Receive diversity schemes – channel models

SIMO channel configuration is characterized by 1 transmit antenna and multiple receiver antennas (Figure 1). SIMO configuration is used to provide receive diversity, where the same information is received across independent fading channels to combat fading. When multiple copies of the same data are received across independently fading channels, the amount of fade suffered by each copy of the data will be different. This guarantees that at-least one of the copy will suffer less fading compared to rest of the copies. Thus, the chance of properly receiving the transmitted data increases. In effect, this improves the reliability of the entire system. 

Single Input Multiple Output (SIMO) models for receive diversity
Figure 1: Single Input Multiple Output (SIMO) channel model

The channels are independent and identically distributed (i.i.d) and hence the error event across those independent channels are also independent. The signal to noise ratios (SNR) of the channels are also i.i.d and the every channel has the same average SNR. The is the fundamental assumption of the SIMO model incorporating receive diversity.

For the case of receive antennas, independent received signal copies are

Writing in compact form, the received signal

where,

Figure 2: Processing the received samples at the receiver

The receiver, takes in these received copies, applies weights according to the chosen receive diversity technique (see below) and outputs a single copy (Figure 2). The combined output signal is given by

where, the weight vector is

How are the weights chosen ? The weights are chosen based on the following receive diversity techniques.

Selection Combining (SC)

In selection combining, the received signal from the antenna that experiences the highest SNR is chosen for further processing. That is, the path that has the highest is chosen.

This is the simplest of all the receive diversity techniques. It simply chooses one branch that experiences the highest SNR. It seems to waste the remaining branches in the selection processes. Also, channel phase information is not needed for this technique.

This technique provides SNR gain in the order of .

Maximum Ratio Combining (MRC)

As we saw earlier, the selection combining is the simplest algorithm but it wastes receive elements in the computations. Maximum ratio combining technique uses all the received signals in order to maximize the output SNR. It represents maximum likelihood estimation. The output signal is the weighted sum of all the received branches. The weights for MRC technique is chosen as

Therefore, it requires the knowledge of the channel at the receiver and matching of both magnitude and phase. The output signal, with weight set as , is given by

The SNR gain achieved by this technique is .

Equal Gain Combining (EGC):

The MRC technique requires matching of both magnitude and the phase of the channel. The magnitudes can fluctuate significantly. In equal gain combining, only the phase of the channel is cancelled out. The weights are chosen as

where is the channel phase information. The output signal is given by

The technique suffers small SNR loss compared to MRC, but it is a good alternative for implementation.

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References

[1] Andrea Goldsmith, “Wireless Communications”, ISBN: 978-0521837163, Cambridge University Press, 1 edition.↗

[2] Barry-Lee-Messerschmitt, “Digital Communication”, ISBN: 978-0792375487 , Springer, 3rd edition, September 30, 2003.↗

Articles in this series

Articles in this series
[1] Introduction to Multiple Antenna Systems
[2] MIMO - Diversity and Spatial Multiplexing
[3] Characterizing a MIMO channel - Channel State Information (CSI) and Condition number
[4] Capacity of a SISO system over a fading channel
[5] Ergodic Capacity of a SISO system over a Rayleigh Fading channel - Simulation in Matlab
[6] Capacity of a MIMO system over Fading Channels
[7] Single Input Multiple Output (SIMO) models for receive diversity
[8] Receiver diversity - Selection Combining
[9] Receiver diversity – Maximum Ratio Combining (MRC)

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Mathuranathan

Mathuranathan Viswanathan, is an author @ gaussianwaves.com that has garnered worldwide readership. He is a masters in communication engineering and has 12 years of technical expertise in channel modeling and has worked in various technologies ranging from read channel, OFDM, MIMO, 3GPP PHY layer, Data Science & Machine learning.

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