4,120 research outputs found
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
A recent strategy to circumvent the exploding and vanishing gradient problem
in RNNs, and to allow the stable propagation of signals over long time scales,
is to constrain recurrent connectivity matrices to be orthogonal or unitary.
This ensures eigenvalues with unit norm and thus stable dynamics and training.
However this comes at the cost of reduced expressivity due to the limited
variety of orthogonal transformations. We propose a novel connectivity
structure based on the Schur decomposition and a splitting of the Schur form
into normal and non-normal parts. This allows to parametrize matrices with
unit-norm eigenspectra without orthogonality constraints on eigenbases. The
resulting architecture ensures access to a larger space of spectrally
constrained matrices, of which orthogonal matrices are a subset. This crucial
difference retains the stability advantages and training speed of orthogonal
RNNs while enhancing expressivity, especially on tasks that require
computations over ongoing input sequences
A Utility Proportional Fairness Radio Resource Block Allocation in Cellular Networks
This paper presents a radio resource block allocation optimization problem
for cellular communications systems with users running delay-tolerant and
real-time applications, generating elastic and inelastic traffic on the network
and being modelled as logarithmic and sigmoidal utilities respectively. The
optimization is cast under a utility proportional fairness framework aiming at
maximizing the cellular systems utility whilst allocating users the resource
blocks with an eye on application quality of service requirements and on the
procedural temporal and computational efficiency. Ultimately, the sensitivity
of the proposed modus operandi to the resource variations is investigated
Initial results on an MMSE precoding and equalisation approach to MIMO PLC channels
This paper addresses some initial experiments using polynomial matrix decompositions to construct MMSE precoders and equalisers for MIMO power line communications (PLC) channels. The proposed scheme is based on a Wiener formulation based on polynomial matrices, and recent results to design and implement such systems with polynomial matrix tools. Applied to the MIMO PLC channel, the strong spectral dynamics of the PLC system together with the long impulse responses contained in the MIMO system result in problems, such that diagonlisation and spectral majorisation is mostly achieved in bands of high energy, while low-energy bands can resist any diagonalisation efforts. We introduce the subband approach in order to deal with this problem. A representative example using a simulated MIMO PLC channel is presented
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