Contents I. Introduction
Wireless sensor network consists of thousands of sensor nodes. Each node possesses sensing capability, processing capability and communication capability with limited information storage and limited battery power [1]. A sensor node senses one or more physical quantities which is required by user and sends it to the sink after some processing. While sending the data to the sink, it consumes large amount of energy as compare to energy consumed in gathering and processing the data. The sink is very large in size and able to accommodate multiple antennas. As compared to ordinary nodes the sink has huge energy still being the central node its energy consumption should be carefully monitored. Main and biggest issue and challenge of wireless sensor network is limitation of energy resources [2]. The overall working of wireless sensor network depends on energy. If energy of sensor network exhaust then node will die and communication breaks down very soon. Data transmission consumes a large chunk of energy thus energy efficient transmission approach should be used in WSN. Till nineties, traditional wireless communication systems were using Single input single output (SISO) technique but later on MIMO technique has been proposed which provides reliability with increased capacity. It saves the energy at both ends the node as well as sink. MIMO system gives less bit error rate (BER) as compare to SISO system for same signal to noise ratio (SNR). MIMO technique has been successfully implemented in several wireless networks. The challenges such as scalability, flexibility, redundancy and heterogeneity make the functioning of WSN very complex and tough. MIMO should be used for saving energy in data transmission from cluster head (CH) to base station (BS). But MIMO cannot be directly implemented over sensor nodes. The sensor nodes have small size and limited power so putting two or more antennas on a single node is almost impossible. Virtual MIMO concept has been suggested in WSN for reaping the benefits of MIMO techniques at a little bit extra cost [3]. In V-MIMO two or more neighbour nodes cooperate in data transmission. One transmitting antenna at cluster head and another on secondary cluster head communicate with two receiving antennas at base station. Any space time code e.g. Alamouti coding scheme [4] should be used for data transmission and reception. Similar work has been reported in [5] where authors proposed an energy-efficient wireless communication protocol which minimizes the energy consumption as much as possible. They have used cooperative MIMO transmission for minimizing consumption of energy by considering channel training overhead and local circuit energy. An optimization model is proposed to find the optimal number of cooperative clusters, nodes and transmission rates. MIMO system have shown that it can not only combat shadowing and fading in wireless communications but it also performs well as compare to Single-Input Single Output (SISO) system with same transmit power and bit error rate (BER) [4]. Cui proposed a virtual MIMO communication architecture with Alamouti code in paper [3]. The results of [3] were further refined in [6]. In [7] selected cooperative MIMO (C-MIMO) outperforms the unselected C-MIMO in total energy consumption and it shows energy efficient performance over existing technique as compared with SISO structure. Cooperative MIMO came to exist because of requirement of complex transceiver circuitry and signal processing leads to larger power consumptions at circuit level. This is the main drawback of MIMO system. Solution of this problem was proposed by cooperative MIMO [3] [5], [8] and virtual antenna array [9] concepts to attain MIMO capability in a network of single antenna nodes. In [10] analysis is done on the basis of energy and delay efficiencies of virtual MIMO and channel parameters like channel path loss exponent, transmission distance and constellation size. Cui [11] analysed a virtual MIMO system with Alamouti code for single hop transmission in wireless sensor network and comparison is done on performances of multi-hop MIMO and SISO networks. In [12] Jayaweera extended the work of [11] with multi hop and gave approach of energy-efficient virtual MIMO based on V-BLAST receiver processing for constraints in energy. The model based simulation approach has been adopted by all the authors but in present work both model based and ray based approach is used. The problem formulation and methodology is discussed in section II. The simulation framework has been illustrated in section III. Various results have been reported in section IV along with discussion and interpretation of results. Section V contains the conclusion and future work.
