3,345 research outputs found
1-Bit Massive MIMO Downlink Based on Constructive Interference
In this paper, we focus on the multiuser massive multiple-input single-output
(MISO) downlink with low-cost 1-bit digital-to-analog converters (DACs) for PSK
modulation, and propose a low-complexity refinement process that is applicable
to any existing 1-bit precoding approaches based on the constructive
interference (CI) formulation. With the decomposition of the signals along the
detection thresholds, we first formulate a simple symbol-scaling method as the
performance metric. The low-complexity refinement approach is subsequently
introduced, where we aim to improve the introduced symbol-scaling performance
metric by modifying the transmit signal on one antenna at a time. Numerical
results validate the effectiveness of the proposed refinement method on
existing approaches for massive MIMO with 1-bit DACs, and the performance
improvements are most significant for the low-complexity quantized zero-forcing
(ZF) method.Comment: 5 pages, EUSIPCO 201
Massive MIMO 1-Bit DAC Transmission: A Low-Complexity Symbol Scaling Approach
We study multi-user massive multiple-input single-output (MISO) systems and
focus on downlink transmission, where the base station (BS) employs a large
antenna array with low-cost 1-bit digital-to-analog converters (DACs). The
direct combination of existing beamforming schemes with 1-bit DACs is shown to
lead to an error floor at medium-to-high SNR regime, due to the coarse
quantization of the DACs with limited precision. In this paper, based on the
constructive interference we consider both a quantized linear beamforming
scheme where we analytically obtain the optimal beamforming matrix, and a
non-linear mapping scheme where we directly design the transmit signal vector.
Due to the 1-bit quantization, the formulated optimization for the non-linear
mapping scheme is shown to be non-convex. To solve this problem, the non-convex
constraints of the 1-bit DACs are firstly relaxed, followed by an element-wise
normalization to satisfy the 1-bit DAC transmission. We further propose a
low-complexity symbol scaling scheme that consists of three stages, in which
the quantized transmit signal on each antenna element is selected sequentially.
Numerical results show that the proposed symbol scaling scheme achieves a
comparable performance to the optimization-based non-linear mapping approach,
while its corresponding complexity is negligible compared to that of the
non-linear scheme.Comment: 15 page
1-Bit Massive MIMO Transmission: Embracing Interference with Symbol-Level Precoding
The deployment of large-scale antenna arrays for cellular base stations
(BSs), termed as `Massive MIMO', has been a key enabler for meeting the
ever-increasing capacity requirement for 5G communication systems and beyond.
Despite their promising performance, fully-digital massive MIMO systems require
a vast amount of hardware components including radio frequency chains, power
amplifiers, digital-to-analog converters (DACs), etc., resulting in a huge
increase in terms of the total power consumption and hardware costs for
cellular BSs. Towards both spectrally-efficient and energy-efficient massive
MIMO deployment, a number of hardware limited architectures have been proposed,
including hybrid analog-digital structures, constant-envelope transmission, and
use of low-resolution DACs. In this paper, we overview the recent interest in
improving the error-rate performance of massive MIMO systems deployed with
1-bit DACs through precoding at the symbol level. This line of research goes
beyond traditional interference suppression or cancellation techniques by
managing interference on a symbol-by-symbol basis. This provides unique
opportunities for interference-aware precoding tailored for practical massive
MIMO systems. Firstly, we characterize constructive interference (CI) and
elaborate on how CI can benefit the 1-bit signal design by exploiting the
traditionally undesired multi-user interference as well as the interference
from imperfect hardware components. Subsequently, we overview several solutions
for 1-bit signal design to illustrate the gains achievable by exploiting CI.
Finally, we identify some challenges and future research directions for 1-bit
massive MIMO systems that are yet to be explored.Comment: This work has been submitted to the IEEE for possible publication.
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Anisotropic Pauli spin blockade in hole quantum dots
We present measurements on gate-defined double quantum dots in Ge-Si
core-shell nanowires, which we tune to a regime with visible shell filling in
both dots. We observe a Pauli spin blockade and can assign the measured leakage
current at low magnetic fields to spin-flip cotunneling, for which we measure a
strong anisotropy related to an anisotropic g-factor. At higher magnetic fields
we see signatures for leakage current caused by spin-orbit coupling between
(1,1)-singlet and (2,0)-triplet states. Taking into account these anisotropic
spin-flip mechanisms, we can choose the magnetic field direction with the
longest spin lifetime for improved spin-orbit qubits
BaFe2As2 Surface Domains and Domain Walls: Mirroring the Bulk Spin Structure
High-resolution scanning tunneling microscopy (STM) measurements on
BaFe2As2-one of the parent compounds of the iron-based superconductors-reveals
a (1x1) As-terminated unit cell on the (001) surface. However, there are
significant differences of the surface unit cell compared to the bulk: only one
of the two As atoms in the unit cell is imaged and domain walls between
different (1x1) regions display a C2 symmetry at the surface. It should have
been C2v if the STM image reflected the geometric structure of the surface or
the orthorhombic bulk. The inequivalent As atoms and the bias dependence of the
domain walls indicate that the origin of the STM image is primarily electronic
not geometric. We argue that the surface electronic topography mirrors the bulk
spin structure of BaFe2As2, via strong orbital-spin coupling
Quantum Memory: A Missing Piece in Quantum Computing Units
Memory is an indispensable component in classical computing systems. While
the development of quantum computing is still in its early stages, current
quantum processing units mainly function as quantum registers. Consequently,
the actual role of quantum memory in future advanced quantum computing
architectures remains unclear. With the rapid scaling of qubits, it is
opportune to explore the potential and feasibility of quantum memory across
different substrate device technologies and application scenarios. In this
paper, we provide a full design stack view of quantum memory. We start from the
elementary component of a quantum memory device, quantum memory cells. We
provide an abstraction to a quantum memory cell and define metrics to measure
the performance of physical platforms. Combined with addressing functionality,
we then review two types of quantum memory devices: random access quantum
memory (RAQM) and quantum random access memory (QRAM). Building on top of these
devices, quantum memory units in the computing architecture, including building
a quantum memory unit, quantum cache, quantum buffer, and using QRAM for the
quantum input-output module, are discussed. We further propose the programming
model for the quantum memory units and discuss their possible applications. By
presenting this work, we aim to attract more researchers from both the Quantum
Information Science (QIS) and classical memory communities to enter this
emerging and exciting area.Comment: 41 pages, 11 figures, 7 table
Practical Interference Exploitation Precoding without Symbol-by-Symbol Optimization: A Block-Level Approach
In this paper, we propose a constructive interference (CI)-based block-level
precoding (CI-BLP) approach for the downlink of a multi-user multiple-input
single-output (MU-MISO) communication system. Contrary to existing CI precoding
approaches which have to be designed on a symbol-by-symbol level, here a
constant precoding matrix is applied to a block of symbol slots within a
channel coherence interval, thus significantly reducing the computational costs
over traditional CI-based symbol-level precoding (CI-SLP) as the CI-BLP
optimization problem only needs to be solved once per block. For both PSK and
QAM modulation, we formulate an optimization problem to maximize the minimum CI
effect over the block subject to a block- rather than symbol-level power
budget. We mathematically derive the optimal precoding matrix for CI-BLP as a
function of the Lagrange multipliers in closed form. By formulating the dual
problem, the original CI-BLP optimization problem is further shown to be
equivalent to a quadratic programming (QP) optimization. Numerical results
validate our derivations, and show that the proposed CI-BLP scheme achieves
improved performance over the traditional CI-SLP method, thanks to the relaxed
power constraint over the considered block of symbol slots
Block-Level Interference Exploitation Precoding without Symbol-by-Symbol Optimization
Symbol-level precoding (SLP) based on the concept of constructive interference (CI) is shown to be superior to traditional block-level precoding (BLP), however at the cost of a symbol-by-symbol optimization during the precoding design. In this paper, we propose a CI-based block-level precoding (CI-BLP) scheme for the downlink transmission of a multi-user multiple-input single-output (MU-MISO) communication system, where we design a constant precoding matrix to a block of symbol slots to exploit CI for each symbol slot simultaneously. A single optimization problem is formulated to maximize the minimum CI effect over the entire block, thus reducing the computational cost of traditional SLP as the optimization problem only needs to be solved once per block. By leveraging the Karush-Kuhn-Tucker (KKT) conditions and the dual problem formulation, the original optimization problem is finally shown to be equivalent to a quadratic programming (QP) over a simplex. Numerical results validate our derivations and exhibit superior performance for the proposed CI-BLP scheme over traditional BLP and SLP methods, thanks to the relaxed block-level power constraint
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