15,475 research outputs found
The Adverse Impact of Temperature on Income
Crop Production/Industries, Risk and Uncertainty,
Multiuser Precoding and Channel Estimation for Hybrid Millimeter Wave MIMO Systems
In this paper, we develop a low-complexity channel estimation for hybrid
millimeter wave (mmWave) systems, where the number of radio frequency (RF)
chains is much less than the number of antennas equipped at each transceiver.
The proposed channel estimation algorithm aims to estimate the strongest
angle-of-arrivals (AoAs) at both the base station (BS) and the users. Then all
the users transmit orthogonal pilot symbols to the BS via these estimated
strongest AoAs to facilitate the channel estimation. The algorithm does not
require any explicit channel state information (CSI) feedback from the users
and the associated signalling overhead of the algorithm is only proportional to
the number of users, which is significantly less compared to various existing
schemes. Besides, the proposed algorithm is applicable to both non-sparse and
sparse mmWave channel environments. Based on the estimated CSI, zero-forcing
(ZF) precoding is adopted for multiuser downlink transmission. In addition, we
derive a tight achievable rate upper bound of the system. Our analytical and
simulation results show that the proposed scheme offer a considerable
achievable rate gain compared to fully digital systems, where the number of RF
chains equipped at each transceiver is equal to the number of antennas.
Furthermore, the achievable rate performance gap between the considered hybrid
mmWave systems and the fully digital system is characterized, which provides
useful system design insights.Comment: 6 pages, accepted for presentation, ICC 201
A Composite Likelihood-based Approach for Change-point Detection in Spatio-temporal Process
This paper develops a unified, accurate and computationally efficient method
for change-point inference in non-stationary spatio-temporal processes. By
modeling a non-stationary spatio-temporal process as a piecewise stationary
spatio-temporal process, we consider simultaneous estimation of the number and
locations of change-points, and model parameters in each segment. A composite
likelihood-based criterion is developed for change-point and parameters
estimation. Asymptotic theories including consistency and distribution of the
estimators are derived under mild conditions. In contrast to classical results
in fixed dimensional time series that the asymptotic error of change-point
estimator is , exact recovery of true change-points is guaranteed in
the spatio-temporal setting. More surprisingly, the consistency of change-point
estimation can be achieved without any penalty term in the criterion function.
A computational efficient pruned dynamic programming algorithm is developed for
the challenging criterion optimization problem. Simulation studies and an
application to U.S. precipitation data are provided to demonstrate the
effectiveness and practicality of the proposed method
Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections
In this paper, we propose a novel low-tubal-rank tensor recovery model, which
directly constrains the tubal rank prior for effectively removing the mixed
Gaussian and sparse noise in hyperspectral images. The constraints of
tubal-rank and sparsity can govern the solution of the denoised tensor in the
recovery procedure. To solve the constrained low-tubal-rank model, we develop
an iterative algorithm based on bilateral random projections to efficiently
solve the proposed model. The advantage of random projections is that the
approximation of the low-tubal-rank tensor can be obtained quite accurately in
an inexpensive manner. Experimental examples for hyperspectral image denoising
are presented to demonstrate the effectiveness and efficiency of the proposed
method.Comment: Accepted by IGARSS 201
Income and temperatures: Working paper series--10-06
The contemporaneous relationship between temperature and income is important because it enables economists to estimate the economic impact of global warming without assuming a structural model. Until recently, empirical evidence generally suggests that there is a negative relationship between temperature and income, and therefore global warming has an adverse impact on economic activity. However, recently Nordhaus (2006) finds that the temperature-income relationship depends on how income is measured. We show in this paper that the results of Nordhaus (2006) may be due to a model misspecification or an omitted-variable problem. Based on a well-motivated temperature-income model, we find that the relationship between temperature and income is not dependent on income measurement. Our regression results show that the adverse impact of an increase of 3 degrees Celsius in temperature can be as much as a 9% decrease in income for developed nations such as the United States and the United Kingdom. Therefore, our results suggest more aggressive climate mitigation policy
Hybrid Transceiver Optimization for Multi-Hop Communications
Multi-hop communication with the aid of large-scale antenna arrays will play
a vital role in future emergence communication systems. In this paper, we
investigate amplify-and-forward based and multiple-input multiple-output
assisted multi-hop communication, in which all nodes employ hybrid
transceivers. Moreover, channel errors are taken into account in our hybrid
transceiver design. Based on the matrix-monotonic optimization framework, the
optimal structures of the robust hybrid transceivers are derived. By utilizing
these optimal structures, the optimizations of analog transceivers and digital
transceivers can be separated without loss of optimality. This fact greatly
simplifies the joint optimization of analog and digital transceivers. Since the
optimization of analog transceivers under unit-modulus constraints is
non-convex, a projection type algorithm is proposed for analog transceiver
optimization to overcome this difficulty. Based on the derived analog
transceivers, the optimal digital transceivers can then be derived using
matrix-monotonic optimization. Numeral results obtained demonstrate the
performance advantages of the proposed hybrid transceiver designs over other
existing solutions.Comment: 32 pages, 6 figures. This manuscript has been submitted to IEEE
Journal on Selected Areas in Communications (special issue on Multiple
Antenna Technologies for Beyond 5G
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