Three Different Types Of Combining Methods Computer Science Essay

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In wireless transmission the signal quality suffers occasional, severe degradations due to effects like fading caused by multipath propagation. To reduce such effects, diversity, as proposed by [1], can be used to transfer the different samples of the same signal over essentially independent channels. There are several approaches to implement diversity in a wireless transmission. The most common on is using the 'multiple antennas' which is used to achieve space diversity. But multiple antennas cannot always be implemented on small terminals or the destination is just too far away to get a good signal-quality, especially when power consumption is an important criteria. Another interesting approach, as proposed in [2] might be to build an ad-hoc network using another mobile station.

In such a system combinations of several relaying protocols and different combining methods are examined to see their effects on the performance.

Basically three different types of combining methods are examined which differs in the knowledge of the channel quality they need to work.

One combination that achieves a good performance is then used to see the effect on the performance depending on the location of the relay. This information is crucial to decide the worth of a mobile relay.

Back ground.

To decrease the multipath propagation loss multiple input multiple output (MIMO) concept is used. Wireless communication using multiple-input multiple-output (MIMO) systems enables increased spectral efficiency for a given total transmit power. Increased capacity is achieved by introducing additional spatial channels that are exploited by using space-time coding. In this article, we survey the environmental factors that affect MIMO capacity. These factors include channel complexity, external interference, and channel estimation error. We discuss examples of space-time codes, including space-time low-density parity-check codes and space time turbo codes, and we investigate receiver approaches, including multichannel multiuser detection (MCMUD). The 'multichannel' term indicates that the receive incorporates multiple antennas by using space-time-frequency adaptive.

The advantages of multiple receive antennas, such as gain and spatial diversity, have been known and exploited for some time, the use of transmit diversity has only been investigated recently. The advantages of MIMO communication, which exploits the physical channel between many transmit and receive antennas, are currently receiving significant attention.

MIMO systems provide a number of advantages over single-antenna-to-single-antenna communication. Sensitivity to fading is reduced by the spatial diversity provided by multiple spatial paths. Under certain environmental conditions, the power requirements associated with high spectral-efficiency communication can be significantly reduced by avoiding the compressive region of the information-theoretic capacity bound. Here, spectral efficiency is defined as the total number of information bits per second per Hertz transmitted from one array to the other. But generously MIMO cannot be used to for Wireless devices. So new concept came into existence called Cooperative communication

Cooperative communication

Wireless devices cannot implement multiple input multiple output due to hardware or size limitation. So wireless device will do replicate their single antenna virtually to act like MIMO system. All this process is possible through cooperative communication.

In cooperative communication, single antenna's terminals cooperate thus create 'virtual MIMO system'. Somehow, the signals received at destination are replicas of same signals degraded by different channel. On the other hand, In MIMO, the receiver demux the signals received to recover the original. 

However, in reality we do not have real MIMO, so to apply MIMO we use virtual MIMO through cooperation communication. I give you one example: A->B, C (B, C is different terminals). At B and C, the signal is amplified (AF) or decoded (DF) and then transmits to D (B, C --->D). At D, the signal will be combined with use combiner to select the best signal.

System diagram

The above diagram shows the single user model .Here to know the efficiency of the system only the single model is taken. In the diagram Sender(S), relay(R) and Destination (D) are shown.


At the sender the data is transferred as a random bipolar bit sequence which is either modulated with Binary Phase Shift Keying (BPSK) or Quadrature Phase Shift Keying (QPSK). QPSK in fact consists of two independent (orthogonal) BPSK systems and therefore has double bandwidth compared to BPSK. Without any loss of generality the simulations are done in the baseband.


At the destination Combiner is used is shown in diagram. At the combiner different combining methods like MMSE Combination, Optimum Combination, Maximum Ratio Combination, Selective diversity Combination, Equal Ratio Combination, Fixed Ratio Combining, Signal to noise ratio combining. Among this only Maximum Ratio Combination, Equal Ratio Combination and Signal to noise ratio Combination will be used and compared.

Maximum Ratio Combination:

On the receive antenna, the received signal is,


is the received symbol on the receive antenna,

is the channel on the receive antenna,

is the transmitted symbol and

is the noise on receive antenna.

Expressing it in matrix form, the received signal is,

, where

is the received symbol from all the receive antenna

is the channel on all the receive antenna

is the transmitted symbol and

is the noise on all the receive antenna.

Equal Ratio Combination: This is the simplest combining method, which should only be used if there is no information about the channel quality available or the computing capacity is extremely limited. The incoming signals are just added up before the symbols are detected.

Note that you do not need information about the quality but about the phase shift of the signal which occurs due to fading

[n] = + .

The parameters and denotes the incoming signal from the sender and the relay.

Signal to noise ratio Combination

An even better performance can be achieved when precise information about the current state of the different channels is known. An often used value to characterize the quality of a link is the SNR, which is used to weight the received signals.

[n] = + .


The relay is the mobile station which is place between the sender and the Destination .The performance of the system will be calculated with the position of the relay .That means the distance of the relay between the source and the destination. Which distance gives the good performance to the system?

At the relay different protocol are used like Amplify and forward and second one is Decode and forward protocols are used.

Amplify and forward

For amplify-and-forward transmission, the source terminal transmits its information as xs[n], say, for n = 1……N/4. During this interval, the relay processes yr[n], and relays the information by transmitting.

Xr[n] = β yr [n -N/4];

For n = N/4 + 1…... N/2. To remain within its power constraint (with high probability),

an amplifying relay must use gain

Allow the amplifier gain to depend upon the fading coefficient between the sources and relay. This transmission scheme can be viewed as repetition coding from two separate transmitters, except that the relay transmitter amplifies its own receiver noise. The destination can decode its received signal yd[n] for n = 1………; N/2 by first appropriately combining the signals from the two sub blocks using a suitably designed matched-filter (maximum-ratio combiner).

Decode-and-Forward Transmission

The source terminal transmits its information as xs[n], say, for n = 0…………..N/4. During this interval, the relay processes yr[n] by decoding an estimate ^xs[n] of the source transmitted signal.

Under a repetition coded scheme, the relay transmits the signal

Xr[n] = ^xs [n - N/4] for n = N/4 + 1……….; N/2.

Decoding at the relay can take on a variety of forms. For example, the relay might fully decode the source message by estimating the source codeword, or it might employ symbol by-symbol decoding and allow the destination to perform full decoding. These options allow for trading of performance and complexity at the relay terminal. Because the performance of symbol-by-symbol decoding varies with the choice of coding and modulation, we focus on full decoding in the sequel; symbol-by-symbol decoding of binary transmissions has been treated from uncoded perspective

Adaptive Protocols

Decode-and-forward is limited by direct transmission between the source and relay. However, since the fading coefficients are known to the appropriate receivers can be measured to high accuracy by the cooperating terminals; thus, they can adapt their transmission format according to the realized value of

This observation suggests the following class of adaptive algorithms. If the measured falls below a certain threshold, the source simply continues its transmission to the destination, in the form of repetition or more powerful codes. If the measured lies above the threshold, the relay forwards what it received from the source, using either amplify and-forward or decode-and-forward, in an attempt to achieve diversity gain. Adaptive protocols of this form should offer diversity because in either case, two of the fading coefficients must be small in order for the transmission to be lost. Specifically, if is small, then must also be small for the transmission to be lost when the source continues its transmission. Similarly, is large, then both and must be small for the transmission to be lost when the relay employs amplify-and-forward or decode and forward

But in this paper only two first protocol are used at the relay. That is Amplify and forward and the second one is Decode and forward. The performance of the protocols with different combination method may be observed.


In a wireless network, the data which is transferred from a sender to a receiver has to propagate through the air .During propagation several phenomena will distort the signal. Within this paper, thermal noise, path loss and the Rayleigh fading are considered. Path loss and fading are multiplied and noise is added.

Noise: The main sources of noise in a wireless network are interference and electronic components like amplifiers. If the latter dominates, thermal noise can be assumed, which can be characterized as additive complex Gaussian noise. The scalar can then be simulated as the sum of a real and an imaginary noise vector, both Gaussians distributed, mutually independent and zero mean with variance. The total noise power will be = 2

Path loss and fading

The signal is attenuated mainly by the effects of free-space path loss and fading, both included in


The path loss (assuming a plane-earth model) is proportional to 1/

As long as the distance between the sender and receiver does not change too much ,it can be assumed to be constant for the whole transmission .The power of the received signal is attenuated proportional to 1/.

In a wireless network it occurs quite often that the line -of-sight link is blocked. Instead of this direct connection, the signal will propagate to the sender on many different ways. This occurs especially in an urban environment, where buildings prevent a line-of-sight link but enable various different ways for indirect connection by reflecting the propagating signal. The above effect is referred to as multi-path propagation.

Only small changes in the whole system change the characteristic of the channel. The signal is altered by attenuating and by adding a phase shift to it. Effect is known as fading.

The fading coefficient can be modeled as a zero mean, complex Gaussian random variable with variances This means that the angle d is uniformly distributed on [0; 2) and the magnitude , is Rayleigh distributed [2]. This Rayleigh distributed magnitude can have a bad effect on the signal quality at the receiver. In this paper the Rayleigh fading channel is considered.