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Relay assisted systems have been of interest since 30 years ago. The main objective of these systems is to increase network coverage and reduce the transmitting power of source node. Although there are many protocols in such systems, the basic idea is that the source nodes forward their own information with the help of other node, which is defined as relay node, aiming to improving the network throughput, power and bandwidth efficiency [1-5].
Wireless relay-assisted networks have been given considerable attention recently due to great demand on ad-hoc networks, low-power sensor networks and satellite networks. Among all different relaying protocols, there are two types of very popular relaying scenarios, named decode-and-forward relaying which is also called regenerative protocols, and amplifier and forward relaying which is also called regenerative protocols. In Decoding and Forward system, the relay node fully decode all received signals, and then transmit the decoded signals to destination node. In Amplifier and Forward protocol networks, relay will not decode or regenerative the received signals thus can be realized with lower complexity. Relay in Amplifier and forward scheme may simply amplify and retransmit received signals or accomplish some detection (non-decoding) and preprocessing job before retransmitting (like in this paper) due to the different functions and complexity ,. Both of the two protocols have their own advantages depend on the channel situation. Frankly speaking, if the channel condition (or distance) between source node and relay node (SR channel) is much worse than that of the channel between relay node and destination node, the amplifier and forward protocol deliver a better performance than decode and forward protocol because the latter exposes severe error propagation. For example, the comparison of distribution allocation effect for both amplifier and forward (AF) and decode and forward (DF) protocols can be illustrated in Figure 1.1, 1.2, 1.3.
Figure 1.1 One-way Relay model
The relay system model can be explained as figure 1, where is the distance between Source node (S) and Relay node (R); is the distance between Relay node (R) and Destination node (D); is geometrical-gain between (S) and (R) while is geometrical-gain between (R) and (D):
where V is pathloss exponent of channel: for a free space pathloss, V=2; in a more practical indoor wireless setting, V is equal to 3 or more. Here we choose V=3 in our simulations and assume that the noise at (R) node and at (D) node always has the same power (at same power level). The parameters of the simulations are shown in Table 1 and the performance comparisons is displayed in Figure 2 and 3.
Table1 Parameters for BER comparison between AF and DF protocol in One-way Relay model
Wireless One-way Relay model
AF (dash line) and DF (solid line)
QPSK scheme with equal power allocation, which means transmit power in (S) and (R) are sameââ‚¬"half of the total transmit power; No channel coding
The distance between Source and Relay is shorter, hence we consider channel condition between Source and Relay is better
The distance between Relay and Destination is shorter, hence we consider channel between Relay and Destination is better
Figure 1.2 System BER comparison between AF and DF in Situation 1
Figure 1.3 System BER comparison between AF and DF in Situation 2
Figure 1.2 and 1.3 illustrates performance of AF and DF is relevant to channel conditions and the distribution of Relay node location. It is proved that with channel coding this becomes more closely associated .
Very recently, a lot of research has been proposed in the field of two-way relay protocols where paired nodes can share their information via a common node [7-8]. Furthermore, such systems can be extended to optimum scenarios where both source nodes (users) and independent relay nodes are equipped multiple antennas [9-10], and multiple pairs of source nodes aim to communicate with each other .
However, in the practical world, wireless networks may need to consist multiple users (source nodes) aiming to exchange their information with others in a more flexible way, which means the fixed relay node may not be suitable any more. Whatââ‚¬â„¢s more, in the near future, wireless networks need to consist much more users (source nodes) aiming to exchange or share their information with each other, so a two-way relay protocol may be not enough any more. In this case, a multi-way distributed relaying system with amplify and forward scheme is developed in this paper.
1.1 Motivation Example
We consider an example network which can be illustrated in figure 1.4.
Figure 1.4 Multi-Way Network Example
This wireless networks consist many users acting as multiple source nodes. Source nodes have more than one destination nodes for practical purpose and have been allocated bandwidth for their transmission. As we can see in figure 1.4, this is a very common wireless network example, which is applicable to many wireless setting, such a cellular networks, as hoc networks and higher level networks.
However, traditional two-way relay protocol has no longer satisfied the demand for such network transmission. The key point to solve this kind of problems is to make use of the broadcast nature of wireless terminals. Instead of transmitting user data independently to potential destination, we can jointly combine and process all the users (source node) information, and then broadcast the whole processed information to all the destination nodes. A novel high efficiency protocol based on amplifier and forward principle can be proposed in this system to realize such networks transmission, which should at least solve two basic problems:
Such protocol should at least contain one wireless medium served as relay node to gather and broadcast all the source information;
All the destination nodes in this network are able to distinguish information from different source nodes, and to reduce or limit the interference caused by multiple users.
To meet the first requirement (A) , an appropriate source node can be utilized as relay node to transmit all the information (including its own information) to all destination nodes. Thus, this protocol can be explained as distributed multi-way protocol.
To meet the second requirement (B), we can apply Code Division Multiple Access (CDMA) technical to effectively limit the interference caused by multiple users .
1.2 Code Division Multiple Access
CDMA is a very widely used multiple access technical in wireless communications, such as cellular communications and ad hoc networks where its merits as well as limitation are well studied in ,.
The basic idea of CDMA system is to separate different users by assigning a unique codeword to them. These codeword are so called spreading code which have two requirements:
Very good autocorrelation property which is similar to Gaussian noise
Very small cross-correlation property to reduce interference from others .
In this case, each userââ‚¬â„¢s information can be extracted from all other information at the receiver end.
According to different methods to spread spectrum, CDMA can be divided into four categories: DS-CDMA, MC-CDMA, TH-CAMA, and FH-CDMA, in which DS-CDMA (single carrier) and MC-CDMA (multi-carrier) are very widely used. In this paper, we only consider DS- CDMA (Direct-Sequence CDMA) .
DS-CDMA system is a spectral efficient system which can limit the interference from multiple access (MAI) and provide reliable communications for multiple users at a QOS level. So it is suitable for our proposed multiuser multi-way relay network.
However, due to different wireless surroundings and different amount of users, multiple user interference (MUI) is still one of the key factors to affect system performance. Hence, high efficiency, low complexity design to cancel MUI is a very important issue for such systems and also a focus of this paper.
1.3 Channel Model
In most wireless settings, Nakagami-m multipath channel provide the most suitable channel model for practical channel simulation. Therefore, in this paper, we will only consider channel model as Nakagami-m channel.
The fading amplitude of Nakagami-m fading channel obeys Nakagani-m distribution, which is a generalized distribution with the probability density function (PDF) :
where is the gamma function, is the mean fading power, and m is Nakagami fading parameter, which is equal to .
We can always change the parameter m in the Nakagami-m distribution to get a good fit to all different channel condition in wireless communications. In Nakagami fading channel, different m gives different severity of the fading. For example, m=0.5 correspond to the worst channel condition: so called one-side Gaussian fading; when , perfect channel condition will appear: conventional Gaussian channel; when m>1, Rician distributions can be closely approximated; when m=1, the fading channel changes into most common wireless channel: Rayleigh fading channel , whose PDF function is shown below:
where is the mode of Rayleigh distribution.
In this paper, we will always consider Rayleigh fading channel as our basic channel if not specialized.
1.4 Multiple-Input Multiple-Output
In a wireless environment, multi-path fading channel makes it difficult for the receiver to determine the transmitted signal reliably. Recent works on wireless networks consider the exploitation of spatial diversity using Multiple-Input Multiple-Output (MIMO) channels in the network to combat fading and improve system performance .
Figure 1.5 MIMO Schematic
MIMO systems are recently very popular wireless communication systems with transmitting or receiving end equipped with multiple antennas as shown in figure 1.5. Such systems create spatial diversity in the process of communication which can significantly improve Bit Error Rate (BER) of the transmission .
MIMO diversity scheme can be achieved by transmitting the same signal from geographically sufficiently separated transmit antennas, which can assist the receiver to generate several copies experiencing independent channel fading. Such spatial diversity is called transmit diversity, which mainly depends on the transmitter preprocessing. MIMO diversity can also be achieved by generating observations of the same transmitted signal from geographically sufficiently separated receive antennas. Such diversity is called receive diversity.
In order to achieve transmit diversity, the transmitter of a MISO system needs to carry out certain preprocessing, so that the receiver can form the decision variables. Therefore, transmit diversity can be classified into closed-loop transmit diversity and open-loop transmit diversity.
There are several techniques can be employed in receive diversity, such as Equal Gain Combining (EGC), Selection Combining (SC) and most frequently used Maximun Ratio Combining (MRC) .
More attentions concentrate on efficient ways to create transmit diversity , such as STBC (Space-Time Block Codes) and Quasi-orthogonal STBC, STTC (Space-Time Trellis Codes), STS (Space-Time Spreading) and so on. The merits and tradeoff of different transmit techniques are still being widely developed.
STS, proposed by Hochwald, is applied for the downlink of Wideband Code Division Multiple Access (WCDMA), which may achieve the highest possible transmit gain.
In this paper, STS technique based on STBC and Quasi-orthogonal STBC will be widely applied in our distributed amplify and forward relay networks to improve system performance.
1.5 Outline of the Report
This project report is organized as follows:
Chapter 2 The basic distributed multi-way AF relay system model is exploited in detail in this chapter. Two timeslots transmission including relay processing is derived and delivered. Since DS-CDMA is utilized in this model, the transmission and detection of DS-CDMA are presented briefly. The BER performance of the whole system in different situations are delivered and analyzed in the end.
Chapter 3 RMD-MMSE MUD ,which is an advanced low complexity optimum CDMA system detector , is discussed and employed in this basic system model to efficiently cancel the MUI from multiple users and improve system performance. The BER performance of RMD-MMSE MUD and system model is analysis and compared in different situations in detail.
Chapter 4 A cooperative model of distributed multi-way AF relays system with low complexity using the principle of STS and Quasi-orthogonal STS is proposed and derived in detail. Multi-way system applying space-time diversity gives a much better performance without decreasing system throughput according to simulation results. Furthermore, a motivated cooperative model with higher complexity and better performance is also demonstrated and the BER performance are displayed and compared.
Chapter 5 Basic multi-way relay network model is extended in another way with the help multiple antennas based on STS. Such new model is derived and demonstrated and gives a satisfied system performance according to system BER performance, which are compared and analyzed systemically with all other models.