Channel capacity improvement for cooperative MIMO wireless sensor networks via adaptive MIMO-SVD

Over the past decade, wireless sensor networks (WSNs) have gained more research attention for their potential applications in, e.g., healthcare, defence and environmental monitoring. However, limited channel capacity and battery life of sensors are the main design challenges in WSNs. The use of cooperative multiple-input multiple-output (MIMO) technology is one of the most promising techniques that can enhance the channel capacity and reduce transmission energy in WSNs. In this paper, we propose an adaptive MIMO singular value decomposition (SVD) method in order to enhance the channel capacity of WSNs, in the case of imperfect channel state information (CSI). The SVD is conditioned to provide efficient space-time precoding/decoding with the use of either least square (LS) or minimum mean-square error (MMSE) filtering. Channel capacity is improved yet further by way of optimized power allocation, via a water filling strategy. The bit error rate (BER) and channel capacity performances of the encoding methods are evaluated for Rayleigh fading channels. Our simulation results show that the adaptive MIMO-SVD technique works best with MMSE channel estimation for cooperative MIMO in WSNs.