In this thesis, computationally efficient detection algorithms for the minimum mean square error (MMSE) estimation with successive interference cancellation (SIC), or with generalized decision feedback equalizer(GDFE) are considered in spatial multiplexing multiple-input multipleoutput (MIMO) systems.
The MMSE-SIC and the MMSE-GDFE architectures are known to achieve the ergodic capacity of MIMO fading channels. This thesis presents a new understanding of SIC and GDFE to provide further insights. This perspective shows that 1) SIC and GDFE are a special case of the two-dimensional estimation with side information (or decision feedback), and 2) SIC can be expressed in terms of the Cholesky decomposition, as GDFE is.
Inspired by the latter, we propose fast algorithms based on the Cholesky decomposition for SIC and GDFE in multiple antenna systems. Although existing detection algorithms can provide an impressive reduction of computational complexity for a large $\It{M}$, the reduction is relatively small for a moderate $\It{M}$ of practical interest (i.e. $\It{M}$ $\le$ 16). The Cholesky decomposition is computationally more efficient than the QR decomposition. Specifically, the required number of multiplications for the Cholesky decomposition is O($M^3$/6) for an $\It{M}$$\times$$\It{M}$ matrix, whereas that of the QR decomposition amounts to O($4M^3$/3).
Efficient and stable Cholesky decomposition based detection algorithms further reduce the computational complexity of existing fast algorithms for SIC in MIMO at fading channels. To this end, we derive special properties of the Cholesky decomposition to render the proposed algorithms more efficient computationally. A complexity comparison shows that the proposed algorithms can reduce the computational complexity of SIC and GDFE more significantly than existing fast algorithms.
Finally, proposed detection algorithms for the MMSE-SIC and the MMSE-GDFE receiver can be readily extended to linear space-time coding schemes in MIMO channels. Furthermore, the proposed perspective connects SIC and GDFE with the vast accumulated knowledge on 2-Destimation problems.
본 연구는 SIC와 GDFE에 대한 새로운 시각과 통찰을 제공하고자 한다. 첫째, SIC와 GDFE는 Pagano에 의해 제안된 부가 정보가 있는 경우의 2차원 추정문제의 특별한 경우로 인식될 수 있다. 둘째, SIC와 GDFE와 마찬가지로 Cholesky decomposition으로 표현될 수 있다.
이러한 고찰을 통해 본 논문은 MIMO 채널에서 MMSE-SIC와 MMSE-GDFE를 위한 저복잡도의 효율적인 검출 알고리즘을 제안한다. 또한, 보다 효율적인 알고리즘 설곌ㄹ 위해 Cholesky decomposition과 UL decomposition의 특성을 이용한다. 마지막으로, 복잡도 비교를 통해 본 논문에서 제안하는 효율적 검출 알고리즘이 기존 검출 알고리즘에 비해 우수함을 보인다.
