Cooperative coding is a communication paradigm that pools
distributed resources of different nodes in a network, such that
the nodes act like a collaborative system instead of greedy
adversarial participants. Cooperation has shown promise in
increasing throughput and providing better power efficiency in
wireless networks. In this work, we consider a basic example of
cooperative communication, relay coding, and consider methods to
improve the power efficiency by employing feedback and using power
control. We consider power control policies based on the degree of
transmitter channel knowledge. First, when perfect feedback is
available, we show results for the optimal power control policy
for any network code. We show that by using the decode and forward
relaying protocol, in some cases it is possible to approach the
universal lower bound on the outage probability for the block
fading relay channel. Second, when a finite rate of feedback is
available, we see that only a few feedback bits are necessary to
achieve most of the gains that the perfect feedback policy has
over constant power transmission. Based on these results, it is
evident that future network protocols should utilize feedback in
order to fully exploit the potential gains of network coding.
In this paper, we study transmission techniques for broadcast channels with a single transmitter and multiple receivers. The transmitter is assumed to be equipped with multiple antennas. Further, each receiver is interested only in part of the transmitted information. Using Gaussian code based information theoretic bounds, we analyze two transmit beamforming techniques. The first technique, zero-forcing beamformer, uses the channel information for all users to send spatially orthogonal signals to different users, i.e., no user receives interference from the other user signals. The second method uses the single-user optimal beamformer with no effort to reduce interference at the receivers. It is shown that the above transmit beamforming techniques are similar to multiuser receivers used in non-orthogonal CDMA systems. In particular, the zero-forcing beamformer is similar to a decorrelating detector and the single-user beamformer is identical to a matched filter. The comparison with CDMA multiuser receivers is strengthened by results on spectral efficiency of proposed beamformers, which follow the behavior of decorrelating and matched-filter receiver.
This paper investigates methods to reduce the amount of computation needed to detect information bits using a linear detector for a CDMA system. We show windowing technique coupled with pipelining can reduce the amount of computation without significantly sacrificing the performance of linear feedback detector. We also describe efficient techniques to adapt to a dynamic system where the system parameters vary due to the change in delays associated with individual users.
This paper considers the application of a " global" optimization scheme to the training of multilayer perceptions for signal classifications. This study is motivated by the fact that the error surface of a multilayer perceptron is a highly nonlinear function of the parameters. Therefore the backpropagation which is a gradient descent algorithm converges to locally minimum structures. As an example we consider a signal classification problem where the optimum classifier has been shown to have an exponential complexity and the optimum decision boundary to be nonlinear and nonconvex. In this example when standard backpropagation is used to train the weights of a multi-layer perception the network is shown to classify with a " linear" decision boundary which corresponds locally to a minima of the neural network configurations. In this paper we propose to enhance the learning process of the network by considering an optimization scheme referred to as simulated annealing. This optimization scheme has been proven to be effective in finding global minima in many applications. We derive an iterative training algorithm based on this " global" optimization technique using the backpropagation as the " local" optimizer. We will verify the effectiveness of the learning algorithm via an empirical analysis of two signal classification problems. 1 PRELIMINARIES Artificial Neural Networks are highly interconnected networks of relatively simple processing units (commonly referred to as nodes e. g. perceptrons) which operate in parallel.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.