Communication systems that use MIMO form a key technology for next generation wireless systems, because they provide a reliable communication (using diversity techniques) or higher data rates (using spatial multiplexing techniques) that are enablers to achieve a channel capacity enhancement, without the need of using additional transmit power or bandwidth, [
1‐
3]. MIMO uses antenna arrays instead of single transmit, receive antennas, and operates by simultaneously transmitting multiple signals in the same frequency band. At the receiver, multiple antennas are also used, and the received signals are processed to separate different transmitted data streams. These transmit-receive antenna pairs form wireless links that have different impulse responses, and thus channel conditions might be different for each one of them. Therefore, a MIMO system can use more efficiently these resources by performing antenna selection in order to transmit through the best wireless links, and by allocating the necessary power to those antennas being chosen in order to achieve performance criteria. Antenna selection techniques choose a subset of the available transmission antennas by comparing the channels they find at the moment of transmission, so that performance criteria such as capacity, bit error rate (BER) or throughput are satisfied. In [
4], authors present an overview of classic results on selection diversity and antenna selection algorithms at the transmit and receive side in MIMO systems. In [
5], antenna selection with imperfect channel estimation is analyzed, and the optimal single receiver antenna selection rule is obtained. It is also shown that the number of receive antennas determines the complexity of the technique. In [
6], antenna selection is also considered for MIMO-OFDM systems, although the selection is limited to a single antenna and no further consideration of extension to multiple antennas is made. It is known that antenna selection improves BER, but it is also known that performance improvement can be achieved if transmit power is varied according to channel conditions. In [
7], building on the classical water-pouring algorithm, authors proposed an antenna selection algorithm based on partial water pouring over the strongest channel model but using equal power allocation. In [
8], power control in MIMO systems is introduced with antenna selection using truncated channel inversion. In [
9], antenna selection is shown for spatial multiplexing and diversity techniques for linear receivers. One of the techniques that were proposed is a suboptimal solution using the eigenstructure of the channel matrix to choose the number of antenna elements needed for an improved performance, no power allocation is carried out. Water-pouring algorithm (WPA) has also been used for antenna selection in [
10], but it is not used for power allocation. In [
11], authors used antenna selection in a long-term evolution (LTE), system primarily to quantify performance (symbol error rate and cumulative distribution function) and to provide a systematic overview of all the hooks in the LTE standard that enable transmit antenna selection. Recently, in [
12], authors used antenna selection and power allocation to improve performance in massive MIMO systems. They use convex optimization to select the antenna subset that maximizes the dirty-paper coding capacity and then optimize over the user power allocation.
In this paper, we present a system that uses both, antenna selection and power allocation, to improve capacity. It is known that the channel capacity in a MIMO communication system depends on the number of transmit and received antennas, the signal-to-noise ratio, the channel state, and the autocorrelation or covariance matrix of the transmitted signal vector [
1]. This covariance matrix is the variable that is left to improve channel capacity. We propose the improvement of the channel capacity through the use of a mathematical model which considers antenna selection techniques (AST) as well as the variation of the covariance matrix using WPA by assigning weights according to the channel coefficients of the channel matrix. We assume that the channel matrix and its coefficients are known or estimated previously. The weights obtained are used to determine the amount of power needed to be allocated to each antenna element of the subset that has been chosen for transmission. The technique to obtain the weights is based on the eigenvalues of the covariance matrix in an optimal way, thus improving performance over that reported in [
9] and [
10]. Since the covariance matrix is analyzed at the transmitter side, in this work, we are going to consider AST only at the transmitter.
In the ‘MIMO communication systems’ section, we present the basic notation with the definitions used in the analysis. The section ‘MIMO system using I-AST and WPA’ introduces the model proposed using antenna selection and WPA. The ‘Numerical results’ section are discussed afterwards for different antenna arrays, and a discussion on capacity bounds is introduced. At the end, the ‘Conclusions’ section is presented.