47,551 research outputs found
Energy-Efficient Joint Estimation in Sensor Networks: Analog vs. Digital
Sensor networks in which energy is a limited resource so that energy
consumption must be minimized for the intended application are considered. In
this context, an energy-efficient method for the joint estimation of an unknown
analog source under a given distortion constraint is proposed. The approach is
purely analog, in which each sensor simply amplifies and forwards the
noise-corrupted analog bservation to the fusion center for joint estimation.
The total transmission power across all the sensor nodes is minimized while
satisfying a distortion requirement on the joint estimate. The energy
efficiency of this analog approach is compared with previously proposed digital
approaches with and without coding. It is shown in our simulation that the
analog approach is more energy-efficient than the digital system without
coding, and in some cases outperforms the digital system with optimal coding.Comment: To appear in Proceedings of the 2005 IEEE International Conference on
Acoustics, Speech and Signal Processing, Philadelphia, PA, March 19 - 23,
200
Analog Multiple Descriptions: A Zero-Delay Source-Channel Coding Approach
This paper extends the well-known source coding problem of multiple
descriptions, in its general and basic setting, to analog source-channel coding
scenarios. Encoding-decoding functions that optimally map between the (possibly
continuous valued) source and the channel spaces are numerically derived. The
main technical tool is a non-convex optimization method, namely, deterministic
annealing, which has recently been successfully used in other mapping
optimization problems. The obtained functions exhibit several interesting
structural properties, map multiple source intervals to the same interval in
the channel space, and consistently outperform the known competing mapping
techniques.Comment: Submitted to ICASSP 201
The Practical Challenges of Interference Alignment
Interference alignment (IA) is a revolutionary wireless transmission strategy
that reduces the impact of interference. The idea of interference alignment is
to coordinate multiple transmitters so that their mutual interference aligns at
the receivers, facilitating simple interference cancellation techniques. Since
IA's inception, researchers have investigated its performance and proposed
improvements, verifying IA's ability to achieve the maximum degrees of freedom
(an approximation of sum capacity) in a variety of settings, developing
algorithms for determining alignment solutions, and generalizing transmission
strategies that relax the need for perfect alignment but yield better
performance. This article provides an overview of the concept of interference
alignment as well as an assessment of practical issues including performance in
realistic propagation environments, the role of channel state information at
the transmitter, and the practicality of interference alignment in large
networks.Comment: submitted to IEEE Wireless Communications Magazin
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