2016 24th European Signal Processing Conference (EUSIPCO) / Institute of Electrical and Electronics Engineers.

Format:

Book

Author/Creator:

Institute of Electrical and Electronics Engineers, author, issuing body.

Language:

English

Subjects (All):

Signal processing--Congresses.

Signal processing.

Physical Description:

1 online resource

Other Title:

2016 24th European Signal Processing Conference

Place of Publication:

Piscataway, NJ : IEEE, 2016.

Summary:

The paper investigates the problem of maximizing the expected achievable sum rate in a fading multiple access cognitive radio network when secondary user (SU) transmitters have energy harvesting capability, and perform cooperative spectrum sensing. We formulate the problem as maximization of throughput of the cognitive multiple access network over a finite time horizon subject to a time averaged interference constraint at the primary user (PU) and almost sure energy causality constraints at the SUs. The problem is a mixed integer nonlinear program with respect to two decision variables, namely, spectrum access decision and spectrum sensing decision, and the continuous variables sensing time and transmission power. In general, this problem is known to be NP hard. For optimization over these two decision variables, we use an exhaustive search policy when the length of the time horizon is small, and a heuristic policy for longer horizons. For given values of the decision variables, the problem simplifies into a joint optimization on SU transmission power and sensing time, which is non-convex in nature. We present an analytic solution for the resulting optimization problem using an alternating convex optimization problem for non-causal channel state information and harvested energy information patterns at the SU base station (SBS) or fusion center (FC) and infinite battery capacity at the SU transmitters. We formulate the problem with causal information and finite battery capacity as a stochastic control problem and solve it using the technique of dynamic programming. Numerical results are presented to illustrate the performance of the various algorithms.

Contents:

[Copyright notice]

Welcome message from general chair

Welcome message from TPC chairs

Committees

Technical program committee

Table of contents

Kernel adaptive filtering subject to equality function constraints

A low-complexity RLS-DCD algorithm for volterra system identification

Integrated direct sub-band adaptive volterra filter and its application to identification of loudspeaker nonlinearity

Design of dynamic linear-in-the-parameters nonlinear filters for active noise control

Extension of Generalized Hammerstein model to non-polynomial inputs

A mathematical analysis of the Genetic-AIRS classification algorithm

A system identification approach to determining listening attention from EEG signals.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9780992862657

0992862655