Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

The spectrum has increasingly become occupied by various wireless technologies. For this reason, the spectrum has become a scarce resource. In prior work, the authors have addressed the spectrum sensing problem by using multi-input and multi-output (MIMO) in cognitive radio systems. We considered the detection and estimation framework for MIMO cognitive network where the noise covariance matrix is unknown with perfect channel state information. In this study, we propose a generalized likelihood ratio test (GLRT) for the spectrum sensing problem in cognitive radio where the noise covariance matrix is unknown with non-perfect channel state information. Two scenarios are examined in this study: (i) in the first scenario, the sub-optimal solution of the worst case of the system’s performance is considered; (ii) in the second scenario, we present a robust detector for the MIMO spectrum sensing problem. For both scenarios, the Bayesian approach with a generalized likelihood ratio test based on the binary hypothesis problem is used. From the results, it can be seen that our approach provides the best performance in the spectrum sensing problem under specified assumptions. The simulation results also demonstrate that our approach significantly outperforms other state-of-the-art spectrum sensing detectors when the channel uncertainty is addressed.

Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies

Spatial Modulation Technologies (SMTs) are schemes that reduce inter-carrier interference (ICI), inter-channel interference, inter-antenna synchronization (IAS), and system complexity for multiple-input multiple-output (MIMO) communication systems. Moreover, high spectral and energy efficiency have rendered SMTs attractive to underwater acoustic (UWA) MIMO communication systems. Consequently, this paper focuses on SMTs such as spatial modulation (SM), generalized spatial modulation (GSM), and fully generalized spatial modulation (FGSM) in which one constant number and one multiple number of antennas are active to transmit data symbols in any time interval for underwater acoustic communication (UWAC). In SMTs, the receiver requires perfect channel state information (P-CSI) for accurate data detection. However, it is impractical that the perfect channel knowledge is available at the receiver. Therefore, channel estimation is of critical importance to obtain the CSI. This paper proposes the pilot-based recursive least-square (RLS) adaptive channel estimation method over the underwater time-varying MIMO channel. Furthermore, maximum likelihood (ML) decoder is used to detect the transmitted data and antennas indices from the received signal and the estimated UWA-MIMO channel. The numerical computation of mean square error (MSE) and bit error rate (BER) performance are computed for different SMTs like SM, GSM and FSGM using Monte Carlo iterations. Simulation results demonstrate that the RLS channel estimation method achieves the nearly same BER performance as P-CSI.

Effect of Channel Estimation Error on Coordinated Small-Cells with Block Diagonalization

This paper investigates the implementation of cooperative communication in small-cells network. Cooperative communication is some cells that in a network can work together, ex. joint transmit power and exchange resource allocation among them. Therefore, cooperative small-cells can be possible to add more multiple input multiple output (MIMO) antennas. But, channel state information (CSI) is required. In practice, perfect channel state information may not be perfect due to the real condition of the real environment such as delay, error, and etc. This paper illustrates cooperative small-cells under error model on the channel estimation. Our propose system is combined by implementing block diagonalization pre-coding and power allocation based on water-filling algorithm. As investigated by simulation results, our proposed coordinated small-cells showed good performances compare to previous algorithm either with perfect channel or with the error channel estimation.