Multiple antenna aided OFDM employing minimum bit error rate multiuser detection

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.

A combined minimum mean square error (MMSE) and maximum likelihood (ML) multiuser detector with reduced-complexity is proposed for uplink space-time block coded (STBC) orthogonal frequency division multiplexing (OFDM) systems. In this combined detector, received signals from co-channels are first detected by an MMSE multiuser detector. Then an ML-based post-detection processor (PDP) is designed to detect estimated outputs from the MMSE detector with the highest probability of errors and correct them to reduce the system bit error rate (BER). An efficient algorithm is proposed for the PDP which requires only a small additional complexity but can improve the system BER performance significantly compared with the conventional MMSE multiuser detector.

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation technique. It supports high data rates over wireless radio channels. Due to high peak-to-average power ratio, OFDM is sensitive to the non-linear effects of the power amplifier. So, many methods of PAPR reduction techniques have been proposed. Of these, the distortionless method of PAPR reduction is Partial Transmit Sequence (PTS) technique. But this method suffers from high computational complexity. Modified PTS technique has been proposed to address the issue of high computational complexity. Modified PTS technique adopts a neighbourhood search algorithm to find the optimum set of phase factors. In this paper, the performance the OFDM system by using modified PTS technique combined with interleaving and pulse shaping is evaluated by employing Least Square (LS) and Minimum Mean Square Error (MMSE) channel estimation methods. Mean Square Error (MSE) and Bit error rate (BER) are the performance measures that are employed to analyse the performance of the OFDM system. The simulation result shows that the performance of OFDM system improves with PAPR reduction.

An orthogonal frequency division multiplexing (OFDM) is one of the effective techniques used in wireless communications. In OFDM systems, channel impairments due to multipath dispersive wireless channels can cause deep fades in wireless channels. The OFDM receiver also requires an accurate and computationally efficient channel state information when coherent detection is involved. Therefore, it needs a good robust estimation method of the channel in wireless communication for OFDM systems. And one of these channel estimation methods is minimum mean square error (MMSE) channel estimation. MMSE channel estimation one most used method in OFDM systems. In this work we enhanced robustness of MMSE channel estimation by using it in base of quasi-cyclic low density parity check (QC-LDPC) coded OFDM system.

This paper investigates low complexity detection for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with co-channel interference (CCI). We firstly derive minimum mean square error (MMSE) detection based on sorted QR decomposition (SQRD) and modified MMSE SQRD detection for MIMO-OFDM systems with CCI. Although better performance can be achieved by the new detectors than liner MMSE (LMMSE) detection, the computational complexity is still high due to the required matrix inversion operation. In order to overcome such an issue and design low complexity detection method for MIMO-OFDM systems with CCI, then, we propose an improved MMSE SQRD detector by utilizing a regularization method. The simplification of the regularization parameter calculation in the proposed method is then discussed. Simulation results show that the error performance of the proposed method approaches modified MMSE SQRD detection in presence of CCI, while keeping a much lower complexity.

Differential Evolution (DE) is a novel but powerful optimizer. It has been proved that the DE is superior to Genetic Algorithm (GA) in many real life applications. This paper proposed a memetic Differential Evolution assisted Minimum Bit Error Rate (MBER) linear detector for Multiple-input and multiple-output (MIMO) multi-user system. Compared with GA based MBER and other linear MIMO detectors such as minimum mean square error (MMSE) and zero forcing(ZF)detectors, the proposed scheme shows a better performance in terms of the bit error rate.

Adaptive beamforming for binary phase shift keying communication systems

In this paper, we derive the achievable rate of single-carrier frequency domain equalizer (SC-FDE) and orthogonal frequency division multiplexing (OFDM) systems, constrained on low resolution analog to digital converters (ADCs). We assume that there is a full knowledge of channels at both transmitter and receiver sides. We model low resolution ADCs with an additive uniform quantization noise. We consider an ultra-wide band millimeter wave (mm-Wave) system with a frequency selective channel. The channel is based on practical results at mm-Wave frequencies. Our numerical results show that SC-FDE systems with minimum mean square error (MMSE) equalizers outperform OFDM and SC-FDE systems with zero forcing (ZF) equalizers at high signal to noise ratio (SNR) regimes. Moreover, benefits of increasing the ADCs resolution are not significant due to exponentially increment in the power consumption of ADCs.

An analytical study is presented on the performance of the variable spreading factor orthogonal frequency and code division multiplexing (VSF-OFCDM) system with hybrid multi-code interference (MCI) cancellation and minimum mean square error (MMSE) detection. The weights of MMSE are derived, and should be updated stage by stage of MCI cancellation. Various values of time and frequency domain spreading factors are dealt with in a QPSK-modulated VSF-OFCDM system. It is shown that the hybrid detection scheme performs much better than the conventional MMSE. For the hybrid detection, a larger value of the frequency domain spreading factor is expected.

Linear precoding is a well known effective technique to boost the performance of orthogonal frequency-division multiplexing (OFDM) systems. A drawback of linearly precoded OFDM (LP-OFDM) systems is the high computational complexity required by maximum-likelihood (ML) detection, which is mandatory to capture all the channel diversity. Conversely, low-complexity techniques, such as the linear minimum mean-squared error (MMSE) detection, suffer from nonnegligible performance loss with respect to the ML performance. This paper proposes a detection technique that performs a local ML (LML) search in the neighborhood of the output provided by the MMSE detector. The trade-off between performance and complexity of the proposed LML-MMSE detector, which fall between the ones of the MMSE and ML detectors, can be nicely adjusted by appropriately setting the neighborhood size. Simulation results show that the LML-MMSE detector with minimum neighborhood size outperforms a block decision-feedback equalization (DFE) approach, while preserving a similar complexity.

The paper considers the problem of constructing adaptive minimum bit error rate (MBER) neural network equalisers for binary signalling. Motivated from a kernel density estimation of the bit error rate (BER) as a smooth function of training data, a stochastic gradient algorithm called the least bit error rate (LBER) is developed for adaptive nonlinear equalisers. This LBER algorithm is applied to adaptive training of a radial basis function (RBF) equaliser in a channel intersymbol interference (ISI) plus co-channel interference setting. A simulation study shows that the proposed algorithm has good convergence speed, and a small-size RBF equaliser trained by the LBER can closely approximate the performance of the optimal Bayesian equaliser. The results also demonstrate that the standard adaptive algorithm, the least mean square (LMS), performs poorly for neural network equalisers because the minimum mean square error (MMSE) is clearly suboptimal in the equalisation setting.

OFDM(Orthogonal Frequency Division Multiplexing) 시스템에서 직교 불균형 문제는 송수신기의 front-end에서 발생하며, 성상도에 영향을 주게 되어 BER(Bit Error Rate)을 증가시킨다. 또한, 위상 잡음은 송수신시 국부 발진기에서 발생되는 잡음으로 각 부반송파의 직교성을 깨뜨림으로써 시스템 성능을 크게 저하시킨다. 기존 방식인 PNS(Phase Noise Suppression) 알고리즘은 이러한 위상 잡음을 효과적으로 제거하는 방법이지만 직교 불균형 이동시에 적용되면 오히려 성능이 감소된다. 본 논문에서는 OFDM 시스템의 수신기에서 하향 변환 시 발생하는 직교 불균형과 위상 잡음의 영향을 분석하고, 수신기 FFT(Fast Fourier Transform) 후단에서 파일럿 심볼을 사용하여 CPE를 먼저 제거하고 직교 불균형과 위상 잡음의 성분을 검출하여 등화기의 판정 기준으로 사용하여 보상하는 방법을 제시하였다. 또, 다른 기존 방식들은 FFT 후단에서 추정하고 피드백 시키거나 프리엠블과 같은 시퀀스를 사용하는 방식이지만, 본 논문에서는 FFT 후단에서 MMSE 등화기만을 사용하여 제거하므로 기존의 방법보다 복잡도가 줄어든다. 기존의 위상 잡음 제거 방식에 ICI(Inter Carrier Interference) 제거 기능을 추가하고 직교 불균형 성분을 추출하여 MMSE(Minimum Mean Square Error) 과정 중에 적응 forgetting factor를 적용하면 성능 개선과 직교 불균형 성분의 영향이 줄어들며 성능이 개선됨을 보인다. In the OFDM system, IQ imbalance problem happens at the RF front-end of transceiver, which degrades the BER(bit error rate) performance because it affects the constellation in the received signal. Also, phase noise is generated in the local oscillator of transceivers and it destroys the orthogonality between the subcarriers. Conventional PNS algorithm is effective for phase noise suppression, but it is not useful anymore when there are jointly IQ(In-phase and Quadrature) imbalance and phase noise. Therefore, in this paper, we analyze the effect of IQ imbalance and phase noise generated in the down-conversion of the receiver. Then, we estimate and compensate the IQ imbalance and phase noise at the same time. Compared with the conventional method that IQ imbalance after IFFT is estimated and compensated in front of FFT via the feedback, this proposed method extracts and compensates effect of IQ imbalance after FFT stage. In case IQ imbalance and phase noise exist at the same time, we can decrease complexity because it is needless to use elimination of IQ imbalance in time domain and training sequences and preambles. Also, this method shows that it reduces the ICI and CPE component using adaptive forgetting factor of MMSE after FFT.

This paper analyzes the performance of space-time decision feedback equalization (STDFE) assisted multiuser detection (MUD) for multiple-antenna space division multiple access (SDMA) systems to improve system capacity over dispersive fading channels. The MUD-STDFE receiver consists of a bank of matched filters and symbol-spaced feedforward filters (FFF) that both spatially and temporally whitens noise and the precursive parts of multiple access interference (MAI), followed by a bank of causal feedback filters (FBF) to cancel postcursive ISI and MAI for each user. The algorithm for computing this multi-dimensional FFF and FBF coefficients is presented, based on minimum mean-squared error (MMSE) optimality criterion. The error probability is estimated accurately and efficiently using the Gauss quadrature rule (GQR). The proposed MUD-STDFE receiver is capable of improving the performance significantly, separating the users and offering a significant performance gain relative to linear MMSE detection in an SDMA system.

Precoding for multiple-input multiple-output (MIMO) spatial multiplexing generally requires high feedback overhead and/or high-complexity processing. Simultaneous reduction in transmitter complexity and feedback overhead is proposed by imposing a diagonal structural constraint to precoding, i.e., power allocation. Minimum bit-error rate (MBER) is employed as the optimization criterion, and an approximate MBER (AMBER) power-allocation algorithm is proposed for a variety of receivers, including zero-forcing (ZF), successive interference cancellation (SIC), and ordered SIC (OSIC). While previously proposed precoding schemes either require ZF equalization for MBER, or use a minimum mean-squared error (MMSE) criterion, we provide a unified MBER solution to power allocation for ZF, SIC, and OSIC receiver structures. Improved error-rate performance is shown both analytically and by simulation. Simulation results also indicate that SIC and OSIC with AMBER power allocation offer superior performance over previously proposed MBER precoding with ZF equalization, as well as over MMSE precoding/decoding. Performance under noisy channels and power feedback is analyzed. A modified AMBER algorithm that mitigates error propagation in interference cancellation is developed. Compared with existing precoding methods, the proposed schemes significantly reduce both transmit processing complexity and feedback overhead, and improve error-rate performance

The conventional decision feedback equaliser (DFE) that employs a linear combination of channel observations and past decisions is considered. The design of this class of DFE is to construct a hyperplane that separates the different signal classes. It is well known that the popular minimum mean square error (MMSE) design is generally not the optimal minimum bit error rate (MBER) solution. A strategy is proposed for designing the DFE based on support vector machines (SVMs). The SVM design achieves asymptotically the MBER solution and is superior in performance to the usual MMSE solution. Unlike the exact MBER solution, this SVM solution can be computed very efficiently.