9,463 research outputs found
Computing the channel capacity of a communication system affected by uncertain transition probabilities
We study the problem of computing the capacity of a discrete memoryless
channel under uncertainty affecting the channel law matrix, and possibly with a
constraint on the average cost of the input distribution. The problem has been
formulated in the literature as a max-min problem. We use the robust
optimization methodology to convert the max-min problem to a standard convex
optimization problem. For small-sized problems, and for many types of
uncertainty, such a problem can be solved in principle using interior point
methods (IPM). However, for large-scale problems, IPM are not practical. Here,
we suggest an first-order algorithm based on Nemirovski
(2004) which is applied directly to the max-min problem.Comment: 22 pages, 2 figure
Large-System Analysis of Multiuser Detection with an Unknown Number of Users: A High-SNR Approach
We analyze multiuser detection under the assumption that the number of users
accessing the channel is unknown by the receiver. In this environment, users'
activity must be estimated along with any other parameters such as data, power,
and location. Our main goal is to determine the performance loss caused by the
need for estimating the identities of active users, which are not known a
priori. To prevent a loss of optimality, we assume that identities and data are
estimated jointly, rather than in two separate steps. We examine the
performance of multiuser detectors when the number of potential users is large.
Statistical-physics methodologies are used to determine the macroscopic
performance of the detector in terms of its multiuser efficiency. Special
attention is paid to the fixed-point equation whose solution yields the
multiuser efficiency of the optimal (maximum a posteriori) detector in the
large signal-to-noise ratio regime. Our analysis yields closed-form approximate
bounds to the minimum mean-squared error in this regime. These illustrate the
set of solutions of the fixed-point equation, and their relationship with the
maximum system load. Next, we study the maximum load that the detector can
support for a given quality of service (specified by error probability).Comment: to appear in IEEE Transactions on Information Theor
Computing with Coloured Tangles
We suggest a diagrammatic model of computation based on an axiom of
distributivity. A diagram of a decorated coloured tangle, similar to those that
appear in low dimensional topology, plays the role of a circuit diagram.
Equivalent diagrams represent bisimilar computations. We prove that our model
of computation is Turing complete, and that with bounded resources it can
moreover decide any language in complexity class IP, sometimes with better
performance parameters than corresponding classical protocols.Comment: 36 pages,; Introduction entirely rewritten, Section 4.3 adde
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Practical issues for the implementation of survivability and recovery techniques in optical networks
Spatial Throughput Maximization of Wireless Powered Communication Networks
Wireless charging is a promising way to power wireless nodes' transmissions.
This paper considers new dual-function access points (APs) which are able to
support the energy/information transmission to/from wireless nodes. We focus on
a large-scale wireless powered communication network (WPCN), and use stochastic
geometry to analyze the wireless nodes' performance tradeoff between energy
harvesting and information transmission. We study two cases with battery-free
and battery-deployed wireless nodes. For both cases, we consider a
harvest-then-transmit protocol by partitioning each time frame into a downlink
(DL) phase for energy transfer, and an uplink (UL) phase for information
transfer. By jointly optimizing frame partition between the two phases and the
wireless nodes' transmit power, we maximize the wireless nodes' spatial
throughput subject to a successful information transmission probability
constraint. For the battery-free case, we show that the wireless nodes prefer
to choose small transmit power to obtain large transmission opportunity. For
the battery-deployed case, we first study an ideal infinite-capacity battery
scenario for wireless nodes, and show that the optimal charging design is not
unique, due to the sufficient energy stored in the battery. We then extend to
the practical finite-capacity battery scenario. Although the exact performance
is difficult to be obtained analytically, it is shown to be upper and lower
bounded by those in the infinite-capacity battery scenario and the battery-free
case, respectively. Finally, we provide numerical results to corroborate our
study.Comment: 15 double-column pages, 8 figures, to appear in IEEE JSAC in February
2015, special issue on wireless communications powered by energy harvesting
and wireless energy transfe
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