The multiple-input multiple-output (MIMO) system with the use of transmit and receive antenna arrays achieves diversity and array gains via transmit beamforming. Due to the absence of full channel state information (CSI) at the transmitter, the transmit beamforming vector can be quantized at the receiver and sent back to the transmitter by a low-rate feedback channel, called limited feedback beamforming. One of the key roles of Vector Quantization (VQ) is how to generate a good codebook such that the distortion between the original image and the reconstructed image is the minimized. In this paper, a novel adaptive codebook selection scheme for image classification is proposed with taking both spatial and temporal correlation inherent in the channel into consideration. The new codebook selection algorithm is developed to select two codebooks from the discrete Fourier transform (DFT) codebook, the generalized Lloyd algorithm (GLA) codebook and the Grassmannian codebook to be combined and used as candidates of the original image and the reconstructed image for image transmission. The channel is estimated and divided into four regions based on the spatial and temporal correlation of the channel and an appropriate codebook is assigned to each region. The proposed method can efficiently reduce the required information of feedback under the spatially and temporally correlated channels, where each region is adaptively. Simulation results show that in the case of temporally and spatially correlated channels, the bit-error-rate (BER) performance can be improved substantially by the proposed algorithm compared to the one with only single codebook.
This paper considers a two-way multiple-input multiple-output (MIMO) relaying system with multiple relays between two terminals nodes. The relay antenna selection scheme based on channel singular valued decomposition (SVD) is used to reduce energy consumption. To enhance the system performance, we apply a SVD-based algorithm with MSE criterion which calculates optimal linear transceivers precoding jointly at the source nodes and relay nodes for amplify-and-forward (AF) protocols. In computer simulations, we use an iteration method to compute the non-convex function of joint source and relays power allocation. The simulation results show the SVD-based precoding design with SVD-based relay and antenna selection scheme can achieve a superior system bit error rate (BER) performance and reduce the power consume of relay antennas.
A virtual multiple-input multiple-output (VMIMO) architecture is proposed recently, which allows cooperatively data exchange among each node in a sensor network. We consider to apply the adaptive filter to design a VMIMO receiver. In order to improve the convergence problem and reduce the complexity of the scalar-form Affine Projection (AP), the method of set-membership filtering (SMF) is applied to the Affine Projection adaptive algorithms. Simulation results show that the SMF is able to reduce the computational complexity effectively and achieves a better performance.
Combined optimization of the source precoder, relay weighting matrices, and destination decoder is proposed in dual-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) multiple-relay networks with the source-to-destination link in correlated channels. This broadband cooperative transceiver design is studied based on the minimum mean-squared error (MMSE) criterion under correlated fading channels. The optimization problem belongs neither concave nor convex so that an iterative nonlinear matrix conjugate gradient (MCG) search algorithm is applied to explore local optimal solutions. Simulation results show that the broadband cooperative transceiver joint architecture performs better the non-cooperative transceiver design in terms of the bit-error-rate (BER).
In the paper, combined optimization of the terminal precoders/equalizers and single-relay precoder is proposed for an amplify-and-forward (AF) multiple-input multiple-output (MIMO) two-way single-relay system with correlated channel uncertainties. Both terminal transceivers and relay precoding matrix are designed based on the minimum mean square error (MMSE) criterion when terminals are unable to erase completely self-interference due to imperfect correlated channel state information (CSI). This robust joint optimization problem of beamforming and precoding matrices under power constraints belongs to neither concave nor convex so that a nonlinear matrix-form conjugate gradient (MCG) algorithm is applied to explore local optimal solutions. Simulation results show that the robust transceiver design is able to overcome effectively the loss of bit-error-rate (BER) due to inclusion of correlated channel uncertainties and residual self-interference.