546,317 research outputs found
Designing Power-Efficient Modulation Formats for Noncoherent Optical Systems
We optimize modulation formats for the additive white Gaussian noise channel
with a nonnegative input constraint, also known as the intensity-modulated
direct detection channel, with and without confining them to a lattice
structure. Our optimization criteria are the average electrical and optical
power. The nonnegativity input signal constraint is translated into a conical
constraint in signal space, and modulation formats are designed by sphere
packing inside this cone. Some remarkably dense packings are found, which yield
more power-efficient modulation formats than previously known. For example, at
a spectral efficiency of 1 bit/s/Hz, the obtained modulation format offers a
0.86 dB average electrical power gain and 0.43 dB average optical power gain
over the previously best known modulation formats to achieve a symbol error
rate of 10^-6. This modulation turns out to have a lattice-based structure. At
a spectral efficiency of 3/2 bits/s/Hz and to achieve a symbol error rate of
10^-6, the modulation format obtained for optimizing the average electrical
power offers a 0.58 dB average electrical power gain over the best
lattice-based modulation and 2.55 dB gain over the best previously known
format. However, the modulation format optimized for average optical power
offers a 0.46 dB average optical power gain over the best lattice-based
modulation and 1.35 dB gain over the best previously known format.Comment: Submitted to Globecom 201
Stable adaptive control with gain constraints
Stable adaptive control with gain constraint
Convolutional coded dual header pulse interval modulation for line of sight photonic wireless links.
The analysis and simulation for convolutional coded dual header pulse interval modulation (CC-DH-PIM) scheme using a rate ½ convolutional code with the constraint length of 3 is presented. Decoding is implemented using the Viterbi algorithm with a hard decision. Mathematical analysis for the slot error rate (SER) upper bounds is presented and results are compared with the simulated data for a number of different modulation techniques. The authors show that the coded DH-PIM outperforms the pulse position modulation (PPM) scheme and offers >4 dB code gain at the SER of 10?4 compared to the standard DH-PIM. Results presented show that the CC-DH-PIM with a higher constraint length of 7 offers a code gain of 2 dB at SER of 10?5 compared to the CC-DH-PIM with a constraint length of 3. However, in CC-DH-PIM the improvement in the error performance is achieved at the cost of reduced transmission throughput compared to the standard DH-PIM
Gain-constrained recursive filtering with stochastic nonlinearities and probabilistic sensor delays
This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2013 IEEE.This paper is concerned with the gain-constrained recursive filtering problem for a class of time-varying nonlinear stochastic systems with probabilistic sensor delays and correlated noises. The stochastic nonlinearities are described by statistical means that cover the multiplicative stochastic disturbances as a special case. The phenomenon of probabilistic sensor delays is modeled by introducing a diagonal matrix composed of Bernoulli distributed random variables taking values of 1 or 0, which means that the sensors may experience randomly occurring delays with individual delay characteristics. The process noise is finite-step autocorrelated. The purpose of the addressed gain-constrained filtering problem is to design a filter such that, for all probabilistic sensor delays, stochastic nonlinearities, gain constraint as well as correlated noises, the cost function concerning the filtering error is minimized at each sampling instant, where the filter gain satisfies a certain equality constraint. A new recursive filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the pre-specified filter gain constraint. A simulation example is provided to illustrate the effectiveness of the proposed filter design approach.This work was supported in part by the National Natural Science Foundation of China by Grants 61273156, 61028008, 60825303, 61104125, and 11271103, National 973 Project by Grant 2009CB320600, the Fok Ying Tung Education Fund by Grant 111064, the Special Fund for the Author of National Excellent Doctoral Dissertation of China by Grant 2007B4, the State Key Laboratory of Integrated Automation for the Process Industry (Northeastern University) of China, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. by Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany
Sidelobe Suppression for Robust Beamformer via The Mixed Norm Constraint
Applying a sparse constraint on the beam pattern has been suggested to
suppress the sidelobe of the minimum variance distortionless response (MVDR)
beamformer recently. To further improve the performance, we add a mixed norm
constraint on the beam pattern. It matches the beam pattern better and
encourages dense distribution in mainlobe and sparse distribution in sidelobe.
The obtained beamformer has a lower sidelobe level and deeper nulls for
interference avoidance than the standard sparse constraint based beamformer.
Simulation demonstrates that the SINR gain is considerable for its lower
sidelobe level and deeper nulling for interference, while the robustness
against the mismatch between the steering angle and the direction of arrival
(DOA) of the desired signal, caused by imperfect estimation of DOA, is
maintained too.Comment: 10 pages, 3 figures; accepted by Wireless Personal Communication
Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems
We propose a subspace constrained precoding scheme that exploits the spatial
channel correlation structure in massive MIMO cellular systems to fully unleash
the tremendous gain provided by massive antenna array with reduced channel
state information (CSI) signaling overhead. The MIMO precoder at each base
station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace
control matrix. The inner precoder is adaptive to the local CSI at each BS for
spatial multiplexing gain. The Tx subspace control is adaptive to the channel
statistics for inter-cell interference mitigation and Quality of Service (QoS)
optimization. Specifically, the Tx subspace control is formulated as a QoS
optimization problem which involves an SINR chance constraint where the
probability of each user's SINR not satisfying a service requirement must not
exceed a given outage probability. Such chance constraint cannot be handled by
the existing methods due to the two stage precoding structure. To tackle this,
we propose a bi-convex approximation approach, which consists of three key
ingredients: random matrix theory, chance constrained optimization and
semidefinite relaxation. Then we propose an efficient algorithm to find the
optimal solution of the resulting bi-convex approximation problem. Simulations
show that the proposed design has significant gain over various baselines.Comment: 13 pages, accepted by IEEE Transactions on Wireless Communication
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