Designing optimal wireless basestation MIMO antennae: Part 1 - Sorting out the confusion

Noam Dvoretzki and Zeev Kaplan, CEVA
embedded.com (July 21, 2014)

Embedded developers building 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.

MIMO (Multiple Input Multiple Output) 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.

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