15,370 research outputs found
Limited-Feedback-Based Channel-Aware Power Allocation for Linear Distributed Estimation
This paper investigates the problem of distributed best linear unbiased
estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless
sensor network (WSN). In particular, the application of limited-feedback
strategies for the optimal power allocation in distributed estimation is
studied. In order to find the BLUE estimator of the unknown parameter, the FC
combines spatially distributed, linearly processed, noisy observations of local
sensors received through orthogonal channels corrupted by fading and additive
Gaussian noise. Most optimal power-allocation schemes proposed in the
literature require the feedback of the exact instantaneous channel state
information from the FC to local sensors. This paper proposes a
limited-feedback strategy in which the FC designs an optimal codebook
containing the optimal power-allocation vectors, in an iterative offline
process, based on the generalized Lloyd algorithm with modified distortion
functions. Upon observing a realization of the channel vector, the FC finds the
closest codeword to its corresponding optimal power-allocation vector and
broadcasts the index of the codeword. Each sensor will then transmit its analog
observations using its optimal quantized amplification gain. This approach
eliminates the requirement for infinite-rate digital feedback links and is
scalable, especially in large WSNs.Comment: 5 Pages, 3 Figures, 1 Algorithm, Forty Seventh Annual Asilomar
Conference on Signals, Systems, and Computers (ASILOMAR 2013
Dynamic Resource Allocation for Multiple-Antenna Wireless Power Transfer
We consider a point-to-point multiple-input-single-output (MISO) system where
a receiver harvests energy from a wireless power transmitter to power itself
for various applications. The transmitter performs energy beamforming by using
an instantaneous channel state information (CSI). The CSI is estimated at the
receiver by training via a preamble, and fed back to the transmitter. The
channel estimate is more accurate when longer preamble is used, but less time
is left for wireless power transfer before the channel changes. To maximize the
harvested energy, in this paper, we address the key challenge of balancing the
time resource used for channel estimation and wireless power transfer (WPT),
and also investigate the allocation of energy resource used for wireless power
transfer. First, we consider the general scenario where the preamble length is
allowed to vary dynamically. Taking into account the effects of imperfect CSI,
the optimal preamble length is obtained online by solving a dynamic programming
(DP) problem. The solution is shown to be a threshold-type policy that depends
only on the channel estimate power. Next, we consider the scenario in which the
preamble length is fixed. The optimal preamble length is optimized offline.
Furthermore, we derive the optimal power allocation schemes for both scenarios.
For the scenario of dynamic-length preamble, the power is allocated according
to both the optimal preamble length and the channel estimate power; while for
the scenario of fixed-length preamble, the power is allocated according to only
the channel estimate power. The analysis results are validated by numerical
simulations. Encouragingly, with optimal power allocation, the harvested energy
by using optimized fixed-length preamble is almost the same as the harvested
energy by employing dynamic-length preamble, hence allowing a low-complexity
WPT system to be implemented in practice.Comment: 30 pages, 6 figures, Submitted to the IEEE Transactions on Signal
Processin
Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users
In downlink multi-antenna systems with many users, the multiplexing gain is
strictly limited by the number of transmit antennas and the use of these
antennas. Assuming that the total number of receive antennas at the
multi-antenna users is much larger than , the maximal multiplexing gain can
be achieved with many different transmission/reception strategies. For example,
the excess number of receive antennas can be utilized to schedule users with
effective channels that are near-orthogonal, for multi-stream multiplexing to
users with well-conditioned channels, and/or to enable interference-aware
receive combining. In this paper, we try to answer the question if the data
streams should be divided among few users (many streams per user) or many users
(few streams per user, enabling receive combining). Analytic results are
derived to show how user selection, spatial correlation, heterogeneous user
conditions, and imperfect channel acquisition (quantization or estimation
errors) affect the performance when sending the maximal number of streams or
one stream per scheduled user---the two extremes in data stream allocation.
While contradicting observations on this topic have been reported in prior
works, we show that selecting many users and allocating one stream per user
(i.e., exploiting receive combining) is the best candidate under realistic
conditions. This is explained by the provably stronger resilience towards
spatial correlation and the larger benefit from multi-user diversity. This
fundamental result has positive implications for the design of downlink systems
as it reduces the hardware requirements at the user devices and simplifies the
throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/one-or-multiple-stream
Estimation in Phase-Shift and Forward Wireless Sensor Networks
We consider a network of single-antenna sensors that observe an unknown
deterministic parameter. Each sensor applies a phase shift to the observation
and the sensors simultaneously transmit the result to a multi-antenna fusion
center (FC). Based on its knowledge of the wireless channel to the sensors, the
FC calculates values for the phase factors that minimize the variance of the
parameter estimate, and feeds this information back to the sensors. The use of
a phase-shift-only transmission scheme provides a simplified analog
implementation at the sensor, and also leads to a simpler algorithm design and
performance analysis. We propose two algorithms for this problem, a numerical
solution based on a relaxed semidefinite programming problem, and a closed-form
solution based on the analytic constant modulus algorithm. Both approaches are
shown to provide performance close to the theoretical bound. We derive
asymptotic performance analyses for cases involving large numbers of sensors or
large numbers of FC antennas, and we also study the impact of phase errors at
the sensor transmitters. Finally, we consider the sensor selection problem, in
which only a subset of the sensors is chosen to send their observations to the
FC.Comment: 28 pages, 5 figures, accepted by IEEE Transactions on Signal
Processing, Apr. 201
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