MIMO Spatial Multiplexing
- overview of MIMO - Multiple Input Multiple Output, spatial multiplexing used to provide additional data bandwidth in multipath radio scenarios.
MIMO technology tutorial includes:• MIMO technology tutorial • SIMO, SIMO, MISO, MU-MIMO • MIMO spatial multiplexing • MIMO coding, inc Alamouti codes • MIMO antenna beamforming • Multi-User MIMO, MU-MIMO
One of the key advantages of MIMO spatial multiplexing is the fact that it is able to provide additional data capacity. MIMO spatial multiplexing achieves this by utilising the multiple paths and effectively using them as additional "channels" to carry data.
The maximum amount of data that can be carried by a radio channel is limited by the physical boundaries defined under Shannon's Law.
Shannon's Law and MIMO spatial multiplexing
As with many areas of science, there a theoretical boundaries, beyond which it is not possible to proceed. This is true for the amount of data that can be passed along a specific channel in the presence of noise. The law that governs this is called Shannon's Law, named after the man who formulated it. This is particularly important because MIMO wireless technology provides a method not of breaking the law, but increasing data rates beyond those possible on a single channel without its use.
Shannon's law defines the maximum rate at which error free data can be transmitted over a given bandwidth in the presence of noise. It is usually expressed in the form:
Where C is the channel capacity in bits per second, W is the bandwidth in Hertz, and S/N is the SNR (Signal to Noise Ratio).
From this it can be seen that there is an ultimate limit on the capacity of a channel with a given bandwidth. However before this point is reached, the capacity is also limited by the signal to noise ratio of the received signal.
In view of these limits many decisions need to be made about the way in which a transmission is made. The modulation scheme can play a major part in this. The channel capacity can be increased by using higher order modulation schemes, but these require a better signal to noise ratio than the lower order modulation schemes. Thus a balance exists between the data rate and the allowable error rate, signal to noise ratio and power that can be transmitted.
While some improvements can be made in terms of optimising the modulation scheme and improving the signal to noise ratio, these improvements are not always easy or cheap and they are invariably a compromise, balancing the various factors involved. It is therefore necessary to look at other ways of improving the data throughput for individual channels. MIMO is one way in which wireless communications can be improved and as a result it is receiving a considerable degree of interest.
MIMO spatial multiplexing
To take advantage of the additional throughput capability, MIMO utilises several sets of antennas. In many MIMO systems, just two are used, but there is no reason why further antennas cannot be employed and this increases the throughput. In any case for MIMO spatial multiplexing the number of receive antennas must be equal to or greater than the number of transmit antennas.
To take advantage of the additional throughput offered, MIMO wireless systems utilise a matrix mathematical approach. Data streams t1, t2, … tn can be transmitted from antennas 1, 2, …n. Then there are a variety of paths that can be used with each path having different channel properties. To enable the receiver to be able to differentiate between the different data streams it is necessary to use. These can be represented by the properties h12, travelling from transmit antenna one to receive antenna 2 and so forth. In this way for a three transmit, three receive antenna system a matrix can be set up:
Where r1 = signal received at antenna 1, r2 is the signal received at antenna 2 and so forth.
In matrix format this can be represented as:
To recover the transmitted data-stream at the receiver it is necessary to perform a considerable amount of signal processing. First the MIMO system decoder must estimate the individual channel transfer characteristic hij to determine the channel transfer matrix. Once all of this has been estimated, then the matrix [H] has been produced and the transmitted data streams can be reconstructed by multiplying the received vector with the inverse of the transfer matrix.
This process can be likened to the solving of a set of N linear simultaneous equations to reveal the values of N variables.
In reality the situation is a little more difficult than this as propagation is never quite this straightforward, and in addition to this each variable consists of an ongoing data stream, this nevertheless demonstrates the basic principle behind MIMO wireless systems.
By Ian Poole
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