Spatial Multidimensional Cooperative Transmission Theories And Key Technologies. Lin Bai
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Название: Spatial Multidimensional Cooperative Transmission Theories And Key Technologies

Автор: Lin Bai

Издательство: Ingram

Жанр: Зарубежная компьютерная литература

Серия:

isbn: 9789811202476

isbn:

СКАЧАТЬ sM−1. This successive cancellation process can continue until all data signals are detected sequentially. In other words, the mth signal will be detected after the first M – m signals are detected and the interference is eliminated, which can be described as

figure

      where figureq represents an estimated value of sq detected from the received signal uq. Assuming that there are no errors during all detection process, sm can be estimated as

figure

      where figure.

      Since the successive interference cancellation algorithm introduced above is based on the zero-forcing decision feedback equalizer (ZF-DFE), we call the algorithm a zero-forcing successive interference cancellation (ZF-SIC).12, 13 It should be noted that if the channel state matrix H is a fat matrix with M > N, which means the number of rows of the channel matrix is larger than the number of columns, then the successive interference cancellation algorithms cannot be used in this case due to the lack of an upper triangular array after QR decomposition.

      In order to improve system performance, background noise should to be considered when performing the detection. To solve this problem, we need to employ a successive interference cancellation algorithm based on minimum mean square error decision equalizer (MMSE-DFE). And two implementation strategies of MMSE-SIC algorithm are introduced in this section.

      Strategy 1: Define the extended channel matrix figure. extended received signal yex = [yT 0T]T, and extended background noise vector figure. Through QR decomposition, we can get

figure

      where Qex and Rex represent the unitary matrix and the upper triangular matrix, respectively. Replacing y, n, Q, and R in Eq. (2.158) by yex, nex, Qex, and Rex, we get

figure

      Based on Eq. (2.170), MMSE-SIC detection can be carried out according to Eqs. (2.158)–(2.168).

      Strategy 2: Utilizing the minimum mean square error estimator (MMSE Estimator) directly, the MMSE estimator for signal s1 can be expressed as

figure

      where figure1 represents the first column vector of HH. The hard decision on symbol s1 can be described as

figure

      Assuming that s1 can be detected correctly and its effect can be removed from y, we can obtain

figure

      According to y1, s2 can be detected by the MMSE method. And the MMSE-SIC detection of sm can be carried out by repeated interference cancellation and MMSE estimation.

      As can be seen from Fig. 2.12, the optimal detector ML has obvious advantages compared with the MIMO linear detector (ZF, MMSE). However, the complexity which exponentially increases with the number of transmitting antennas makes it difficult to apply in practical systems. And although linear detectors (ZF, MMSE) have a relatively low complexity, their performance is not satisfactory. The SIC method can improve the performance of the linear detectors (ZF, MMSE) to a certain extent, but their performance is still not on par with ML.

      How to utilize the optimal spatial signal combination and detection method effectively to improve the performance and spectrum efficiency of wireless communication systems is a key issue to be remembered when designing the next generation of wireless communication. The knowledge of spatial multidimensional signal combination and detection is first introduced in this chapter, and then the principle and basic concept of two typical multi-antenna systems, namely the array antenna system and the MIMO antenna system, which can improve the performance and spectrum efficiency of wireless communication system, are also introduced. In Chapter 3, we will elaborate on the principles and techniques of adaptive antennas based on the array antenna system introduced in this chapter.

figure

       Fig. 2.12. The MIMO multiple detector error performance of a 4 × 4 MIMO system.

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