputs found
Capacity Bounds for Communication Systems with Quantization and Spectral Constraints
Low-resolution digital-to-analog and analog-to-digital converters (DACs and
ADCs) have attracted considerable attention in efforts to reduce power
consumption in millimeter wave (mmWave) and massive MIMO systems. This paper
presents an information-theoretic analysis with capacity bounds for classes of
linear transceivers with quantization. The transmitter modulates symbols via a
unitary transform followed by a DAC and the receiver employs an ADC followed by
the inverse unitary transform. If the unitary transform is set to an FFT
matrix, the model naturally captures filtering and spectral constraints which
are essential to model in any practical transceiver. In particular, this model
allows studying the impact of quantization on out-of-band emission constraints.
In the limit of a large random unitary transform, it is shown that the effect
of quantization can be precisely described via an additive Gaussian noise
model. This model in turn leads to simple and intuitive expressions for the
power spectrum of the transmitted signal and a lower bound to the capacity with
quantization. Comparison with non-quantized capacity and a capacity upper bound
that does not make linearity assumptions suggests that while low resolution
quantization has minimal impact on the achievable rate at typical parameters in
5G systems today, satisfying out-of-band emissions are potentially much more of
a challenge.Comment: Appears in the Proceedings of IEEE International Symposium on
Information Theory (ISIT) 202
Capacity Bounds for One-Bit MIMO Gaussian Channels with Analog Combining
The use of 1-bit analog-to-digital converters (ADCs) is seen as a promising
approach to significantly reduce the power consumption and hardware cost of
multiple-input multiple-output (MIMO) receivers. However, the nonlinear
distortion due to 1-bit quantization fundamentally changes the optimal
communication strategy and also imposes a capacity penalty to the system. In
this paper, the capacity of a Gaussian MIMO channel in which the antenna
outputs are processed by an analog linear combiner and then quantized by a set
of zero threshold ADCs is studied. A new capacity upper bound for the zero
threshold case is established that is tighter than the bounds available in the
literature. In addition, we propose an achievability scheme which configures
the analog combiner to create parallel Gaussian channels with phase
quantization at the output. Under this class of analog combiners, an algorithm
is presented that identifies the analog combiner and input distribution that
maximize the achievable rate. Numerical results are provided showing that the
rate of the achievability scheme is tight in the low signal-to-noise ratio
(SNR) regime. Finally, a new 1-bit MIMO receiver architecture which employs
analog temporal and spatial processing is proposed. The proposed receiver
attains the capacity in the high SNR regime.Comment: 30 pages, 9 figures, Submitted to IEEE Transactions on Communication
On MIMO Channel Capacity with Output Quantization Constraints
The capacity of a Multiple-Input Multiple-Output (MIMO) channel in which the antenna outputs are processed by an analog linear combining network and quantized by a set of threshold quantizers is studied. The linear combining weights and quantization thresholds are selected from a set of possible configurations as a function of the channel matrix. The possible configurations of the combining network model specific analog receiver architectures, such as single antenna selection, sign quantization of the antenna outputs or linear processing of the outputs. An interesting connection between the capacity of this channel and a constrained sphere packing problem in which unit spheres are packed in a hyperplane arrangement is shown. From a high-level perspective, this follows from the fact that each threshold quantizer can be viewed as a hyperplane partitioning the transmitter signal space. Accordingly, the output of the set of quantizers corresponds to the possible regions induced by the hyperplane arrangement corresponding to the channel realization and receiver configuration. This connection provides a number of important insights into the design of quantization architectures for MIMO receivers; for instance, it shows that for a given number of quantizers, choosing configurations which induce a larger number of partitions can lead to higher rates