9,054 research outputs found
Nomographic Functions: Efficient Computation in Clustered Gaussian Sensor Networks
In this paper, a clustered wireless sensor network is considered that is
modeled as a set of coupled Gaussian multiple-access channels. The objective of
the network is not to reconstruct individual sensor readings at designated
fusion centers but rather to reliably compute some functions thereof. Our
particular attention is on real-valued functions that can be represented as a
post-processed sum of pre-processed sensor readings. Such functions are called
nomographic functions and their special structure permits the utilization of
the interference property of the Gaussian multiple-access channel to reliably
compute many linear and nonlinear functions at significantly higher rates than
those achievable with standard schemes that combat interference. Motivated by
this observation, a computation scheme is proposed that combines a suitable
data pre- and post-processing strategy with a nested lattice code designed to
protect the sum of pre-processed sensor readings against the channel noise.
After analyzing its computation rate performance, it is shown that at the cost
of a reduced rate, the scheme can be extended to compute every continuous
function of the sensor readings in a finite succession of steps, where in each
step a different nomographic function is computed. This demonstrates the
fundamental role of nomographic representations.Comment: to appear in IEEE Transactions on Wireless Communication
Network vector quantization
We present an algorithm for designing locally optimal vector quantizers for general networks. We discuss the algorithm's implementation and compare the performance of the resulting "network vector quantizers" to traditional vector quantizers (VQs) and to rate-distortion (R-D) bounds where available. While some special cases of network codes (e.g., multiresolution (MR) and multiple description (MD) codes) have been studied in the literature, we here present a unifying approach that both includes these existing solutions as special cases and provides solutions to previously unsolved examples
Full-Duplex Cloud Radio Access Network: Stochastic Design and Analysis
Full-duplex (FD) has emerged as a disruptive communications paradigm for
enhancing the achievable spectral efficiency (SE), thanks to the recent major
breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex
(HD) SE gain, in cellular networks, is however largely limited by the
mutual-interference (MI) between the downlink (DL) and the uplink (UL). A
potential remedy for tackling the MI bottleneck is through cooperative
communications. This paper provides a stochastic design and analysis of FD
enabled cloud radio access network (C-RAN) under the Poisson point process
(PPP)-based abstraction model of multi-antenna radio units (RUs) and user
equipments (UEs). We consider different disjoint and user-centric approaches
towards the formation of finite clusters in the C-RAN. Contrary to most
existing studies, we explicitly take into consideration non-isotropic fading
channel conditions and finite-capacity fronthaul links. Accordingly,
upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics
of all intended and interfering signals, are derived. The performance of the FD
C-RAN is investigated through the proposed theoretical framework and
Monte-Carlo (MC) simulations. The results indicate that significant FD versus
HD C-RAN SE gains can be achieved, particularly in the presence of
sufficient-capacity fronthaul links and advanced interference cancellation
capabilities
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