![than that of MIMO coherent reception with imperfect channel state information for
a certain range of average SNR.
EXISTING SYSTEM:
NONCOHERENT receivers with energy detection have received growing attention
recently due to their possible use in applications ranging from sensor networks to
short range interconnects. They can offer the possibility of low complexity receiver
architectures and can also open up novel communication applications where
energy detection is naturally present, such as visible light communication [1], [2]
and harvesting of RF energy [3]. In conjunction with this, there has also been a
growing interest in the use of antenna systems with hundreds of antennas in the
form of large or massive multiple-input multiple-output (MIMO) or distributed
antenna systems (DAS). There is a large body of work on massive MIMO
including recent reviews [4], [5] as well as papers on performance analysis [6]–[8],
detection [9], [10], and channel modeling [11]. On the other hand, in the area of
DAS, recent work includes large-scale DAS for energy efficient communication
[12] as well as distributed antennas in energy transfer and energy harvesting
applications [13] including RFID [14]. In all these applications, if there is to be
communication in the uplink, then it is necessary to bring the signals coherently
back to a common point such as a base station. This can be performed by optical](https://image.slidesharecdn.com/signaldesignformultipleantennasystemswithspatialmultiplexingandnoncoherentreception-150928052952-lva1-app6891/85/Signal-design-for-multiple-antenna-systems-with-spatial-multiplexing-and-noncoherent-reception-2-320.jpg)
![fibers and has been proposed for use in RFID [14] as well as C-RAN [15], for
example.
However, our interest is in systems in which only noncoherent signals are
required so that the backhaul infrastructure for each antenna element can be
simplified. One difference between massive MIMO and DAS is that the channel
assumptions can be different. In massive MIMO it is often assumed that the uplink
channel gains to each of the antenna elements in the massive MIMO array are
identically distributed while in DAS they will be differently distributed due to the
large separation between uplink antennas. Correlation between antennas in DAS
will also be low, and in massive MIMO it is also often assumed low or nil [6]–[10]
and can be justified if the antenna spacings are large enough. In practice this
assumption may not always be appropriate. However, a setting with large number
of receive antennas could capture a cellular uplink where the receive antennas are
at the base station end which can afford bigger spacings and hence could result in
uncorrelatedness at the receive side. In this work, we focus on the massive MIMO
configuration so that we can assume uplink channel gains are independent and
identically distributed, taking correlation between elements to be negligible, to
perform performance analysis similar to other work in this area. In particular, we
show that when the general configuration of MIMO noncoherent detection based
on energy detection is utilized with hundreds of antennas employed at the receiver,
reliable communication is possible.](https://image.slidesharecdn.com/signaldesignformultipleantennasystemswithspatialmultiplexingandnoncoherentreception-150928052952-lva1-app6891/85/Signal-design-for-multiple-antenna-systems-with-spatial-multiplexing-and-noncoherent-reception-3-320.jpg)
![PROPOSED SYSTEM:
In our work we determine the signaling strategies and effective precoder for large
MIMO systems using spatial multiplexing and noncoherent detection in a flat
Rayleigh fading environment. We derive the expression for the symbol vector error
probability (SEP) using [20] and use this expression to determine the optimal
signal amplitude levels and effective precoder matrix. It is found that at high
values of average SNR per symbol vector per diversity branch, the SEP tends to
reach saturation as discussed at the end of the previous paragraph. To obtain the
best error performance, we need to find, for a given constellation and a given
channel transmit covariance matrix, the optimal precoder and constellation
parameters that minimize the saturation value of the SEP. This is particularly
difficult for MIMO because the combinations of signals present at each antenna are
directly related to the number of symbols and number of transmit antennas and
therefore optimization of the precoding matrix becomes nontrivial.
Therefore, for the optimization, we restrict ourselves to binary symbols with
two transmit antennas so that a tractable solution can be found. For the binary
constellations {0, 1} and {1,−r} with r > 1, two transmit antennas, a real valued
transmit covariance matrix with equal diagonal elements, and a diagonal precoder,](https://image.slidesharecdn.com/signaldesignformultipleantennasystemswithspatialmultiplexingandnoncoherentreception-150928052952-lva1-app6891/85/Signal-design-for-multiple-antenna-systems-with-spatial-multiplexing-and-noncoherent-reception-4-320.jpg)
Signal design for multiple antenna systems with spatial multiplexing and noncoherent reception
- 1. Signal Design for Multiple Antenna Systems With Spatial Multiplexing and Noncoherent Reception ABSTRACT: We consider signal design for multiple-input multipleoutput (MIMO) systems with spatial multiplexing and noncoherent reception in a flat Rayleigh fading environment.We obtain the symbol vector error probability (SEP) of noncoherent detection as a function of the signal amplitude levels and the effective precoder matrix, which depends on the precoder structure and the channel transmit covariance matrix. It is found that at high values of average signal-to-noise ratio (SNR) per symbol vector per diversity branch, the SEP tends to reach saturation. To obtain the best error performance, we need to find the optimal precoder and constellation parameters that minimize the saturation value of the SEP. Optimization of the saturation value of the SEP is carried out using the binary signal constellations {0,1} and {1,−r}, r > 1, two transmit antennas, a real valued transmit covariance matrix with equal diagonal elements, and a diagonal precoder. Results show that the optimal values of the parameters are almost independent of the number of receive antennas when the number of receive antennas is large. We also observe that the error performance of MIMO noncoherent reception is better
- 2. than that of MIMO coherent reception with imperfect channel state information for a certain range of average SNR. EXISTING SYSTEM: NONCOHERENT receivers with energy detection have received growing attention recently due to their possible use in applications ranging from sensor networks to short range interconnects. They can offer the possibility of low complexity receiver architectures and can also open up novel communication applications where energy detection is naturally present, such as visible light communication [1], [2] and harvesting of RF energy [3]. In conjunction with this, there has also been a growing interest in the use of antenna systems with hundreds of antennas in the form of large or massive multiple-input multiple-output (MIMO) or distributed antenna systems (DAS). There is a large body of work on massive MIMO including recent reviews [4], [5] as well as papers on performance analysis [6]–[8], detection [9], [10], and channel modeling [11]. On the other hand, in the area of DAS, recent work includes large-scale DAS for energy efficient communication [12] as well as distributed antennas in energy transfer and energy harvesting applications [13] including RFID [14]. In all these applications, if there is to be communication in the uplink, then it is necessary to bring the signals coherently back to a common point such as a base station. This can be performed by optical
- 3. fibers and has been proposed for use in RFID [14] as well as C-RAN [15], for example. However, our interest is in systems in which only noncoherent signals are required so that the backhaul infrastructure for each antenna element can be simplified. One difference between massive MIMO and DAS is that the channel assumptions can be different. In massive MIMO it is often assumed that the uplink channel gains to each of the antenna elements in the massive MIMO array are identically distributed while in DAS they will be differently distributed due to the large separation between uplink antennas. Correlation between antennas in DAS will also be low, and in massive MIMO it is also often assumed low or nil [6]–[10] and can be justified if the antenna spacings are large enough. In practice this assumption may not always be appropriate. However, a setting with large number of receive antennas could capture a cellular uplink where the receive antennas are at the base station end which can afford bigger spacings and hence could result in uncorrelatedness at the receive side. In this work, we focus on the massive MIMO configuration so that we can assume uplink channel gains are independent and identically distributed, taking correlation between elements to be negligible, to perform performance analysis similar to other work in this area. In particular, we show that when the general configuration of MIMO noncoherent detection based on energy detection is utilized with hundreds of antennas employed at the receiver, reliable communication is possible.
- 4. PROPOSED SYSTEM: In our work we determine the signaling strategies and effective precoder for large MIMO systems using spatial multiplexing and noncoherent detection in a flat Rayleigh fading environment. We derive the expression for the symbol vector error probability (SEP) using [20] and use this expression to determine the optimal signal amplitude levels and effective precoder matrix. It is found that at high values of average SNR per symbol vector per diversity branch, the SEP tends to reach saturation as discussed at the end of the previous paragraph. To obtain the best error performance, we need to find, for a given constellation and a given channel transmit covariance matrix, the optimal precoder and constellation parameters that minimize the saturation value of the SEP. This is particularly difficult for MIMO because the combinations of signals present at each antenna are directly related to the number of symbols and number of transmit antennas and therefore optimization of the precoding matrix becomes nontrivial. Therefore, for the optimization, we restrict ourselves to binary symbols with two transmit antennas so that a tractable solution can be found. For the binary constellations {0, 1} and {1,−r} with r > 1, two transmit antennas, a real valued transmit covariance matrix with equal diagonal elements, and a diagonal precoder,
- 5. we present the analysis to optimize, with respect to the precoder and constellation parameters, the saturation value of the SEP. These constellations are selected to represent ASK and binary phase-shift keying (BPSK) with the caveat that for BPSK, we choose r > 1 (instead of r = 1 since the constellation {1,−1} is not feasible), so that signal separation can be performed under noncoherent conditions. It should also be noted that for particular transmit correlations, even in the case of noncoherent energy detection, there will be a difference in the optimum performance of {1,−r} and {1, r} constellations. SOFTWARE IMPLEMENTATION: Modelsim 6.0 Xilinx 14.2 HARDWARE IMPLEMENTATION: SPARTAN-III, SPARTAN-VI