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Build wireless base station MIMO antennae (Part 1)

Posted: 08 Aug 2014  Print Version  Bookmark and Share

Keywords:Multiple Input Multiple Output  MIMO  SNR  receiver  algorithm 

Embedded developers designing today's wireless network spectrum infrastructure are constantly striving to provide solutions to the growing demand for higher data rates in mobile devices. Given that radio spectrum is limited and expensive, it is vital to discover a better way to utilize the same bandwidth while transmitting even more data— or in other words, to improve the spectral efficiency of the channel.

Multiple Input Multiple Output (MIMO) is one of the leading approaches for improving data rates and/or SNR (signal-to-noise ratio). By using multiple receive and transmit antennas, MIMO can exploit the diversity of the wireless channel. This is then used to increase the spectral efficiency of the channel and improve the data rates for any given channel bandwidth.

The MIMO dimension depends on the number of antennas transmitting and receiving. In a 4X4 MIMO configuration, four transmit antennas and four receive antennas are used. Under the right conditions, this enables transmitting up to four times more data on the same channel bandwidth.

On the one hand, a simple MIMO receiver is based on a linear receiver algorithm, which is easy to implement but cannot fully exploit the MIMO benefits. On the other hand, an optimal MAP (maximum a posteriori) approximation MIMO algorithm can be implemented using an iterative technique; however, this incurs high latency penalties.

A more practical non-linear MIMO receiver implementation known as ML (maximum likelihood) or MLD (maximum likelihood detector) is fundamentally based on an exhaustive constellation search. The MLD is more demanding on processing than a conventional linear receiver, but can offer significantly higher bit rates for the same channel conditions. In addition, the MLD is more robust to channels with antenna correlation.

Working with high-order MIMO dimensions (more than two receive and two transmit antennas) can result in significantly improved spectral efficiency, but this comes at a cost. The computational complexity of the MLD receiver grows exponentially with the increase of the MIMO dimension. High-order MIMO requires considerable processing power— to the point where a straightforward MLD approach is impractical, and suboptimal MLD algorithms must be used to enable user equipment (UE) implementations.

Considering these multiple challenges, this article will first review the relevant MIMO modes and technology and the advantages of choosing a suboptimal MLD receiver over a minimal mean square error (MMSE) receiver. It will also explain the complexities of the MLD implementation and how to resolve them using suboptimal ML solutions.

Sorting out the various MIMO techniques
MIMO techniques can be split into three main groups:
• Beam-forming is used to improve the SNR of a given channel
• Transmit and receive diversity is used to improve channel quality or robustness
• Spatial Multiplexing is used to increase data throughput for a given channel

Beam-forming utilizes knowledge of the channel at the transmitter to focus the power in the direction of the receiver. Details of the channel can be obtained by receiving feedback from the receiver regarding direction and attenuation properties.

Figure 1: Tx beam forming.

By identifying the direction of the UE, the transmitter can steer a beam in that direction and thus amplify the received signal. This MIMO technique is most effective for low-SNR channels. Figure 1 describes a directional wave front achieved by timing the phase of the transmit antennas.

Transmit and receive diversity creates redundancy by transmitting the same data on multiple antennas and combining the signals received at the destination antennas to increase the robustness for a given channel. This MIMO technique is most effective for low SNR and rich multipath (or scattering) conditions. The diversity can maximize the utilization of the channel by overcoming attenuations at antennas, and make better use of antennas that receive strong signals. Overall, the SNR obtained at each antenna is improved, and this reduces decoding errors at the receiver.

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