3 research outputs found
Energy Efficient ADC Bit Allocation and Hybrid Combining for Millimeter Wave MIMO Systems
Low resolution analog-to-digital converters (ADCs) can be employed to improve
the energy efficiency (EE) of a wireless receiver since the power consumption
of each ADC is exponentially related to its sampling resolution and the
hardware complexity. In this paper, we aim to jointly optimize the sampling
resolution, i.e., the number of ADC bits, and analog/digital hybrid combiner
matrices which provides highly energy efficient solutions for millimeter wave
multiple-input multiple output systems. A novel decomposition of the hybrid
combiner to three parts is introduced: the analog combiner matrix, the bit
resolution matrix and the baseband combiner matrix. The unknown matrices are
computed as the solution to a matrix factorization problem where the optimal,
fully digital combiner is approximated by the product of these matrices. An
efficient solution based on the alternating direction method of multipliers is
proposed to solve this problem. The simulation results show that the proposed
solution achieves high EE performance when compared with existing benchmark
techniques that use fixed ADC resolutions
Energy Efficiency Maximization in Millimeter Wave Hybrid MIMO Systems for 5G and Beyond
At millimeter wave (mmWave) frequencies, the higher cost and power
consumption of hardware components in multiple-input multiple output (MIMO)
systems do not allow beamforming entirely at the baseband with a separate radio
frequency (RF) chain for each antenna. In such scenarios, to enable spatial
multiplexing, hybrid beamforming, which uses phase shifters to connect a fewer
number of RF chains to a large number of antennas is a cost effective and
energy-saving alternative. This paper describes our research on fully adaptive
transceivers that adapt their behaviour on a frame-by-frame basis, so that a
mmWave hybrid MIMO system always operates in the most energy efficient manner.
Exhaustive search based brute force approach is computationally intensive, so
we study fractional programming as a low-cost alternative to solve the problem
which maximizes energy efficiency. The performance results indicate that the
resulting mmWave hybrid MIMO transceiver achieves significantly improved energy
efficiency results compared to the baseline cases involving analogue-only or
digital-only signal processing solutions, and shows performance trade-offs with
the brute force approach.Comment: 2020 IEEE International Conference on Communications and Networking
(ComNet
Energy efficient and low complexity techniques for the next generation millimeter wave hybrid MIMO systems
The fifth generation (and beyond) wireless communication systems require increased
capacity, high data rates, improved coverage and reduced energy consumption.
This can be potentially provided by unused available spectrum such
as the Millimeter Wave (MmWave) frequency spectrum above 30 GHz. The high
bandwidths for mmWave communication compared to sub-6 GHz microwave frequency
bands must be traded off against increased path loss, which can be compensated
using large-scale antenna arrays such as the Multiple-Input Multiple-
Output (MIMO) systems. The analog/digital Hybrid Beamforming (HBF) architectures
for mmWave MIMO systems reduce the hardware complexity and power
consumption using fewer Radio Frequency (RF) chains and support multi-stream
communication with high Spectral Efficiency (SE). Such systems can also be
optimized to achieve high Energy Efficiency (EE) gains with low complexity but
this has not been widely studied in the literature. This PhD project focussed on
designing energy efficient and low complexity communication techniques for next
generation mmWave hybrid MIMO systems.
Firstly, a novel architecture with a framework that dynamically activates the
optimal number of RF chains was designed. Fractional programming was used
to solve an EE maximization problem and the Dinkelbach Method (DM) based
framework was exploited to optimize the number of active RF chains and the data
streams. The DM is an iterative and parametric algorithm where a sequence of
easier problems converge to the global solution. The HBF matrices were designed
using a codebook-based fast approximation solution called gradient pursuit which
was introduced as a cost-effective and fast approximation algorithm. This work
maximizes EE by exploiting the structure of RF chains with full resolution
sampling unlike existing baseline approaches that use fixed RF chains and aim
only for high SE.
Secondly, an efficient sparse mmWave channel estimation algorithm was developed
with low resolution Analog-to-Digital Converters (ADCs) at the receiver.
The sparsity of the mmWave channel was exploited and the estimation problem
was tackled using compressed sensing through the Stein's unbiased risk estimate
based parametric denoiser. The Expectation-maximization density estimation
was used to avoid the need to specify the channel statistics. Furthermore, an
energy efficient mmWave hybrid MIMO system was developed with Digital-to-
Analog Converters (DACs) at the transmitter where the best subset of the active
RF chains and the DAC resolution were selected. A novel technique based on the
DM and subset selection optimization was implemented for EE maximization.
This work exploits the low resolution sampling at the converting units and provides
more efficient solutions in terms of EE and channel estimation than existing
baselines in the literature.
Thirdly, the DAC and ADC bit resolutions and the HBF matrices were jointly
optimized for EE maximization. The flexibility in choosing the bit resolution
for each DAC and ADC was considered and they were optimized on a frame-by-frame
basis unlike the existing approaches, based on the fixed resolution sampling.
A novel decomposition of the HBF matrices to three parts was introduced to
represent the analog beamformer matrix, the DAC/ADC bit resolution matrix and
the baseband beamformer matrix. The alternating direction method of multipliers
was used to solve this matrix factorization problem as it has been successfully
applied to other non-convex matrix factorization problems in the literature. This
work considers EE maximization with low resolution sampling at both the DACs
and the ADCs simultaneously, and jointly optimizes the HBF and DAC/ADC bit
resolution matrices, unlike the existing baselines that use fixed bit resolution or
otherwise optimize either DAC/ADC bit resolution or HBF matrices