Sensors_**2011**, _11_, 9887-9903; doi:10.3390/s111009887  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Cooperative MIMO Communication at Wireless Sensor Network: An Error Correcting Code Approach**  
**[PERSON] \({}^{1}\) and [PERSON] \({}^{2,\star}\)**  
\({}^{1}\) Electrical and Electronic Engineering Department, Islamic University of Technology, Boardbazar, Gazipur 1704, Dhaka, Bangladesh; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) School of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-774, Korea  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]. _Received: 10 September 2011; in revised form: 13 October 2011 / Accepted: 13 October 2011 / Published: 20 October 2011_  
**Abstract:** Cooperative communication in wireless sensor network (WSN) explores the energy efficient wireless communication schemes between multiple sensors and data gathering node (DGN) by exploiting multiple input multiple output (MIMO) and multiple input single output (MISO) configurations. In this paper, an energy efficient cooperative MIMO (C-MIMO) technique is proposed where low density parity check (LDPC) code is used as an error correcting code. The rate of LDPC code is varied by varying the length of message and parity bits. Simulation results show that the cooperative communication scheme outperforms SISO scheme in the presence of LDPC code. LDPC codes with different code rates are compared using bit error rate (BER) analysis. BER is also analyzed under different Nakagami fading scenario. Energy efficiencies are compared for different targeted probability of bit error \(p_{b}\). It is observed that C-MIMO performs more efficiently when the targeted \(p_{b}\) is smaller. Also the lower encoding rate for LDPC code offers better error characteristics. **Keywords:** cooperative technique; LDPC; BER; MIMO; wireless sensor networksIntroduction  
Recent advances in micro-electro-mechanical systems technology have enabled the development of wireless sensor nodes in a wireless sensor network (WSN). These tiny sensor nodes are able to sense, process and communicate with each other [1, 2]. Since the battery capacity in each node is limited and the goal is to maximize the lifetime of the network, there are strict energy consumption constraints in WSNs [3]. The size of sensors is typically small but the functions inside the sensor are complex. Recent hardware advancements allow more signal processing functionality to be integrated into a single sensor chip. RF transceiver, A/D and D/A converters, base band processors, and other application interfaces are integrated into a single device to be used as a smart wireless node. A wireless sensor network typically consists of a large number of sensor nodes distributed over a certain region. Monitoring node (MN) monitors its surrounding area, gathers application-specific information, and transmits the collected data to a data gathering node (DGN) or a gateway. Energy issues are more critical in the case of MNs rather than in the case of DGNs since MNs are remotely deployed and it is not easy to frequently change the energy sources. Therefore, the MNs have been the principal design issue for energy limited wireless sensor network design. One prospective solution is the use of MIMO [4, 5] for energy efficient design with a targeted probability of bit error at the receiver. Also LDPC-coded MIMO optical communication is mentioned in [6]. But the MIMO techniques require complex transceiver circuitry and signal processing leading to large power consumptions at the circuit level. Moreover, physical implementation of multiple antennas at a small-size sensor node may not be feasible. The solution came in the form of cooperative MIMO (C-MIMO) [4, 5, 6, 7, 8]. C-MIMO is a kind of MIMO technique where the multiple inputs and outputs are formed via cooperation in a network of single antenna nodes.