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Informatik

Jaypee Institute of Information Technology, Noida - JIIT

2011

Antonia S. ©
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ID# 27839







Minor report on MIMO-ODFM - multiple input multiple output- orthogonal frequency division multiplexing

ABSTRACT


In this new information age, high data rate and strong reliability features in wire-less communication systems are the dominant factor for a successful deployment of commercial networks. MIMO-OFDM (multiple input multiple output- orthogonal frequency division multiplexing), a new wireless broadband technology, has gained great popularity for its capability of high rate transmission and its robust-ness against multi-path fading and other channel impairments.


A major challenge to MIMO-OFDM systems is how to obtain the channel state information accurately and promptly for coherent detection of information symbols and channel synchronization.The multiuser MIMO-OFDM system has great potential of providing enormous capacity due to its integrated space-frequency diversity and multiuser diversity.


This project report is based on a novel method to enable concurrent communications in MIMO networks. This method enables an 802.11n MIMO-OFDM receiver to decode independent data streams from two independent 802.11n transmitters concurrently. The method I have discussed in this report, namely the MATRIX OPERATION, enables a single 802.11n based MIMOOFDM receiver to decode data streams from two independent 802.11n based transmitters simultaneously with a very slight modification on the existing PHY layer of 802.11n testbeds.


It has been well studied that independent data streams can be sent from a multi-antenna transmitter and decoded by multi-antenna receiver using spatial multiplexing techniques. This idea is adopted in the 802.11n standard to increase the single link throughput.

Similarly, independent data streams that are transmitted from independent transmitters can also be decoded simultaneously at the multi-antenna receiver, since this situation is essentially equivalent to the former case. However, the latter process has two major practical challenges if it is desired to be implemented on 802.11n nodes; timing and the carrier synchronization between the independent transmitters.


INTRODUCTION


MIMO


MIMO stands for Multiple-Input Multiple-Output. In communication systems, this usually means that several transmitting and receiving antennas are employed at both the transmitter and receiver to improve communication performance. It is one of several forms of
smart antenna
technology.


 Antenna Arrays


A special case of MIMO systems are antenna arrays that have been in use for a long time

·        Several antennas can be used with a specific phase and amplitude setting to transmit the same signal. This setup produces a higher gain in a certain direction and is called beamforming.

·        It also increases the diversity of the channel. If there is negative interference of the signal transmitted from one of the antennas at the receiver, then there is a high probability that at least one signal transmitted from another antenna of the array is decodable.Using antenna arrays does neither increase the used bandwidth nor does it decrease the throughput of data .                                                                            


                                                                                        


 MIMO CHANNEL MODEL


Wireless channels limitations


Wireless transmission introduces following factors-


·        Fading: multiple paths with different phases add up at the receiver, giving a random (Rayleigh/Ricean) amplitude signal.


·        ISI: multiple paths come with various delays, causing intersymbol Interference.

·        CCI: Co-channel users create interference to the target user

·        Noise: electronics suffer from thermal noise, limiting the SNR.

FORMS OF MIMO

SISO/SIMO/MISO is
degenerate
cases of MIMO

·       Multiple-input and single-output (MISO) is a degenerate case when the receiver has a single antenna.

·       Single-input and multiple-output (SIMO) is a degenerate case when the transmitter has a single antenna.

·       Single-input single-output
(SISO) is a radio system where neither the transmitter nor receiver has multiple antennas.

·       Some limitations are also there like the physical antenna spacing is selected to be large; multiple
wavelengths
at the base station. The antenna separation at the receiver is heavily space constrained in hand sets, though advanced antenna design and algorithm techniques are under discussion.

MIMO Benefits


• Higher capacity (bits/s/Hz) (spectrum is expensive; number of base stations limited).

• Better transmission quality (BER, outage)

• Increased coverage

• Improved user position estimation

Due to


·        Spatial multiplexing gain

Capacity gain at no additional power or bandwidth consumption obtained through the use of multiple antennas at both sides of a wireless radio link.


·        Diversity gain

Improvement in link reliability obtained by transmitting the same data on independently fading branches

Ø  Array gain

Ø  Interference reduction


FUNCTIONS OF MIMO

MIMO can be sub-divided into three main categories, Precoding, spatial multiplexing  and diversity coding.

·       Precoding

It is multi-stream beamforming, in the narrowest definition. In more general terms, it is considered to be all spatial processing that occurs at the transmitter. In (single-layer) beamforming, the same signal is emitted from each of the transmit antennas with appropriate phase (and sometimes gain) weighting such that the signal power is maximized at the receiver input.

The benefits of beamforming are to increase the received signal gain, by making signals emitted from different antennas add up constructively, and to reduce the multipath fading effect. Precoding requires knowledge of channel state information (CSI) at the transmitter.

·        SPATIAL MULTIPLEXING

It requires MIMO antenna configuration. In spatial multiplexing, a high rate signal is split into multiple lower rate streams and each stream is transmitted from a different transmit antenna in the same frequency channel. If these signals arrive at the receiver antenna array with sufficiently different spatial signatures, the receiver can separate these streams into (almost) parallel channels.

Spatial multiplexing is a very powerful technique for increasing channel capacity at higher signal-to-noise ratios (SNR). The maximum number of spatial streams is limited by the lesser of the number of antennas at the transmitter or receiver. Spatial multiplexing can be used with or without transmit channel knowledge. Spatial multiplexing can also be used for simultaneous transmission to multiple receivers, known as space-division multiple access.

·        Diversity Coding

These techniques are used when there is no channel knowledge at the transmitter. In diversity methods, a single stream (unlike multiple streams in spatial multiplexing) is transmitted, but the signal is coded using techniques called space-time coding. The signal is emitted from each of the transmit antennas with full or near orthogonal coding.

Diversity coding exploits the independent fading in the multiple antenna links to enhance signal diversity. Because there is no channel knowledge, there is no beam forming or array gain from diversity coding.

SPATIAL MULTIPLEXING


The main difficulty today is that users demand higher data rates for their applications whereas the usable spectrum is limited (both technically and by regulations). This is due to the increase in the popularity of mobile applications as for example cell phones or wireless internet access.

Wireless systems do not provide the option of just adding an additional cable as in wire or fibre optics based systems. Therefore, the spectral efficiency needs to be increased in order to enable a higher throughput. But customers do not only want fast data access - this access also needs to be reliable (QOS - quality of service)

The idea seems fairly trivial but sending signals in the same frequency band over a common channel is generally not possible. This is because the signals interfere with each other and cannot be easily decoded at the receiver.


In most applications, every signal sent from a transmitting antenna reaches the receiving antenna over multiple paths. This phenomenon called multipath propagation is produced by electromagnetic waves that are reflected off walls and other objects. The signal arriving at the receiver is therefore generally a superposition of scaled and delayed versions of the original signal.


Instead of seeing multipath propagation as a factor that decreases the system performance; clever approaches use it as an advantage in MIMO systems. One can imagine the following setup:


• Transmitter using antennas T1 and T2

• Receiver using antennas R1 and R2

• T1 and T2 transmit different signals

• Both are placed inside a building - assuming no LOS for simplicity


A signal sent from T1 and received at R1 follows a different path compared to the signal sent from T2 and received at R2. The same is true for the signals from T1 to R2 and from T2 to R1. If one assumes that the different paths are known at the receiver, clever calculations can remove the effect of the superposition and decode both streams. In that case, the data rate would have been doubled without using additional spectrum.

Due to the spatial distribution of the antennas, the reliability of the link should be increased at the same time. The major concern is how to obtain the channel matrix .This is generally done in a training phase where known signals are sent by the transmitter.

This does of course decrease the overall throughput as no real information is transmitted during that phase. This loss is generally smaller than the additional capacity gained by using a second stream. This procedure is called spatial multiplexing.

Ø  Spatial multiplexing has been generally used to increase the capacity of a MIMO link by transmitting independent data streams in the same time slot and frequency band simultaneously from each transmit antenna, and differentiating multiple data streams at the receiver using channel information about each propagation path.


Ø  Benefits

·         It does not require any additional power.

·         No additional bandwidth requirement.

Ø  WORKING

In the above figure it has been shown that when we have M= 3 number of antennas in the transmitting side and have K (a1, a2, a3, a4, a5, a6) = 6 bits for sending. At first divide the bits into M=3 sub streams of data {(a1, a3), (a2, a4), (a3, a6)} and then multiply each sub stream of data with three carrier frequency in order to transmit them via three separate antennas.

Now at the receiving end each sub-stream will have three spatial signatures-that means total 9 spatial signature will be at the receiving antenna-due to the multipath environment each sub stream will have its own spatial signature. Based on this spatial signature sub-streams of data will be demultiplexed and decoded in order to get back the original data stream-this is how spatial multiplexing works.

ØSPATIAL DIVERSITY

In contrast to spatial multiplexing, the purpose of spatial diversity is to increase the diversity order of a MIMO link to mitigate fading by coding a signal across space and time so that a receiver could receive the replicas of the signal and combine those received signals constructively to achieve a diversity gain.

The main idea behind diversity is that when several copies of the same signals are passed through different channels then they experience independent fading of each other-there will be high probability that some signals will undergo deep fades while other may not. When these signals reach the receiver then there will be significant energy to make a decision that what was actual sent.

Application of spatial diversity: This technique is very effective in frequency selective and time selective fading channels.


ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING


Orthogonal Frequency Division Multiplexing (OFDM) is one of the most promising physical layer technologies for high data rate wireless communications due to its robustness to frequency selective fading, high spectral efficiency, and low computational complexity. OFDM can be used in conjunction with a Multiple-Input Multiple-Output (MIMO) transceiver to increase the diversity gain and/or the system capacity by exploiting spatial domain.

Because the OFDM system effectively provides numerous parallel narrowband channels, MIMO-OFDM is considered a key technology in emerging high-data rate systems such as 4G, IEEE 802.16, and IEEE 802.11n.




Why using OFDM    ?

The standard approach to modulate information onto a carrier is by varying the frequency, the phase or the amplitude. As the data rate increases, the time a single symbol (one or several bits) is “on air” is decreased.

In case of impulse noise or other short period noise with high energy, it is likely that a symbol gets distorted to such a high extent that it cannot be recovered. The shorter the period in which the symbol is available, the higher is the probability that the symbol is fully destroyed by bursts of noise.


Frequency Division Multiplexing


To solve this problem, one can use frequency division multiplexing (FDM). Instead of using a single carrier that occupies the whole available frequency band, several subcarriers are employed within the available frequency band.

The data stream is distributed over all available subcarriers. This increases the symbol period and therefore decreases susceptibility to noise bursts. It also adds additional immunity to narrow banded noise; as such noise only affects several of the subcarriers and not the entire signal.

FDM comes at the cost of a lower data rate as a guard interval has to be inserted between the different subcarriers and therefore a part of the available frequency spectrum is wasted. FDM also adds some complexity to the hardware by using several streams. At the same time it also removes some of the complexity by slowing down the bit rate of each subcarrier.


If one can choose a set of subcarriers that are orthogonal to each other, then there is

No need to use a guard interval to separate the subcarriers. This would increase the spectral efficiency of the system.


Two signals u(t) and v(t) are said to be orthogonal to each other if-

ISSUES IN OFDM


Ø  In a multipath channel, several delayed versions of the original signal appear at the receiver. One speaks of intersymbol interference (ISI) if a consecutive OFDM-symbol gets distorted by the previous one.

In a general case, only the first few samples of the signal get distorted. The problem can be solved by waiting a specific time between transmitting two consecutive symbols. This guard interval (in time domain) is depending on the channel.


Ø  The other problem is that a single OFDM symbol can interfere with itself. This is called intrasymbol interference. The reason is the following: A convolution in time domain is equivalent to a multiplication in the frequency domain if the signal is either periodic or infinitely long.


Using a cyclic prefix leads to a significant simplification of the receiver: Instead of having to remove a convolution in time (between the signal and the channel), it is only necessary to remove a multiplication in frequency domain.


NOISE CONSIDERATIONS


The most common noise source in a wireless system is thermal noise - usually manifesting itself as Additive White Gaussian Noise (AWGN). As the noise spectrum is uniform in the frequency domain, this kind of noise has the same impairment on the overall system as it has in a single carrier system.

Another common type of noise is impulse noise. This type of broadband noise is generally only present during a short period. As described before, the OFDM system performs better under impulse noise than a single carrier system. Colored noise is difficult to handle as it doesn’t have a constant spectrum as AWGN. A simple solution for high noise environments is to lower the data rate.

 

CHANNEL MODELS

 

If there are other systems present, carrier interference can occur. An OFDM system can handle that by disabling the affected subcarriers. Another type of imperfection emerges from the local oscillator. There are two effects that have to be considered: Phase noise (sometimes called phase jitter) and the frequency offset.

 

The frequency offset of an oscillator can be understood as the average frequency of the oscillator. This frequency is generally slightly different from the expected frequency. Clock quality, temperature and other effects are generally responsible for this offset. A solution to this problem is to introduce pilot subcarriers for synchronization. It has to be noted that introducing pilot subcarriers affects the maximum data rate negatively.


ØNotation

The following notation will be used:


·         x(t) signal leaving the transmitter (time domain)

·         X signal vector in frequency domain: Input of IFFT

·         y(t) signal reaching the receiver (time domain)

·         Y received signal vector in frequency domain: Output of FFT

·         h(t) channel impulse response (time domain)

·         H channel response matrix in frequency domain

·         n(t) additive noise (time domain)

·         N noise

·         T total number of transmitting antennas

·         Trnumber of the transmitting antenna

·         R total number of receiving antennas

·         r number of the receiving antenna

·         C total number of subcarriers

ØSISO Channel Model

The simplest possible system is a SISO (Single-Input, Single-Output) system. In time domain, it can be written as:


This is equivalent to the following notation in the frequency domain:


ØOFDM Channel Model for C Channels (SISO)


An OFDM system can be represented by the following model in frequency domain:


Each line corresponds to one of the orthogonal tones.


ØMIMO Channel Model for a 4×4 Systems


To simplify the notation for a MIMO system with T transmitting and R receiving Antennas, it is assumed that T = R = 4. Such a general setup is shown below in the figure. It is straight forward to change the number of transmitting or receiving antennas.


A standard approach for a MIMO system with 4 transmitting and 4 receiving antennas


ØMIMO-OFDM Channel Model


As can be seen from the SISO OFDM channel model, the different OFDM sub channels can be treated separately. This allows formulating a simple model for a MIMO-OFDM system: The whole system can be seen as a stack of C different MIMO systems. A graphic showing such a system is presented in figure shown below.




WORKING OF MIMO-OFDM

Like any other communication system MIMO-OFDM system also has transmitter and receiver but the antennas are more than one both at transmit and receive end. MIMO system can be implemented in various ways, if we need to take the diversity advantage to combat fading then we need to send the same signals through various MIMO antennas and at the receiving end all the signals received by MIMO antennas will receive the same signals traveled through various path.

In this case the entire received signal must pass through un-correlated channels. If we are inserted to use MIMO for capacity increase then we can send different set of data (not the same set of data like diversity MIMO) via a number of antennas and the same number of antennas will receive the signals in the receiving end. For MIMO to be efficient antenna spacing need to be done very carefully- at least half the wave length of the transmitting signal.

MIMO OFDM SYSTEM


Limitations of MIMO-OFDM

·         Antenna spacing must be appropriate depending on the type of channels


SIGNAL PROCESSING TECHNIQUE TO ENABLE CONCURRENT COMMUNICATION IN MIMO NETWORKS


From the above discussions it has been clear that MIMO systems provide improved capacity for a single link due to their spatial multiplexing gain, diversity gain, and interference mitigation abilities. Among these benefits, interference mitigation and spatial multiplexing can be used to enable concurrent communications with the purpose of increasing the aggregate throughput in a MIMO enabled network.


It has been well studied that independent data streams can be sent from a multi-antenna transmitter and decoded by multi-antenna receiver using spatial multiplexing techniques. This idea is adopted in the 802.11n standard to increase the single link throughput.

Similarly, independent data streams that are transmitted from independent transmitters can also be decoded simultaneously at the multi-antenna receiver, since this situation is essentially equivalent to the former case. In this method that we present in this paper, namely the matrix operation, enables a single 802.11n based MIMO-OFDM receiver to decode data streams from two independent 802.11n based transmitters simultaneously with a very slight modification on the existing PHY layer of 802.11n testbeds.


The idea of achieving concurrent communications with MIMO enabled nodes has been adopted by some MAC protocols with the purpose of increasing the aggregate throughput of the network.


MATRIX OPERATION

The matrix operation is defined as synchronous, independent data transmission from two 802.11n based MIMO-OFDM transmitters to a single 802.11n based MIMO-OFDM receiver. The two transmit nodes (clients) are equipped with 2 antennas each while the receive node (base station) is equipped with 4 antennas.

This scenario is illustrated in Fig. 1. Assuming two client nodes are sending independent data streams, the objective of the matrix operation is to simultaneously decode independent data streams from these two independent transmitters. Theoretically, this situation is equivalent to a single 4 × 4 MIMO link where the transmitter has two pair of antennas each located at different locations.


FIG 1: MATRIX OPERATION SYSTEM MODEL


IMPLEMENTATION CHALLENGES


·         Timing synchronization: Two client nodes need to coordinate the onset of their transmissions.



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