Design of Iterative Message-Passing Receivers with Sparse Channel Estimators

Jørgensen, Peter Bjørn

4. term

Elektronics and IT, Master (Elite Wireless Communication)

2013

2013-06-06

100 pages

I dette speciale udvikles en ny iterativ algoritme til modtagelse af `orthogonal frequency division multiplexing' radiosignaler som udnytter scenariet hvor kanalens impulsrespons består af nogle få dominerende komponenter. Estimering af denne type kanal formuleres som et komprimeret signalbehandlingsproblem. Vha. den bayesianske metode integreres problemet med modtagerens andre opgaver via en faktorgrafrepræsentation af hele systemet. En hierarkisk a priori sandsynlighedsfordeling pålægges kanalens impulsrespons. Den iterative algoritme udledes analytisk ved anvendelse af en kombinationen af `belief propagation' og `mean field' metoderne på denne faktorgraf, hvorved estimering af kanalen og dekodning foretages som en samlet opgave. Simuleringer viser at den udviklede algoritme opnår lavere bitfejlrate end en tilsvarende modtager baseret på en robust antagelse om kanalen, men som ikke udnytter at kanalens impulsrespons kan beskrives vha. få dominerende komponenter. Derudover undersøges to metoder til reducering af algoritmernes beregningskompleksitet: 1) modifikation af den iterative algoritme således kun en del af data informationen anvendes til estimering af kanalen 2) gruppering af kanalvariablerne i faktorgrafen i vektorer af en bestemt størrelse. Den sidstnævnte metode giver ikke anledning til højere bitfejlrate for udviklede modtager, mens modtagerens beregningskompleksitet reduceres markant.

In this thesis we devise a novel iterative orthogonal frequency-division multiplexing receiver algorithm that exploits the assumption that the wireless channel is sparse, i.e. its impulse response consists of a few dominant components. The task of the sparse channel estimation is posed as a sparse signal estimation problem. Using the approach of sparse Bayesian learning with hierarchical prior modeling the channel estimation problem is integrated with the other receiver tasks through a factor graph representation of the whole system. The iterative algorithm for joint channel estimation and decoding is thus analytically derived by applying the combined belief propagation and mean field inference framework as message-passing on this factor graph. Our numerical results show that the proposed algorithm outperforms, in terms of bit-error-rate, an analogous receiver that uses a robust channel assumption, but does not exploit the sparsity of the channel. As the channel estimation part is of high computational complexity we propose two different methods for reducing the complexity: 1) heuristic modification of the message-computation scheduling such that only part of the soft data information is used in channel estimation 2) grouping channel variables of the factor graph into vectors of a certain size.

kanalestimering ; bayesiansk statistik ; stokastiske processor ; compressed sensing ; message passing

Download
Download
View record in AAU Student Projects