Research in smart antenna technology has progressed over the past few years and is gradually reaching the phase of practical use. We have developed a smart antenna test bed for wireless local area network (LAN) that is based on the IEEE802.11b. The objective is to improve anti-multipath fading performance and expand communication distance. Using this test bed, we carried out field tests in two environment. One environment is an office in an non line of sight (NLOS), and another environment is an outdoor in a line of sight (LOS). In this paper, we explain the outline of the test bed, the measurement method, and present the results of the field tests. In the office environment, we measured the performance of each set with a different number of antenna elements. The results show that the dead-spots where communication becomes impossible disappear if the number of antenna elements is more than or equal to two. In addition, a greater number of elements indicates better performance. The total average throughput is 1.6 times as efficient when two elements are used, and 1.9 times when four elements are used. Cold spots where the throughput is slower than 1 Mbps are reduced by 80-90%. In the outdoor LOS environment field test, it is shown that by using four-element smart antenna for both transmitter and receiver, the communication distance reached 1km with an average throughput of 4 Mbps. These results prove that the smart antenna drastically improves the performance of a wireless LAN system, i.e. the IEEE802.11b.

In a frequency-selective multiple-input multiple-output (MIMO) channel, the optimum transmission is achieved by beamforming with eigenvectors obtained at each discrete frequency point, i.e., an extension of eigenbeam-space division multiplexing (E-SDM). However, the calculation load of eigenvalue decomposition at the transmitter increases in proportion to the number of frequency points. In addition, frequency-independent eigenvectors increase the delay spread of the effective channel observed at the receiver. In this paper, we propose a pseudo eigenvector scheme for the purpose of mitigating the calculation load and maintaining frequency continuity (or decreasing the delay spread). First, we demonstrate that pseudo eigenvectors reduce the delay spread of the effective channels with low computational complexity. Next, the practical performance of the pseudo E-SDM (PE-SDM) transmission is evaluated. The simulation results show that PE-SDM provides almost the same or better performance compared with E-SDM when the receiver employs a time-windowing-based channel estimation available in the low delay spread cases.

In SDMA, a spatial domain interference canceller applying a multistage processing concept to the MMSE multibeam adaptive array has an attractive feature. Weak power signals strongly interfered can be detected in the succeeded stages after removing other strong power signals which are already detected. This idea can be enhanced to the reference timing estimation required in the MMSE algorithm. In this paper, the spatial domain interference canceller introducing multistage timing estimation is proposed and its performance is evaluated by computer simulations. The results show that the timing estimation performance highly improved.

Implementation of several wireless applications such as radar systems and source localization is possible with direction of arrival (DOA) estimation, an array signal processing technique. In the past, we proposed a DOA estimation method using deep neural networks (DNNs), which presented very good performance compared to the traditional root multiple signal classification (root-MUSIC) algorithm when the number of radio wave sources is two. However, once three radio wave sources are considered, the performance of that proposed DNN decays especially at low and high signal-to-noise ratios (SNRs). In this paper, mainly focusing on the case of three sources, we present two additional strategies based on our previous method and capable of dealing with each SNR region. The first, which supports DOA estimation at low SNRs, is a scheme that makes use of principal component analysis (PCA). By representing the DNN input data in a lower dimension with PCA, it is believed that the noise corrupting the data is greatly reduced, which leads to improved performance at such SNRs. The second, which supports DOA estimation at high SNRs, is a scheme where several DNNs specialized in radio waves with close DOA are accordingly selected to produce a more reliable angular spectrum grid in such circumstances. Finally, in order to merge both ideas together, we use our previously proposed SNR estimation technique, with which appropriate selection between the two schemes mentioned above is performed. We have verified the superiority of our methods over root-MUSIC and our previous technique through computer simulation when the number of sources is three. In addition, brief discussion on the performance of these proposed methods for the case of higher number of sources is also given.