Application of the spatial division multiplexing technique in cooperative mimo systems

Cooperative MIMO is a combination technique between the single antenna cooperation communications and multipleinput multiple-output systems to achieve the advantages of traditional MIMO. In this paper, we focus on model that combines the spatial multiplexing technique and the cooperative communications, with relay nodes using decode and forward technique where source node and the relay nodes have only one antenna, destination node has multiple antennas; and relay nodes use amplify and forward technique to reduce power consumption and suitable for compact devices; and destination node uses zero forcing (ZF) algorithm. Finally, we show our simulation results in applying the spatial division multiplexing technique in cooperative mimo systems.


ABSTRACT:
Cooperative MIMO is a combination technique between the single antenna cooperation communications and multipleinput multiple-output systems to achieve the advantages of traditional MIMO.In this paper, we focus on model that combines the spatial multiplexing technique and the cooperative communications, with relay nodes using decode and forward technique where source node and the relay nodes have only one antenna, destination node has multiple antennas; and relay nodes use amplify and forward technique to reduce power consumption and suitable for compact devices; and destination node uses zero forcing (ZF) algorithm.Finally, we show our simulation results in applying the spatial division multiplexing technique in cooperative mimo systems.

INTRODUCTION
Nowadays, the demand of using broadband services and high-speed wireless platform is growing very fast, so the radio spectrum resources are running out.To overcome the issue, the multiple-input multiple-output (MIMO) technique, which uses multiple antennas at the transmitter and the receiver, is a promising technique to meet the demand to improve the quality and channel capacity of systems without increasing the transmit power and the frequency bandwidth

Spatial division multiplexing (SDM) technique
applying to MIMO systems, as shown in Fig. 1 performs the split of the transmit information bit into smaller sequence, and then transmits signals independently and simultaneously with same data resources on transmit antennas.So, this technique helps to increase system capacity.The purpose of cooperative communication techniques that improve the quality of transmit signal from the source node to the destination node, specifically through improved BER at the receiver of the system.Thus, the signal processing techniques need to be selected and combined so that the system gain levels of maximum diversity.

PROPOSED MODEL OF COOPERATIVE MIMO SYSTEMS
In the previous section, we can find that the benefits of spatial multiplexing can be achieved with the terminal has only one antenna, and known as Cooperative Spatial Multiplexing (CSM).In this section, we propose the CSM model as shown in Fig. 3.  (3) Thereforce, when increasing the transmit power PS will increase the reliability of the SR channel, but reduces the power allocated to the relay nodes leading to the restoration of the signal at the destination nodes less reliable.Conversely, reducing the capacity of power PS allow power allocated to the relay nodes increases, but the poor quality on the SR channels.So we expect that there exists a pair of PS and PR values so that the optimal minimum error probability at the destination nodes (ie, maximizing the quality of the system).
The receive signal at the jth antenna (j=1, 2, …, M) of the destination node is expressed as follows: (4) where dDR is distance between relay node Ri  The system equation can be expressed as follow: =+ y Hx z (16) From receiver vector y, the receiver using the detector to detect transmit signal vector x .We have many detection algorithms for detect signal at receive antenns such as: Zero forcing (ZF), Minimum Mean Square Error (MMSE), etc.

THE SIMULATION RESULTS OF SYSTEMS
We perform simulation and evaluation of the system quality through BER and capacity parameters of AF-CSM system under many different conditions such as total transmit power constraints, changing the relative position of the relay nodes and power allocation between the source node and the relay node.In addition, the quality of AF-CSM system is compared with other systems such as SISO, traditional V-BLAST, and especially CSM system using DF at the relay node.For fair and accurate, the totaltransmit power of all system and are assumed to be P.

Fig. 4. BER Comparison of AF-CSM with other systems
A comparison of BER performance between AF-CSM system with other systems is shown in Fig. 4. It can be seen that the quality of the AF-CSM system is better than other ones.However, when the parameters unchanged and applied to the power distribution system of CSM, we can see the quality of the AF-CSM system is significantly improved and better SISO.The quality AF-CSM system when applying the optimal power allocation to minimize BER and change the position of the relay nodes is shown in Fig. 6.We can see that the quality of our system improve gradually when reduce the distance between source node and the relay node.
Especially, when distance is 0.1 (normalized distance) , the quality of the AF-CSM system better than traditional V-BLAST.Thus, it can be concluded that channel quality between source node and the relay nodes decides significantly to the quality of the whole system.Ứng dụng kỹ thuật ghép kênh phân chia theo không gian trong hệ thống MIMO hợp tác [1][2].However, the implementation of MIMO systems on mobile terminals (referred to as MS) has to solve many challenges such as small size, limited energy, channel correlation,[3].There are many previous research works focusing on spatial diversity to increase quality, but rarely consider the increase of the system capacity [4].Therefore, the purpose of our paper research is to examine the model combining the spatial division multiplexing technique and the Trang 6 single antenna cooperative communications to create virtual spatial multiplexing MIMO systems [4-7].In the paper, we also mention about the optimum power allocation (between the source node and the relay nodes), in order to maximize the quality of the systems [8].This paper is divided into five parts as followings.After a brief introduction, an overview of the spatial division multiplexing technique and cooperative MIMO systems is described in section II.In section III, we present the model of cooperative MIMO systems using the spatial division multiplexing techniques.The results and discussion of our model will be shown in section IV. Conclusions are presented in final part.

Fig. 2 .
Fig. 2. Cooperative Communication model Cooperative process divided into two orthogonal phase (to avoid interference between two phases) as follows: + Phase 1: The source node sends information to

Fig. 3 .
Fig. 3. Cooperative spatial multiplexing model The specification of CSM model as follow: + A source node, a destination node and N relay nodes.+ Source node and relay nodes have only one antenna at each node, create a virtual antenna array In the next phase, all of the Relay nodes will be amplified and forward the data was received earlier.This is the Relay nodes in the same cell, the correlation distance between nodes is: all the relay nodes will be assumed have equal distance to the source node, the ith relay node (1<i < N) only transmit (tN+i)th bit from xi signal which received in every time slot t>0.Each relay node Ri will amplify receive signal with gain parameter is β which satisfy the constrains of transmit power of relay node is Pr before forwarding to the destination node.During data forwarding, the data rate is Therefore the bits energy in source node is ES = PS/RS , and in the relay nodes is ER = N.PR/RS.So the model which we use has 4 rely nodes that mean the transmission speed in the CSM by 2 times the speed of SISO to achieve the same spectral efficiency, when the total transmit power of system is (PS + NPR) is kept at the fixed power P.That mean: abbreviated to following equaltion:

Fig. 5 .
Fig. 5. Power distribution of AF-CSM system.Next, we go to the trend of the power distribution of the AF-CSM system in Fig. 5.In the figure, we illustrate the distribution of power within the system in order to minimize the amount of BER (maximum quality system), the standardized distance between the source node and the destination node is 0.5.Through figure we can see most of the transmit power tends to focus on source node to optimize system quality .This means that the SR channels are very sensitive channels and have a huge impact on the quality of the system.

Fig. 7 .
Fig. 7. Simulation results of optimal power allocation, SNR = 40dB.The optimal power allocation between source node and the relay nodes corresponding binding agreement with normalized SR distance is shown in Fig. 7. Based on the results shown in the figure, we can see when the SR distance increasing, the transmit power at source node must also increase (respectively, the transmit power at the relay nodes descending) ensure good quality on SR channel.This will help the relay nodes has better channel estimation and thus minimizing the bit error rate of BER and maximize overall system quality.