MIMO Beamforming

MIMO Beamforming

Qinghua Li (Intel Corporation, Santa Clara, USA), Xintian Eddie Lin (Intel Corporation, Santa Clara, USA) and Jianzhong ("Charlie") Zhang (Samsung, Richardson, USA)
DOI: 10.4018/978-1-59904-988-5.ch012
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Transmit beamforming improves the performance of multiple-input multiple-output antenna system (MIMO) by exploiting channel state information (CSI) at the transmitter. Numerous MIMO beamforming schemes are proposed in open literature and standard bodies such as 3GPP, IEEE 802.11n and 802.16d/e. This chapter describes the underlying principle, evolving techniques, and corresponding industrial applications of MIMO beamforming. The main limiting factor is the cumbersome overhead to acquire CSI at the transmitter. The solutions are categorized into FDD (Frequency Division Duplex) and TDD (Time Division Duplex) approaches. For all FDD channels and radio calibration absent TDD channels, channel reciprocity is not available and explicit feedback is required. Codebook-based feedback techniques with various quantization complexities and feedback overheads are depicted in this chapter. Furthermore, we discuss transmit/receive (Tx/Rx) radio chain calibration and channel sounding techniques for TDD channels, and show how to achieve channel reciprocity by overcoming the Tx/Rx asymmetry of the RF components
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It is well known that antenna phase array can form one directional radiation pattern (i.e. beam) to enhance transmit (or receive) signal energy at a desired direction. The directivity is obtained by constructive interference among multiple antenna signals in the desired direction. This is called transmit (or receive) beamforming. Beamforming can be applied to MIMO system by exploiting the multiple antennas at the transmitter and receiver. For example, 1 MIMO beamforming forms two beams and sends two data streams as shown in Figure 1. The received power can be increased by about 1.8 dB over that of MIMO. We call beamforming techniques in MIMO system MIMO beamforming. The principle of MIMO beamforming and the ideal beamforming algorithm, i.e. SVD beamforming (Telatar, 1995) are illustrated in Figure 2 and Figure 1. The transformation of input and output signal spaces is illustrated in Figure 2 for a example. In general, the singular value decomposition of an channel matrix is:

Figure 1.

A 3×2 MIMO beamforming example with system configuration and baseband signal model. The channel between the -th transmit and -th receive antenna is denoted by . The beamforming weight on the -th transmit antenna for -th data stream is denoted by .

Figure 2.

Transformation of signal space over MIMO channel

where and are unitary matrix2; is the -th singular value with . The transformation of rotates input vector to output vector and amplifies the length by for . Since the transformation is linear, the input space 3 is transformed to the output space for , where is the number of active data streams. As a result, the signal space is converted into zero vector, i.e. null space. This is illustrated in Figure 2.

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