Currently, space division multiplexing (SDM), where individual data streams are transmitted from different antennas simultaneously, is expected to be a promising technology for achieving a high data rate within a limited frequency band in a multiple-input multiple-output channel. In this paper, transmitter and receiver architectures of SDM applications are described, and performance improvement with the increase of data streams is shown referring to results of computer simulations. In addition, channel coded systems are also evaluated.

We consider space division multiplexing in a MIMO-OFDM system for high data rate transmission. Channel estimation is very important for suppressing interference and demultiplexing signals. In a wireless LAN system such as IEEE 802.11a, only a few training symbols are inserted in each subcarrier. First, we propose a channel estimation method for a MIMO-OFDM system with two training symbols per subcarrier. The basic idea is to estimate the time-domain channel responses between the transmit and receive antennas. The array response vectors for each subcarrier are calculated by applying a fast Fourier transform to them. We then can obtain the adaptive weights to cancel the interference. We show that employing training symbols having a lower condition number of the matrix used for the channel estimation improves the estimation accuracy. Furthermore, we show the bit error rate for several signal detection schemes using the above estimated channel. It is shown that an ordered successive detection based on an MMSE criterion has excellent performance, that is, it can achieve higher-speed transmissions with a lower transmit power.

A uniform circular array (UCA) is a well-known array configuration which can accomplish estimation of 360 field of view with identical accuracy. However, a UCA cannot estimate coherent signals because we cannot apply the SSP owing to the structure of UCA. Although a variety of studies on UCA in coherent multipath environments have been done, it is impossible to estimate the DOA of coherent signals with different incident polar angles. Then, we have proposed Root-MUSIC algorithm with a cylindrical array. However, the estimation performance is degraded when incident signals arrive with close polar angles. To solve this problem, in the letter, we propose to use SAGE algorithm with a cylindrical array. Here, we adopt a CLA Root-MUSIC for the initial estimation and decompose two-dimensional search to double one-dimensional search to reduce the calculation load. The results show that the proposal achieves high resolution with low complexity.

MIMO (Multiple-Input Multiple-Output) technologies have attracted much interest for high-rate and high-capacity wireless communications. MIMO technologies under frequency-selective fading environments (wideband MIMO technologies) have also been studied. A wideband MIMO system is affected by ISI (Inter Symbol Interference) and CCI (Co-Channel Interference). Hence, we need a MIMO signal detection technique that simultaneously suppresses ISI and CCI. The OFDM system and SC-FDE (Single Carrier-Frequency Domain Equalization) techniques are often used for suppressing ISI. By employing these techniques with the ZF (Zero Forcing) or the MMSE (Minimum Mean Square Error) spatial filtering technique, we can cancel both ISI and CCI. To use ZF or MMSE, we need channel state information for calculating the receive weights. Although an LS (Least Square) channel estimation technique has been proposed for MIMO-OFDM systems, it needs a large estimation matrix at the receiver side to obtain sufficient estimation performance in heavy multipath environments. However, the use of a large matrix increases computational complexity and the circuit size. We use frequency domain channel estimation to solve these problems and propose an iterative method for achieving better estimation performance. In this paper, we assume the use of a MIMO-UWB system that employs a UWB-IR (Ultra-Wideband Impulse Radio) scheme with the FDE technique as the wideband wireless transmission scheme for heavy multipath environments, and we evaluate the iterative frequency domain channel estimation through computer simulations and computational complexity calculations.

In time-varying fading environments, the performance of multiple-input multiple-output (MIMO) systems applying an eigenbeam-space division multiplexing (E-SDM) technique may be degraded due to a channel change during the time interval between the transmit weight matrix determination and the actual data transmission. To compensate for the channel change, we have proposed some channel prediction methods. Simulation results based on computer-generated channel data showed that better performance can be obtained when using the prediction methods in Rayleigh fading environments assuming the Jakes model with rich scatterers. However, actual MIMO systems may be used in line-of-sight (LOS) environments, and even in a non-LOS case, scatterers may not be uniformly distributed around a receiver and/or a transmitter. In addition, mutual coupling between antennas at both the transmitter and the receiver cannot be ignored as it affects the system performance in actual implementation. We conducted MIMO channel measurement campaigns at a 5.2 GHz frequency band to evaluate the channel prediction techniques. In this paper, we present the experiment and simulation results using the measured channel data. The results show that robust bit-error rate performance is obtained when using the channel prediction methods and that the methods can be used in both Rayleigh and Rician fading environments, and do not need to know the maximum Doppler frequency.

MIMO systems using a space division multiplexing (SDM) technique in which each transmit antenna sends an independent signal substream have been studied as one of the successful applications to increase data rates in wireless communications. The throughput of a MIMO channel can be maximized by using an eigenbeam-SDM (E-SDM) technique, and this paper investigates the practical performance of 22 and 44 MIMO E-SDM based on indoor measurements. The channel capacity and bit error rate obtained in various uniform linear array configurations are evaluated and are compared with the corresponding values for conventional SDM. Analysis results show that the bit error rate performance of E-SDM is better than that of SDM and that E-SDM gives better performance in line-of-sight (LOS) conditions than in non-LOS ones. They also show that the performance of E-SDM in LOS conditions depends very much on the array configuration.

Recently, much progress has been made in the study of belief propagation (BP) based signal detection with large-scale factor graphs. When we apply the BP algorithm to equalization in a SISO multipath channel, the corresponding factor graph has many short loops and patterns in an edge connection/strength. Thus, proper convergence may not be achieved. In general, the log-likelihood ratio (LLR) oscillates in ill-converged cases. Therefore, LLR oscillation avoidance is important for BP-based equalization. In this paper, we propose applying node selection (NS) to prevent the LLR from oscillating. The NS extends the loop length virtually by a serial LLR update. Thus, some performance improvement is expected. Simulation results show that the error floor is significantly reduced by NS in the uncoded case and that the NS works very well in the coded case.

For simulating i.i.d. time-varying MIMO channels using multiple Jakes' rings, it is desirable to generate channels having stable statistics with fewer scatterers. The statistical property of the conventional Jakes' model may depend on the initial phase set assigned to scattering points. In this letter, we present simple and effective conditions on arrangement of scattering points to achieve stable fading properties. The results show that the proposed arrangement provides higher statistical stability in generating time-varying channels.

It is well known that weather conditions affect the performance of satellite broadcasting receiving systems. For example, snow accretion on antennas degrades the receiving performance seriously because it reduces received signal power and also can increase antenna noise. Since effects of the weather are considered to differ for various types of receiving antenna, an investigation on this phenomenon is very important. A study on weather effects to three types of satellite broadcasting receiving antenna, a planar antenna, a center-fed parabolic reflector antenna, and an offset parabolic reflector antenna, is presented in this paper. Since the performance of receiving antennas can be determined by a parameter G/T, a long-term and continuous measurement of G/T must be performed. Furthermore, the measurement of more than one receiving system should be performed simultaneously. Also, the measurement should be performed in a snowy area (in winter) and a rainy area (in the other seasons) to evaluate the effect of the weather. To fulfil the criterion, a continuous measurement system of G/T has been built in Hokkaido University, Sapporo. Sapporo, which is located at latitude 42 degrees north, has a long and snowy winter, and also has rainy days in the other seasons so that we can evaluate the effect of weather. Using this measurement system, cumulative distributions of measurement results are obtained so that the performance of different types of receiving system can be evaluated. In this paper, some considerations on the noise level are also discussed briefly to evaluate the performance degradation of the receiving systems.

Mainly, a uniform linear array (ULA) has been used for DOA estimation of coherent signals because we can apply the spatial smoothing preprocessing (SSP) technique. However, estimation by a ULA has ambiguity due to the symmetry, and the estimation accuracy depends on the DOA. Although these problems can be solved by using a uniform circular array (UCA), we cannot estimate the DOA of coherent signals because the SSP technique cannot be applied directly to the UCA. In this paper, we propose to estimate 2-dimensional DOA (polar angles and azimuth angles) estimation of coherent signals using a cylindrical array which is composed of stacked UCAs.

We evaluated the behavior of a multi-user multiple-input multiple-output (MIMO) system in time-varying channels using measured data. A base station for downlink or broadcast transmission requires downlink channel state information (CSI), which is outdated in time-varying environments and we encounter degraded performance due to interference. One of the countermeasures against time-variant environments is predicting channels with an autoregressive (AR) model-based method. We modified the AR prediction for a time division duplex system. We conducted measurement campaigns in indoor environments to verify the performance of the scheme of channel prediction in an actual environment and measured channel data. We obtained the bit-error rate (BER) using these data. The AR-model-based technique of prediction assuming the Jakes' model was found to reduce BER. Also, the optimum AR-model order was investigated by using the channel data we measured.

Multiple-input multiple-output (MIMO) systems using eigenbeam space division multiplexing (E-SDM) perform well and have increased capacities compared with those using conventional space division multiplexing (SDM). However, channel state information (CSI) is required at a transmitter, and the performance of E-SDM systems depends much on the accuracy of the CSI at a transmitter and a receiver. In time-varying fading environments, the channel change between the transmit weight determination time and the actual data transmission time causes the system performance to degrade. To compensate for the channel error, a linear extrapolation method has been proposed for a time division duplexing system. Unfortunately, the system performance still deteriorates as the maximum Doppler frequency increases. Here, two new techniques of channel extrapolation are proposed. One is second order extrapolation, and the other is exponential extrapolation. Also, we propose maximum Doppler frequency estimation methods for exponential extrapolation. Simulation results for 4tx 4rx MIMO systems showed that using the proposed techniques, E-SDM system performs better in a higher Doppler frequency region.

This paper proposes a novel channel estimation method for iterative equalization in MIMO systems. The proposed method incorporates co-channel interference (CCI) cancellation in the channel estimator and the channel estimation is successively performed with respect to each stream. Accuracy of channel estimation holds the key to be successfully converged the iterative equalization and decoding process. Although the channel estimates can be re-estimated by means of LS (Least Square) channel estimation using tentative decisions obtained in the iterative process, its performance is severely limited in a MIMO system because of erroneous decisions and ill-conditioned channel estimation matrix. The proposed method can suppress the above effects by means of CCI cancellation and successive channel estimation. Computer simulation confirms that the proposed channel estimation method can accurately estimate the channel, and the receiver with iterative equalization and the proposed method achieves excellent decoding performance in a MIMO-SM system.

The MIMO system can meet the growing demand for higher capacity in wireless communication fields. So far, the authors have reported that, based on channel measurements, uncoded performance of narrowband MIMO spatial multiplexing in indoor line-of-sight (LOS) environments generally outperforms that in non-LOS (NLOS) ones under the same transmit power condition. In space-frequency coded MIMO-OFDM spatial multiplexing, however, we cannot expect high space-frequency diversity gain in LOS environments because of high fading correlations and low frequency selectivity of channels so that the performance may degrade unlike uncoded cases. In this letter, we present the practical performance of coded MIMO-OFDM spatial multiplexing based on indoor channel measurements. The results show that an LOS environment tends to provide lower space-frequency diversity effect whereas the MIMO-OFDM spatial multiplexing performance is still better in the environment compared with an NLOS environment.

Direction of arrival (DOA) estimation of wireless signals is demanded in many applications. In addition to classical methods such as MUSIC and ESPRIT, non-linear algorithms such as compressed sensing have become common subjects of study recently. Deep learning or machine learning is also known as a non-linear algorithm and has been applied in various fields. Generally, DOA estimation using deep learning is classified as on-grid estimation. A major problem of on-grid estimation is that the accuracy may be degraded when the DOA is near the boundary. To reduce such estimation errors, we propose a method of combining two DNNs whose grids are offset by one half of the grid size. Simulation results show that our proposal outperforms MUSIC which is a typical off-grid estimation method. Furthermore, it is shown that the DNN specially trained for a close DOA case achieves very high accuracy for that case compared with MUSIC.

Frequency-selective fading due to multipath propagation is serious hindrance in high-speed TDMA mobile communications. An adaptive antenna has been proposed to reduce the frequency-selective fading and realize path-diversity. This paper presents a criterion which selects multipath signals and weighting factors for combining them. First, we describe a selection criterion which chooses the multipath signals for the path-diversity. We propose a ratio of signal power to error power for the criterion. Furthermore, we propose weighting factors which realize approximately the maximal ratio combining. Computer simulation results show that the proposed selection criterion and weighting factors reveal excellent performance.

In this paper, the performance of multiuser MIMO E-SDM systems in downlink transmission is evaluated in both uncorrelated and correlated time-varying fading environments. In the ideal case, using the block diagonalization scheme, inter-user interference can be completely eliminated at each user; and using the E-SDM technique for each user, optimal resource allocation can be achieved, and spatially orthogonal substreams can be obtained. Therefore, a combination of the block diagonalization scheme and the E-SDM technique applied to multiuser MIMO systems gives very good results. In realistic environments, however, due to the dynamic nature of the channel and processing delay at both the transmitter and the receiver, the channel change during the delay may cause inter-user interference even if the BD scheme is used. In addition, the change may also result in large inter-substream interference and prevent optimal resource allocation from being achieved. As a result, system performance may be degraded seriously. To overcome the problem, we propose a method of channel extrapolation to compensate for the channel change. Applying our proposed method, simulation results show that much better system performance can be obtained than the conventional case. Moreover, it also shows that the system performance in the correlated fading environments is much dependent on the antenna configuration and the angle spread from the base station to scatterers.

Direction of arrival (DOA) estimation of wireless signals has a long history but is still being investigated to improve the estimation accuracy. Non-linear algorithms such as compressed sensing are now applied to DOA estimation and achieve very high performance. If the large computational loads of compressed sensing algorithms are acceptable, it may be possible to apply a deep neural network (DNN) to DOA estimation. In this paper, we verify on-grid DOA estimation capability of the DNN under a simple estimation situation and discuss the effect of training data on DNN design. Simulations show that SNR of the training data strongly affects the performance and that the random SNR data is suitable for configuring the general-purpose DNN. The obtained DNN provides reasonably high performance, and it is shown that the DNN trained using the training data restricted to close DOA situations provides very high performance for the close DOA cases.

Technology for sixth-generation (6G) mobile communication system is now being widely studied. A sub-Terahertz band is expected to play a great role in 6G to enable extremely high data-rate transmission. This paper has two goals. (1) Introduction of 6G concept and propagation characteristics of sub-Terahertz-band radio waves. (2) Performance evaluation of intelligent reflecting surfaces (IRSs) based on beamforming in a sub-Terahertz band for smart radio environments (SREs). We briefly review research on SREs with reconfigurable intelligent surfaces (RISs), and describe requirements and key features of 6G with a sub-Terahertz band. After that, we explain propagation characteristics of sub-Terahertz band radio waves. Important feature is that the number of multipath components is small in a sub-Terahertz band in indoor office environments. This leads to an IRS control method based on beamforming because the number of radio waves out of the optimum beam is very small and power that is not used for transmission from the IRS to user equipment (UE) is little in the environments. We use beams generated by a Butler matrix or a DFT matrix. In simulations, we compare the received power at a UE with that of the upper bound value. Simulation results show that the proposed method reveals good performance in the sense that the received power is not so lower than the upper bound value.

Direction of arrival (DOA) estimation is an antenna array signal processing technique used in, for instance, radar and sonar systems, source localization, and channel state information retrieval. As new applications and use cases appear with the development of next generation mobile communications systems, DOA estimation performance must be continually increased in order to support the nonstop growing demand for wireless technologies. In previous works, we verified that a deep neural network (DNN) trained offline is a strong candidate tool with the promise of achieving great on-grid DOA estimation performance, even compared to traditional algorithms. In this paper, we propose new techniques for further DOA estimation accuracy enhancement incorporating signal-to-noise ratio (SNR) prediction and an end-to-end DOA estimation system, which consists of three components: source number estimator, DOA angular spectrum grid estimator, and DOA detector. Here, we expand the performance of the DOA detector and angular spectrum estimator, and present a new solution for source number estimation based on DNN with very simple design. The proposed DNN system applied with said enhancement techniques has shown great estimation performance regarding the success rate metric for the case of two radio wave sources although not fully satisfactory results are obtained for the case of three sources.