9,275 research outputs found
Bayesian Optimal Data Detector for mmWave OFDM System with Low-Resolution ADC
Orthogonal frequency division multiplexing (OFDM) has been widely used in
communication systems operating in the millimeter wave (mmWave) band to combat
frequency-selective fading and achieve multi-Gbps transmissions, such as IEEE
802.15.3c and IEEE 802.11ad. For mmWave systems with ultra high sampling rate
requirements, the use of low-resolution analog-to-digital converters (ADCs)
(i.e., 1-3 bits) ensures an acceptable level of power consumption and system
costs. However, orthogonality among sub-channels in the OFDM system cannot be
maintained because of the severe non-linearity caused by low-resolution ADC,
which renders the design of data detector challenging. In this study, we
develop an efficient algorithm for optimal data detection in the mmWave OFDM
system with low-resolution ADCs. The analytical performance of the proposed
detector is derived and verified to achieve the fundamental limit of the
Bayesian optimal design. On the basis of the derived analytical expression, we
further propose a power allocation (PA) scheme that seeks to minimize the
average symbol error rate. In addition to the optimal data detector, we also
develop a feasible channel estimation method, which can provide high-quality
channel state information without significant pilot overhead. Simulation
results confirm the accuracy of our analysis and illustrate that the
performance of the proposed detector in conjunction with the proposed PA scheme
is close to the optimal performance of the OFDM system with infinite-resolution
ADC.Comment: 32 pages, 12 figures; accepted by IEEE JSAC special issue on
millimeter wave communications for future mobile network
Truly Proximal Policy Optimization
Proximal policy optimization (PPO) is one of the most successful deep
reinforcement-learning methods, achieving state-of-the-art performance across a
wide range of challenging tasks. However, its optimization behavior is still
far from being fully understood. In this paper, we show that PPO could neither
strictly restrict the likelihood ratio as it attempts to do nor enforce a
well-defined trust region constraint, which means that it may still suffer from
the risk of performance instability. To address this issue, we present an
enhanced PPO method, named Truly PPO. Two critical improvements are made in our
method: 1) it adopts a new clipping function to support a rollback behavior to
restrict the difference between the new policy and the old one; 2) the
triggering condition for clipping is replaced with a trust region-based one,
such that optimizing the resulted surrogate objective function provides
guaranteed monotonic improvement of the ultimate policy performance. It seems,
by adhering more truly to making the algorithm proximal - confining the policy
within the trust region, the new algorithm improves the original PPO on both
sample efficiency and performance
Comment on "Fault-Tolerate Quantum Private Comparison Based on GHZ States and ECC"
A two-party quantum private comparison scheme using GHZ states and
error-correcting code (ECC) was introduced in Li et al.'s paper [Int. J. Theor.
Phys. 52: 2818-2815, 2013], which holds the capability of fault-tolerate and
could be performed in a none-ideal scenario. However, this study points out
there exists a fatal loophole under a special attack, namely the
twice-Hadamard-CNOT attack. A malicious party may intercept the other's
particles, firstly executes the Hadamard operations on these intercepted
particles and his (her) own ones respectively, and then sequentially performs
twice CNOT operations on them and the auxiliary particles prepared in advance.
As a result, the secret input will be revealed without being detected through
measuring the auxiliary particles. For resisting this special attack, an
improvement is proposed by applying a permutation operator before TP sends the
particle sequences to all the participants.Comment: 8 pages, 1 figur
Reliable OFDM Receiver with Ultra-Low Resolution ADC
The use of low-resolution analog-to-digital converters (ADCs) can
significantly reduce power consumption and hardware cost. However, their
resulting severe nonlinear distortion makes reliable data transmission
challenging. For orthogonal frequency division multiplexing (OFDM)
transmission, the orthogonality among subcarriers is destroyed. This
invalidates conventional OFDM receivers relying heavily on this orthogonality.
In this study, we move on to quantized OFDM (Q-OFDM) prototyping implementation
based on our previous achievement in optimal Q-OFDM detection. First, we
propose a novel Q-OFDM channel estimator by extending the generalized Turbo
(GTurbo) framework formerly applied for optimal detection. Specifically, we
integrate a type of robust linear OFDM channel estimator into the original
GTurbo framework and derive its corresponding extrinsic information to
guarantee its convergence. We also propose feasible schemes for automatic gain
control, noise power estimation, and synchronization. Combined with the
proposed inference algorithms, we develop an efficient Q-OFDM receiver
architecture. Furthermore, we construct a proof-of-concept prototyping system
and conduct over-the-air (OTA) experiments to examine its feasibility and
reliability. This is the first work that focuses on both algorithm design and
system implementation in the field of low-resolution quantization
communication. The results of the numerical simulation and OTA experiment
demonstrate that reliable data transmission can be achieved.Comment: 14 pages, 17 figures; accepted by IEEE Transactions on Communication
Landau-Zener-St\"uckelberg Interferometry for Majorana Qubit
Stimulated by a very recent experiment observing successfully two
superconducting states with even- and odd-number of electrons in a nanowire
topological superconductor as expected from the existence of two end Majorana
quasiparticles (MQs) [Albrecht \textit{et al.}, Nature \textbf{531}, 206
(2016)], we propose a way to manipulate Majorana qubit exploiting quantum
tunneling effects. The prototype setup consists of two one-dimensional (1D)
topological superconductors coupled by a tunneling junction which can be
controlled by gate voltage. We show that, upon current injection, the time
evolution of superconducting phase difference at the junction induces an
oscillation in energy levels of the Majorana parity states, whereas the
level-crossing is avoided by a small coupling energy of MQs in the individual
1D superconductors. This results in a Landau-Zener-St\"{u}ckelberg (LZS)
interference between the Majorana parity states. Adjusting the current pulse
and gate voltage, one can build a LZS interferometry which provides an
arbitrary manipulation of the Majorana qubit. The LZS rotation of Majorana
qubit can be monitored by the microwave radiated from the junction
Modeling and Analysis of SLED
SLED is a crucial component for C-band microwave acceleration unit of SXFEL.
To study the behavior of SLED (SLAC Energy Doubler), mathematic model is
commonly built and analyzed. In this paper, a new method is proposed to build
the model of SLED at SINAP. With this method, the parameters of the two
cavities can be analyzed separately. Also it is suitable to study parameter
optimization of SLED and analyze the effect from the parameters variations.
Simulation results of our method are also presented.Comment: 7 pages,10 figure
Predictability of road traffic and congestion in urban areas
Mitigating traffic congestion on urban roads, with paramount importance in
urban development and reduction of energy consumption and air pollution,
depends on our ability to foresee road usage and traffic conditions pertaining
to the collective behavior of drivers, raising a significant question: to what
degree is road traffic predictable in urban areas? Here we rely on the precise
records of daily vehicle mobility based on GPS positioning device installed in
taxis to uncover the potential daily predictability of urban traffic patterns.
Using the mapping from the degree of congestion on roads into a time series of
symbols and measuring its entropy, we find a relatively high daily
predictability of traffic conditions despite the absence of any a priori
knowledge of drivers' origins and destinations and quite different travel
patterns between weekdays and weekends. Moreover, we find a counterintuitive
dependence of the predictability on travel speed: the road segment associated
with intermediate average travel speed is most difficult to be predicted. We
also explore the possibility of recovering the traffic condition of an
inaccessible segment from its adjacent segments with respect to limited
observability. The highly predictable traffic patterns in spite of the
heterogeneity of drivers' behaviors and the variability of their origins and
destinations enables development of accurate predictive models for eventually
devising practical strategies to mitigate urban road congestion
Manipulating the Majorana Qubit with the Landau-Zener-St\"{u}ckelberg Interference
Constructing a universal operation scheme for Majorana qubits remains a
central issue for the topological quantum computation. We study the
Landau-Zener-St\"{u}ckelberg interference in a Majorana qubit and show that
this interference can be used to achieve controllable operations. The Majorana
qubit consists of an rf SQUID with a topological nanowire Josephson junction
which hosts Majorana bound states. In the SQUID, a magnetic flux pulse can
drive the quantum evolution of the Majorana qubit. The qubit experiences two
Landau-Zener transitions when the amplitude of the pulse is tuned around the
superconducting flux quanta . The Landau-Zener-St\"{u}ckelberg
interference between the two transitions rotates the Majorana qubit, with the
angle controlled by the time scale of the pulse. This rotation operation
implements a high-speed single-qubit gate on the Majorana qubit, which is a
necessary ingredient for the topological quantum computation
Finite-Alphabet Precoding for Massive MU-MIMO with Low-resolution DACs
Massive multiuser multiple-input multiple-output (MU-MIMO) systems are
expected to be the core technology in fifth-generation wireless systems because
they significantly improve spectral efficiency. However, the requirement for a
large number of radio frequency (RF) chains results in high hardware costs and
power consumption, which obstruct the commercial deployment of massive MIMO
systems. A potential solution is to use low-resolution digital-to-analog
converters (DAC)/analog-to-digital converters for each antenna and RF chain.
However, using low-resolution DACs at the transmit side directly limits the
degree of freedom of output signals and thus poses a challenge to the precoding
design. In this study, we develop efficient and universal algorithms for a
downlink massive MU-MIMO system with finite-alphabet precodings. Our algorithms
are developed based on the alternating direction method of multipliers (ADMM)
framework. The original ADMM does not converge in a nonlinear discrete
optimization problem. The primary cause of this problem is that the alternating
(update) directions in ADMM on one side are biased, and those on the other side
are unbiased. By making the two updates consistent in an unbiased manner, we
develop two algorithms called iterative discrete estimation (IDE) and IDE2: IDE
demonstrates excellent performance and IDE2 possesses a significantly low
computational complexity. Compared with state-of-the-art techniques, the
proposed precoding algorithms present significant advantages in performance and
computational complexity.Comment: 15 pages, 11 figures, 3 tables; accepted by IEEE Transactions on
Wireless Communication
Quantum algorithm for association rules mining
Association rules mining (ARM) is one of the most important problems in
knowledge discovery and data mining. Given a transaction database that has a
large number of transactions and items, the task of ARM is to acquire
consumption habits of customers by discovering the relationships between
itemsets (sets of items). In this paper, we address ARM in the quantum settings
and propose a quantum algorithm for the key part of ARM, finding out frequent
itemsets from the candidate itemsets and acquiring their supports.
Specifically, for the case in which there are frequent -itemsets
in the candidate -itemsets (), our
algorithm can efficiently mine these frequent -itemsets and estimate their
supports by using parallel amplitude estimation and amplitude amplification
with complexity ,
where is the error for estimating the supports. Compared with the
classical counterpart, classical sampling-based algorithm, whose complexity is
, our quantum algorithm
quadratically improves the dependence on both and in the
best case when and on alone in the worst
case when .Comment: 8 page
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