Multi-user spatial modulation MIMO

In this paper, we design a zero-forcing (ZF) Tomlinson-Harashima precoding (THP) and a minimum mean square error (MMSE) THP for multiuser MIMO relay system with direct link. Based on these precoding structures, we propose the optimal combining weights of the ZF THP and the MMSE THP in half duplex relay system, and the sub-optimal weights of MMSE THP to reduce feedback channel state information (CSI). Through the Monte Carlo simulation, we verify that the optimal weights combining method outperforms the constant weights combining method. In addition, we confirm that the performance of the sub-optimal weights combining method is close to the optimal method, while reducing the feedback CSI simultaneously.

Multi input multi output (MIMO) system with the multiple antennas at both the transmitter and receiver is the emerging technology in the modern wireless system. MIMO systems are well adopted due to its high spectral efficiency and high data rates. For the detection of MIMO systems, a powerful technique called Lattice Reduction (LR) algorithm are used to obtain the better orthogonality in the channel matrices and is aided with the linear and low complexity detectors such as Zero forcing(ZF) and Minimum mean square error (MMSE) for the reduction in complexity. In this paper we propose a new algorithm called Enhanced lattice reduction algorithm to improve the error performance for the linear detectors with the lesser complexity and also it is aided with the non linear precoding called Tomlinson-Harashima precoding (THP) in the presence of channel state information (CSI). Lattice reduction aided with THP improves the performance linearly. BER performance of the proposed algorithm is determined and it is compared with the other LR aided algorithms in the literature.

In orthogonal frequency-division multiplexing (OFDM) systems, it is generally assumed that the channel response is static in an OFDM symbol period. However, the assumption does not hold in high-mobility environments. As a result, intercarrier interference (ICI) is induced and the system performance is degraded. A simple remedy for this problem is the application of the zero-forcing (ZF) and minimum mean square error (MMSE) equalizers. Unfortunately, the direct ZF method requires the inversion of an NxN ICI matrix, where N is the number of subcarriers. When N is large, the computational complexity can become prohibitively high. As for the direct MMSE method, in addition to an NxN matrix inverse, it requires an extra NxN matrix multiplication, making the required computational complexity higher compared to the direct ZF method. In this dissertation, we first propose a low-complexity ZF method to solve the problem in single-input-single-output (SISO) OFDM systems. The main idea is to explore the special structure inherent in the ICI matrix and to apply Newton's iteration for matrix inversion. With our formulation, fast Fourier transforms (FFTs) can be used in the iterative process, reducing the complexity from O(N^3) to O(Nlog_2 N) . Also, the required number of the iteration is typically one or two. We also analyze the convergence behavior of the proposed method and derive the theoretical output signal-to-interference-noise-ratio (SINR). For the MMSE method, we first reformulate the MMSE solution in a way that the extra matrix multiplication can be avoided. Similar to the ZF method, we then exploit the structure of the ICI matrix and apply Newton's iteration to reduce the complexity of the matrix inversion. For a multiple-input-multiple-output (MIMO) OFDM system, the required complexity of the ZF and MMSE methods becomes more intractable. We then manage to extend the proposed ZF and MMSE methods for SISO-OFDM systems to MIMO-OFDM systems. It turns out that the computational complexity can be reduced even more significantly. Simulation results show that the performance of the proposed methods is almost as good as that of the direct ZF and MMSE methods, while the required computational complexity is reduced dramatically. Finally, we explore the application of the proposed methods in mobility-induced ICI mitigation for OFDM multiple access (OFDMA) systems, and in carrier frequency offset (CFO) induced ICI mitigation for OFDMA uplink systems. As that in OFDM systems, the proposed methods can reduce the required computational complexity, effectively.

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

In code division multiple access (CDMA) systems transmitting over time-varying multipath channels, both intersymbol interference (ISI) and multiple access interference (MAI) arise. The conventional suboptimum receiver consisting of a bank of matched filters is often inefficient because interference is treated as noise. The optimum multi-user detector is too complex to be implemented at present. Four suboptimum detection techniques based on zero forcing (ZF) and minimum mean square error (MMSE) equalization with and without decision feedback (DF) are presented and compared. They combat both ISI and MAI. The computational complexity of all four equalizers is essentially the same and they are independent of the size of the data symbol alphabet. It is shown that all four equalizers considerably outperform the conventional receiver. The MMSE equalizers are superior to the corresponding ZF equalizers. Furthermore, the equalizers with DF perform better than the corresponding equalizers without DF. An algorithm termed channel sorting is introduced to reduce the impairing effect of error propagation on the equalizers with DF. >

This paper considers the uplink of a single-cell large-scale multiuser multiple-input multiple-output (MU-MIMO) system. The transmitted data is detected by linear receivers at the base station (BS), that is, maximum ratio combining (MRC), zero-forcing (ZF) and minimum mean square error (MMSE). New non-asymptotic outage expressions that are valid for any number of BS antennas are derived. It is shown that the MMSE scheme always achieves the best outage performance, and the ZF scheme outperforms the MRC scheme in the high signal-to-noise-ratio (SNR) regime, while the opposite holds in the low SNR regime. We also demonstrate that when the number of BS antennas is very large and the number of users is fixed, the asymptotic outage probability tends to zero, providing that the channel state information (CSI) is perfect. However, if both the number of BS antennas and the number of users grow large, it would be a different matter. Extensive numerical results are presented to verify the theoretical analysis.

Multiple-input multiple-output (MIMO) systems can achieve high data-rate and high capacity transmission. In MIMO systems, the scheme that weight substreams based on minimum mean square error (MMSE) criterion at the transmitter using feedback channel state information (CSI) achieves good performance (denoted hereafter as MMSE precoder). However, if the optimum number of substreams is not selected based on CSI and methods for reducing the performance degradation due to feedback delay is not used, MMSE precoder cannot achieve the full performance possible. In this paper, we propose an MMSE precoder with mode selection for MIMO systems, which selects both the number of substreams and the modulation scheme for each substream to minimize the average BER at a fixed rate. We evaluate the BER of the proposed MMSE precoder with two methods for reducing performance degradation due to feedback delay, channel prediction and receive weight robust to feedback delay. We also evaluate the BER of the proposed MMSE precoder with the method that combines channel prediction with receive weight robust to feedback delay. Simulation results show that the BER of the proposed MMSE precoder is improved compared to that of the conventional MMSE precoder using the fixed number of substreams. We also show that the method that combines channel prediction with receive weight robust to feedback delay can achieve good BER even when the large feedback delay exists.

In this paper, we investigate the possibility of reliable and computationally efficient detection for spectrally efficient non-orthogonal Multiplexing (FDM) system, exhibiting varying levels of intercarrier interference. Optimum detection is based on the Maximum Likelihood (ML) principle. However, ML is impractical due to its computational complexity. On the other hand, linear detection techniques such as Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) exhibit poor performance. Consequently, we explore the combination of MMSE estimation with ML estimation around a neighborhood of the MMSE estimate. We evaluate the performance of the different schemes in Additive White Gaussian Noise (AWGN), with reference to the number of FDM carriers and their frequency separation. The combined MMSE-ML scheme achieves a near optimum error performance with polynomial complexity for a small number of BPSK FDM carriers. For QPSK modulation the performance of the proposed system improves for a large number of ML comparisons. In all cases, the detectability of the FDM signal is bounded by the signal dimension and the carriers frequency distance.

Limited feedback precoding (LFP) significantly improves multiple-input multiple-output (MIMO) spatial multiplexing link reliability with a small amount of feedback from the receiver back to the transmitter. One of the key problems linked to LFP is how to select an optimal precoder from a pre- determined unitary codebook. We find that the conventional precoder selection criteria are not applicable to the stream maximization transmission (SMT) mode with linear receivers, including zero forcing (ZF) and minimum mean square error (MMSE) decoders. To solve this issue, a novel singular value decomposition (SVD) based precoder selection criterion is proposed in this paper. This criterion features a unified structure for all the linear receivers such as ZF and MMSE decoders, and is shown by simulation to provide significant coding gains in various SMT systems. With the same complexity as the conventional one, the proposed criterion could find its applications in next generation systems employing SMT spatial multiplexing, significantly improving system performance with affordable feedback requirement.

In this paper, we consider an optimal finite-alphabet non-orthogonal multiple access (NOMA) design for a multiuser multi-input single-output (MISO) visible light communication (VLC) broadcast system when channel state information is available at the transmitter. By utilizing the cooperation of multiuser interference, an optimal multidimensional additively uniquely decomposable constellation group (AUDCG) is designed to maximize the received worst-case minimum Euclidean distance of all users subject to a normalized average optical power within the orthants of a real-valued space. This optimal AUDCG is proved to be commonly used pulse amplitude (PAM) with an optimal beamformer, which is the optimal solution to a linear max-min programming problem. In addition, our optimal AUDCG admits fast demodulation of the sum signal from a noisy received signal as well as fast decoding individual signal from the estimated sum signal. Computer simulations indicate that our proposed finite-alphabet NOMA design has significantly better error performance than currently available zero-forcing (ZF), minimum mean square error (MMSE) and time-division orthogonal access (TDMA) transmission schemes for multiuser MISO VLC broadcast systems.

Spatial modulation (SM) is a multiple antenna transmission approach that uses the index of the transmitting antenna for delivering information data. The choice on the transmitting antenna depends on the incoming bit sequence and has a random nature. The receiver estimates the transmitted symbol and the active transmit antenna index and uses both estimations to retrieve the original information bits. For best reception, individual channel links between transmit and receive antenna pairs must be unique. In fact, correct reception totally depends on the uniqueness of channel coefficients. Incorrectly interpreting channel state information at the receiver causes the BER performance to degrade and the detection to fall short. In this paper, we develop a channel model that incorporates both correlated and imperfect channel state information and use this model to study the bit-error-ratio (BER) performance of most widely used SM detectors, namely the minimum mean square error, the maximum likelihood and the optimum detector in flat Rayleigh fading channels to examine the sensitivity these detectors exhibit to the presence of either channel correlation or imperfect channel state information. The capacity of SM system is studied in comparison to other multiple-input multiple-output techniques and the influence of channel impairments on the overall system capacity is observed.

The receiver in a wireless communication system has the task of computing good estimates for the data symbols that have been transmitted. The best (optimum) detector is the Maximum Likelihood (ML) detector. However, it requires a high computational complexity. This work aims to efficiently detect the transmitted symbols with a reduced complexity. In order to produce a near optimum receiver, two methods are presented. These methods are obtained by convex optimization relaxations which yield global optimum solutions. The relaxations are combined with the idea of using the structure of the channel matrix to reduce the computational complexity. The channel matrix exhibits a banded Toeplitz structure. In each case, the dual problem of the convex optimization relaxation is solved to estimate the noise power. Gradient descent algorithm is used to solve the dual problem in the first relaxation while the bisection method is applied for the second relaxation. In both cases, the result is a Generalized Minimum Mean Squared Error (GMMSE) detector which has a form similar to the Minimum Mean Square Error (MMSE) detector and a performance almost the same as the MMSE detector, but it is not require the knowledge of the noise power. The GMMSE detectors can be used in scenarios where adapted or blind adaptive detection is not suitable, for instance when the channel is rapidly changing. Using a circular approximation of banded Toeplitz matrix the Fast Fourier Transform (FFT) can be applied to reduce the computational complexity of the detectors. Finally, the local search method is applied to enhance the performance of the proposed GMMSE detector. The proposed detector is a near optimum detector with low computational complexity.

Equalisation is one of the most important algorithms to improve the Bit-Error-Rate (BER) performance in communication systems. In general, equalisation can be classified into two categories: linear and non-linear equalisation. The linear equalisers include Zero Forcing (ZF), and Minimum Mean Square Error (MMSE) equalisers. The non-linear equalisers include ZF and MMSE equalisers that are based on Successive Interference Cancellation (SIC). The ZF equaliser suffers from noise enhancement. The MMSE equaliser can mitigate the noise enhancement using the estimated Signal-to-Noise Ratio (SNR). The ZF-SIC and MMSE-SIC equalisers suffer from error propagation, and high computational complexity, respectively. In this paper, the Orthogonal Frequency Division Multiplexing (OFDM) system is modulated and demodulated using Inverse Discrete Wavelet Transform (IDWT), and DWT instead of Inverse Discrete Fourier Transform (IDFT) and DFT, respectively. Also, we propose a Joint Low-complexity Regularised ZF-SIC (JLRZF-SIC) equaliser that jointly performs the equalisation, and Carrier Frequency Offset (CFO) compensation with lower complexity using banded matrix approximation. The proposed JLRZF-SIC equaliser can accomplish the equalisation, and CFO compensation processes with low-complexity compared to the corresponding MMSE-SIC equaliser. Simulation results show that the proposed JLRZF-SIC equaliser outperforms different linearand non-linear equalisers.

Transmit preprocessing employed at the basestation (BS) has been proposed for simplifying the design of the mobile receiver. Provided that the channel impulse response (CIR) of all the BS to mobile station (MS) links is known in advance-even before the signal's transmission-it is plausible that the different users' signals may be differentiated with the aid of their unique, user-specific downlink CIRs. Naturally, this non-causal CIR knowledge is unavailable in practice. Hence a natural design option is to estimate the CIRs at the receiver after the BS's signal was received and convey it using side-information to the BS for its future use. Naturally, the resultant CIR has to be quantized before its transmission. In addition to this quantization error, it also becomes outdated and both imperfections result in an erosion of the achievable transmit preprocessing gain expressed in terms of either the attainable transmit power reduction or the number of users that may be supported. Another attractive design option is to avoid the CIR-signalling latency by invoking the previously received CIRs for predicting their future evolution using CIR-tap prediction. In this paper, Kalman filtering aided CIR prediction is combined with both minimum mean square error (MMSE) and zero forcing based preprocessing. Our simulation results show that the proposed scheme is capable of attaining 6.5 dB gain at a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , when using 6 antennas.

The combination of space-time block codes (STBC) and spatial multiplexing (SM) schemes has the advantages of diversity gain and high data rate. For such STBC/SM hybrid systems, we propose a new detector based on the block structure of STBC and the sphere decoder (SD). By employing the SD, optimal detection is performed with computational complexity much less than the brute-force maximum likelihood (ML) search. Simulation results show that the new decoder significantly outperforms the previously developed zero-forcing (ZF) group/STBC detector, the minimum mean square error (MMSE) group/STBC detector, and the MMSE and ML detectors for equivalent SM systems.