MIMO Beamforming

MIMO Beamforming

Qinghua Li, Xintian Eddie Lin, Jianzhong ("Charlie") Zhang
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, 978-1-59904-988-5.ch012.m011 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 978-1-59904-988-5.ch012.m02 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 978-1-59904-988-5.ch012.m03example. In general, the singular value decomposition of an 978-1-59904-988-5.ch012.m04 channel matrix 978-1-59904-988-5.ch012.m05 is:

Figure 1.

A 3×2 MIMO beamforming example with system configuration and baseband signal model. The channel between the 978-1-59904-988-5.ch012.m22-th transmit and 978-1-59904-988-5.ch012.m23-th receive antenna is denoted by 978-1-59904-988-5.ch012.m24. The beamforming weight on the 978-1-59904-988-5.ch012.m25-th transmit antenna for 978-1-59904-988-5.ch012.m26-th data stream is denoted by 978-1-59904-988-5.ch012.m27.

Figure 2.

Transformation of signal space over MIMO channel

where 978-1-59904-988-5.ch012.m07and 978-1-59904-988-5.ch012.m08 are unitary matrix2; 978-1-59904-988-5.ch012.m09 is the 978-1-59904-988-5.ch012.m10-th singular value with 978-1-59904-988-5.ch012.m11. The transformation of 978-1-59904-988-5.ch012.m12 rotates input vector 978-1-59904-988-5.ch012.m13 to output vector 978-1-59904-988-5.ch012.m14and amplifies the length by 978-1-59904-988-5.ch012.m15 for 978-1-59904-988-5.ch012.m16. Since the transformation is linear, the input space 978-1-59904-988-5.ch012.m173 is transformed to the output space 978-1-59904-988-5.ch012.m18 for 978-1-59904-988-5.ch012.m19, where 978-1-59904-988-5.ch012.m20 is the number of active data streams. As a result, the signal space 978-1-59904-988-5.ch012.m21 is converted into zero vector, i.e. null space. This is illustrated in Figure 2.

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