The issue of energy saving is significant since in a battery-operated wireless node, the battery energy is finite and a node can only transmit a finite number of bits. To extend the life span of a node in wireless sensor networks, an energy-efficient communication technique is mandatory so that energy expenditure is minimized, and at the same time, it satisfies the given throughput and delay necessities. In this context, we analyze the performance of cooperative Multiple-Input Multiple-Output (MIMO)-based communication in wireless sensor networks. In this paper, we analyze the communication method to reduce the total delay and total energy expenditure required to send specified quantity of bits. This energy efficiency investigation of MIMO scheme is broadened to individual single antenna nodes that collaborate to form multiple antenna transmitters or receivers. It is also seen that over some distance ranges, cooperative MIMO broadcast and reception can simultaneously achieve both energy savings and delay reduction. Simulations show that with proper design, cooperative MIMO-based communication is more faster and energy-efficient as compared to non-cooperative approach e.g., Single-Input Single-Output (SISO) for larger distances and prolong sensor network lifetime.

Recent hardware advances allow more signal processing functionally to be integrated into a fully functional wireless sensors node with small batteries. Such wireless nodes are deployed in many humanly inaccessible situations to provide a fast, reliable, fault-tolerant, and energy-aware channel for monitoring applications (Paulraj et al., 2003). Energy optimization is a critical issue in the design of low power, wireless sensor networks. Wireless nodes operate with small batteries for which replacement, when possible, is very difficult and expensive. Thus, in many cases, the wireless node must operate without battery replacement for many years. Consequently, minimizing the energy consumption is a very important issue in design consideration and energy-efficient transmission schemes must be used for data transfer in wireless sensor networks. The total energy consumption includes both transmission energy and circuit energy consumption.

Motivated by information theoretic predictions on large spectral efficiency of Multiple-Input Multiple-Output (MIMO) systems in fading channels (Paulraj et al., 2003). It has been shown that MIMO systems can support high data rates under the same transmit power budget and Bit Error Rate (BER) performance as a Single Input Single Output (SISO) systems. For the same throughput requirements, MIMO systems require less transmission energy than SISO systems. However, direct application of MIMO techniques to sensor networks is impractical due to the limited physical size of sensor node which typically can only support a single antenna. Therefore, by allowing individual single antenna nodes to cooperate on information transmission and reception, a cooperative MIMO system can be constructed such that energy-efficient MIMO schemes can be developed. Energy-efficiency consumption techniques typically focus on minimizing the transmission energy only, which is reasonable in long range applications where the transmission energy is dominant in the total energy consumption. However, in short range applications such as wireless sensor networks, the circuit energy consumption is comparable to or even dominates the transmission energy. The circuit energy consumption includes the energy consumed by all the circuit blocks along the signal path: Analog to Digital converter (ADC), Digital to Analog converter (DAC), mixer, frequency synthesizer, Low Noise Amplifier (LNA), power amplifier and baseband DSP. Some joint energy-minimizing techniques have been proposed for SISO systems in Schurges et al. (2001); Min and Chadraksan (2002); Cui et al. (2003a and 2003b); and Alamouti (1998), where multimode operation with optimized system parameters is discussed.

Telecommunications Journal, Speech Noise Elimination, Traditional Spectrum Subtraction, Communication Systems, Speech Processing Algorithms, Signal Processing, Noise Reduction Techniques, Voice Activity Detection, Spectrum Subtraction Method, Signal to Noise Ratios, Cockpit Voice Recorder, Musical Noise.