In this paper, we address the problem of detector design in severely frequency-selective channels for spatial multiplexing systems that adopt filter bank multicarrier based on offset quadrature amplitude modulation (FBMC/OQAM) as the communication waveforms. We consider decision feedback equalizers (DFEs) that use multiple feedback filters to jointly cancel the post-cursor components of inter-symbol interference, inter-antenna interference, and, in some configuration, inter-subchannel interference. By exploiting the special structures of the correlation matrix and the staggered property of the FBMC/OQAM signals, we obtain an efficient method of computing the DFE coefficients that requires a smaller number of multiplications than the linear equalizer (LE) and conventional DFE do. The simulation results show that the proposed detectors considerably outperform the LE and conventional DFE at moderate-to-high signal-to-noise ratios.

In this paper, a deep learning-based secret key generation scheme is proposed for FDD multiple-input and multiple-output (MIMO) systems. We built an encoder-decoder based convolutional neural network to characterize the wireless environment to learn the mapping relationship between the uplink and downlink channel. The designed neural network can accurately predict the downlink channel state information based on the estimated uplink channel state information without any information feedback. Random secret keys can be generated from downlink channel responses predicted by the neural network. Simulation results show that deep learning based SKG scheme can achieve significant performance improvement in terms of the key agreement ratio and achievable secret key rate.

In this paper, we first propose two batch blind source separation and equalization algorithms based on support vector regression (SVR) for linear time-invariant multiple input multiple output (MIMO) systems. The proposed algorithms combine the conventional cost function of SVR with error functions of classical on-line algorithm for blind equalization: both error functions of constant modulus algorithm (CMA) and radius directed algorithm (RDA) are contained in the penalty term of SVR. To recover all sources simultaneously, the cross-correlations of equalizer outputs are included in the cost functions. Simulation experiments show that the proposed algorithms can recover all sources successfully and compensate channel distortion simultaneously. With the use of iterative re-weighted least square (IRWLS) solution of SVR, the proposed algorithms exhibit low computational complexity. Compared with traditional algorithms, the new algorithms only require fewer samples to achieve convergence and perform a lower residual interference. For multilevel signals, the single algorithms based on constant modulus property usually show a relatively high residual error, then we propose two dual-mode blind source separation and equalization schemes. Between them, the dual-mode scheme based on SVR merely requires fewer samples to achieve convergence and further reduces the residual interference.

An ordered successive interference cancellation (OSIC) scheme based on combined post-processing signal-to-interference-plus-noise ratio (PSINR) is proposed for multiple-input multiple-output (MIMO) systems with retransmission. For the OSIC procedures at the current transmission round, instead of reusing the PSINRs and decision statistics calculated for the previous transmission rounds, the proposed OSIC scheme newly calculates the combined PSINRs and combined decision statistics from the available receive signal vectors and channel matrices at every retransmission. Therefore, the proposed OSIC scheme utilizes all receive signal vectors and channel matrices obtained up to the current transmission round during the OSIC procedures. A low-complexity version of the proposed OSIC scheme is also proposed, and the low-complexity version recalculates the combined PSINRs and combined decision statistics from part of the available receive signal vectors and channel matrices. Simulation results verify that the proposed schemes achieve significantly better error performance than existing OSIC schemes based on the detection and combining process for MIMO systems with retransmission.

The ordered successive interference cancellation (OSIC) detector based on the minimum mean square error (MMSE) criterion has been proved to be a low-complexity detector with efficient bit error rate (BER) performance. As the well-known MMSE-Based OSIC detector, the MMSE-Based vertical Bell Laboratories Layered Space-Time (VBLAST) detector, whose computational complexity is cubic, can not attain the minimum BER performance. Some approaches to reducing the BER of the MMSE-Based VBLAST detector have been contributed, however these improvements have large computational complexity. In this paper, a low complexity MMSE-Based OSIC detector called MMSE-OBEP (ordering based on error probability) is proposed to improve the BER performance of the previous MMSE-Based OSIC detectors, and it has cubic complexity. The proposed detector derives the near-exact error probability of the symbols in the MMSE-Based OSIC detector, thus giving priority to detect the symbol with the smallest error probability can minimize the error propagation in the MMSE-Based OSIC detector and enhance the BER performance. We show that, although the computational complexity of the proposed detector is cubic, it can provide better BER performance than the previous MMSE-Based OSIC detector.

In this letter, a simple dispersion matrix design method for generalized space-time shift keying is presented, in which the dispersion matrices are systematically constructed with cyclic identity matrix, without the need of computer search. The proposed scheme is suitable for any number of transmit antennas greater than two, and can achieve the transmit diversity order of two except two special cases. Simulation results are presented to verify our theoretical analysis and the performance of the proposed scheme.

In this paper, an adaptive expansion strategy (AES) is proposed for multiple-input/multiple-output (MIMO) detection in the presence of circular signals. By exploiting channel properties, the AES classifies MIMO channels into three types: excellent, average and deep fading. To avoid unnecessary branch-searching, the AES adopts single expansion (SE), partial expansion (PE) and full expansion (FE) for excellent channels, average channels and deep fading channels, respectively. In the PE, the non-circularity of signal is exploited, and the widely linear processing is extended from non-circular signals to circular signals by I (or Q) component cancellation. An analytical performance analysis is given to quantify the performance improvement. Simulation results show that the proposed algorithm can achieve quasi-optimal performance with much less complexity (hundreds of flops/symbol are saved) compared with the fixed-complexity sphere decoder (FSD) and the sphere decoder (SD).

We propose linear precoders which jointly minimize the mean-squared error of estimated symbol at the destination node for cooperative multiple-input multiple-output communication systems. Unlike the existing precoders which require high computational complexity to solve the optimization problem on the direct link, the proposed precoder is expressed in a closed-form. Simulation results show that the proposed precoder outperforms the existing precoders in the low SNR region. Moreover, we observe that the proposed iterative algorithm is not sensitive to the initial matrices.

Two designs of wideband compact MIMO antenna using printed dipoles are proposed in this paper. One is a three-port orthogonal polarization antenna and the other is a cube-six-port antenna. Measured results for the antennas show that they resonate at 2.6 GHz and support a bandwidth of over 400 MHz. The worst mutual coupling for the three-port orthogonal polarization antenna is kept under -20 dB whereas that level of the cube-six-port antenna is -18 dB. A number of experiments are conducted on MIMO systems with these compact antennas and linear antenna arrays. Measured data are analyzed to examine channel characteristics, such as cumulative distribution functions (CDFs) of eigenvalues. Furthermore, the effect of different antenna configurations on channel capacity is highlighted and discussed. A high data rate capacity can be achieved with the compact antennas, particularly from the cube-six-port variant. These antennas might be applied in actual MIMO systems in wireless communications.

A Gaussian MIMO broadcast channel (GMBC) models the MIMO transmission of Gaussian signals from a transmitter to one or more receivers. Its capacity region and different precoding schemes for it have been well investigated, especially for the case wherein there are only transmit power constraints. In this paper, a special case of GMBC is investigated, wherein receive power constraints are also included. By imposing receive power constraints, the model, called protected GMBC (PGMBC), can be applied to certain scenarios in spatial spectrum sharing, secretive communications, mesh networks and base station cooperation. The sum capacity, capacity region, and application examples for the PGMBC are discussed in this paper. Sub-optimum precoding algorithms are also proposed for the PGMBC, where standard user precoding techniques are performed over a BC with a modified channel, which we refer to as the "protection-implied BC." In the protection-implied BC, the receiver protection constraints have been implied in the channel, which means that by satisfying the transmit power constraints on the protection implied channel, receiver protection constraints are guaranteed to be met. Any standard single-user or multi-user MIMO precoding scheme may then be performed on the protection-implied channel. When SINR-matching duality-based precoding is applied on the protection-implied channel, sum-capacity under full protection constraints (zero receive power), and near-sum-capacity under partial protection constraints (limited non-zero receive power) are achieved, and were verified by simulations.

In this letter, we present a multiple-input multiple-output (MIMO) optimal combining (OC) scheme based on alternate iteration. With the channel state information (CSI) of co-channel interferers (CCIs), this algorithm can be used in flat fading and frequency selective channels to suppress CCIs. Compared with the optimal transceiver of MIMO maximal ratio combining (MRC) systems, results of simulation show that this scheme improves the uplink transmission performance significantly.

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.

A detector for space-time block coding is proposed to combat time-selective fading. To suppress both noise and interference, a minimum mean square error (MMSE) based detector is introduced for space-time block coding. It is shown by simulations that the proposed detector outperforms the conventional detectors when the channel is time-selective fading.

In this letter, a codebook based multiuser MIMO precoding scheme is proposed for a space-division multiple access (SDMA) system with limited feedback. Focusing on the case of SDMA systems with two transmit antennas, a precoder codebook design is proposed based on the idea that a precoder inducing larger fluctuations in the signal to interference and noise ratio (SINR) at each link can lead to a larger gain in terms of multiuser diversity. It is shown that the proposed multiuser MIMO precoding outperforms existing multiuser MIMO techniques in terms of the average system throughput.

In multiple-input multiple-output (MIMO) wireless systems, the receiver must extract each transmitted signal from received signals. Iterative signal detection with belief propagation (BP) can improve the error rate performance, by increasing the number of detection and decoding iterations in MIMO systems. This number of iterations is, however, limited in actual systems because each additional iteration increases latency, receiver size, and so on. This paper proposes a convergence acceleration technique that can achieve better error rate performance with fewer iterations than the conventional iterative signal detection. Since the Log-Likelihood Ratio (LLR) of one bit propagates to all other bits with BP, improving some LLRs improves overall decoder performance. In our proposal, all the coded bits are divided into groups and only one group is detected in each iterative signal detection whereas in the conventional approach, each iterative signal detection run processes all coded bits, simultaneously. Our proposal increases the frequency of initial LLR update by increasing the number of iterative signal detections and decreasing the number of coded bits that the receiver detects in one iterative signal detection. Computer simulations show that our proposal achieves better error rate performance with fewer detection and decoding iterations than the conventional approach.

While significant work has been dedicated to analyzing the performance of antennas for multiple-input multiple-output (MIMO) and diversity systems, little has appeared on synthesizing optimal antennas for these systems. This paper explores optimal antenna characteristics given understanding about the average structure of the propagation environment, making the results applicable for time-varying channels created by mobile nodes or scatterer motion. Specifically, it examines optimal antenna designs for the cases where 1) the antennas reside in a fixed aperture or 2) the number of antenna elements is fixed (under fast-fading conditions with spatially correlated signals).

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.

We propose a new broadcast strategy for a multiple-input multiple-output (MIMO) system with N transmit antennas at the transmitter and M≤N single antenna receivers. The proposed method, based on dirty-paper coding (DPC), spatially separates the M users but does not suffer from the power loss of classical spatial division multiple access (SDMA). For the special case of M=N=2 and when the two single antenna receivers are assumed to be co-located, the proposed scheme produces a 2 transmit, 2 receiver antenna MIMO transmission system that doubles the symbol rate of MIMO space-time block code (STBC) systems from one to two symbol per transmission time. It is proved theoretically and experimentally that the proposed scheme provides the same performance level as that of MIMO STBC systems (i.e., the Alamouti scheme) for the first symbol, and the same performance as the Bell labs layered space-time (BLAST) system for the second symbol. When compared to the BLAST system, the proposed scheme has the same symbol rate, but achieves significantly better performance, since it provides 2 level diversity per symbol on the first symbol while the BLAST system does not provide any diversity.

In this paper, a computationally efficient stack-based iterative detection algorithm is proposed for V-BLAST systems. To minimize the receiver's efforts as much as possible, the proposed scheme employs iterative tree search for complexity reduction and storage saving. After an M-ary tree structure by QR decomposition of channel matrix is constructed, the full tree depth is divided into the first depth and the remaining ones. At tree depth of one, the proposed algorithm finds M candidate symbols. Based on these symbols, it iteratively searches the remaining symbols at second-to-last depth, until finding an optimal symbol sequence. Simulation results demonstrate that the proposed algorithm yields the performance close to that of sphere detection (SD) with significant saving in complexity and storage.

We propose a geometric mean decomposition (GMD) based vector precoding (VP) for multiple input multiple output (MIMO) systems. Minimum mean square error (MMSE) criterion is used for the joint VP design. The application of GMD method eliminates the imbalance among subchannel gains and obtains a better perturbation vector than the conventional method. We then exploit the extended channel matrix for further performance improvement. Simulation results show the proposed schemes significantly outperform the existing VP schemes.