Cube decoding

A novel lattice decoder called the cube decoder (CD) is proposed in this paper. The cube decoder finds the nearest lattice point to the received signal vector inside a hypercube centered at the received vector. The dimensions of the hypercube depends on the modulating lattice. This is achieved by reformulating the detection problem as a bounded-error subset selection (BESS) and solving a binary integer program. In this paper, it is assumed that the channel is known to the receiver. The proposed decoder uses the lattice reduction technique to reduce the interference introduced by the channel. Simulation shows that the CD gives near-optimal performance. Unlike the sphere decoder (SD), the complexity of the CD shows weak dependence on SNR.

[1]  U. Fincke,et al.  Improved methods for calculating vectors of short length in a lattice , 1985 .

[2]  Babak Hassibi,et al.  Efficient statistical pruning for maximum likelihood decoding , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[3]  Reinaldo A. Valenzuela,et al.  Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture , 1999 .

[4]  Gerard J. Foschini,et al.  Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas , 1996, Bell Labs Technical Journal.

[5]  Balas K. Natarajan,et al.  Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..

[6]  Stephen P. Boyd,et al.  Integer parameter estimation in linear models with applications to GPS , 1998, IEEE Trans. Signal Process..

[7]  Ahmed H. Tewfik,et al.  Bounded subset selection with noninteger coefficients , 2004, 2004 12th European Signal Processing Conference.

[8]  Gregory W. Wornell,et al.  Lattice-reduction-aided detectors for MIMO communication systems , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[9]  László Lovász,et al.  Factoring polynomials with rational coefficients , 1982 .

[10]  Ahmed H. Tewfik,et al.  A sparse solution to the bounded subset selection problem: a network flow model approach , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.