Dynamic diversity combiner with associative memory model for recovering signals in communication systems

• US 20030048861A1
• Filed: 09/10/2001
• Published: 03/13/2003
• Est. Priority Date: 09/10/2001
• Status: Active Grant

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation), Princeton University
• Original Assignee: Princeton University
• Inventors: Zhang, Jinyun, Kung, Sun Yuan, Zhang, Xinying
• Granted Patent: US 6,999,538 B2
• Time in Patent Office: Days
• US Class Current: 375/347
• CPC Class Codes: H04B 7/0854 using error minimizing algorithms, e.g. minimum mean squared error [MMSE], "cross-correlation" or matrix inversion
• CPC Class Codes: H04B 7/12 Frequency diversity
• CPC Class Codes: H04L 1/0618 Space-time coding

Abstract

A source signal transmitted through multiple channels having space, time and frequency diversities to generate multiple received signals is recovered by an iterative associative memory model with dynamic maximum likelihood estimation. A current symbol vector representing the multiple received signals is projected to a net-vector using a linear matrix operation with a weight matrix W. The weight matrix is obtained by a singular value decomposition of an input symbol sequence. The net-vector is mapped to a nearest symbol vector using a non-linear operation with an activation function The projecting and converting steps are repeated until the nearest symbol vector converges to a valid symbol vector representing the source signal.

Claims

1. A method for dynamically recovering a source signal transmitted through multiple channels having space, time and frequency diversities to generate multiple received signals, comprising:

projecting a current symbol vector representing the multiple received signals to a net-vector using a linear matrix operation with a weight matrix W;

mapping the net-vector to a nearest symbol vector using a non-linear operation with an activation function T; and

repeating the projecting and converting until the nearest symbol vector converges to a valid symbol vector representing the source signal.

2. The method of claim 1 wherein the weight matrix is conjugate-symmetric and normalized with non-zero singular values identically equal to one.

3. The method of claim 1 wherein the converting further comprising:

quantitizes the net-vector.

4. The method of claim 1 wherein the channels operate in a h-domain and a receiver operates in a g-domain, and further comprising:

performing the projecting and converting steps only in the g-domain.

5. The method of claim 1 further comprising:

estimating channel parameters g by maximizing a weighted density function {g, ŝ

}=arg max ƒ

(ŝ

|x, g), where ŝ

represents the source signal and x the multiple received signals.

6. The method of claim 1 wherein the weight matrix W is a singular value decomposition of the received signals.

7. The method of claim 1 wherein the weight matrix W is set to W^(ρ

)=UU^H, where the matrix U is formed from right singular vectors corresponding to non-zero singular values of a decomposition Γ

^ρ

(x)=U Σ

V, where Γ

^ρ

(x) is a block Toeplitz matrix with ρ

blocks of columns.

8. The method of claim 1 wherein multiple source generate the multiple received signals.

9. A dynamic diversity combiner method for recovering a source signal transmitted through multiple channels having space, time and frequency diversities to generate multiple received signals, comprising:

means for projecting a current symbol vector representing the multiple received signals to a net-vector using a linear matrix operation with a weight matrix W;

means for mapping the net-vector to a nearest symbol vector using a non-linear operation with an activation function T; and

means for repeating the projecting and converting until the nearest symbol vector converges to a valid symbol vector representing the source signal.