時間-空間-都卜勒超寬頻系統(UWB)設計應用於漸褪頻道

In a companion paper, we have studied the zero-forcing (ZF) transceiver with decision feedback equalizer (DFE) over slowly time-varying narrowband multiinput multioutput (MIMO) channels. The space-time generalized triangular decomposition (ST-GTD) was used for the design of ZF-DFE transceivers. The space-time geometric mean decomposition (ST-GMD) ZF transceiver minimizes both the arithmetic mean square error (MSE) at the feedback detector and the average uncoded bit error rate (BER) in moderate high signal-to-noise ratio (SNR). This paper addresses the design problem of DFE transceiver without zero-forcing constraint. In the first part, a channel independent temporal precoder is superimposed on the conventional block-wise GMD-based minimum mean square error (MMSE) DFE transceiver to take advantage of the temporal diversity. In the second part, ST-GTD is applied for the design of MMSE DFE transceivers. With accurate channel prediction and space-time powerloading, the proposed ST-GMD MMSE transceiver minimizes the arithmetic MSE at the feedack detector, and maximizes Gaussian mutual information. For practical applications, the ST-GTD MMSE transceiver which does not require channel prediction but shares the same asymptotic BER performance with the ST-GMD MMSE system is also developed. In the convex region, our analysis shows that the proposed MMSE transceivers has better BER performance than the conventional GMD-based MMSE transceiver; the average BERs of the proposed systems are nonincreasing functions of the ST-block size. The superior performance of ST-GMD MMSE transceiver over the ST-GMD ZF transceiver is also verified analytically.

Linear minimum mean-squared error (LMMSE)-based channel equalization is widely used in multi-input multioutput (MIMO) underwater acoustic communications (UAC). The practical challenge of LMMSE based schemes is the necessity of matrix inversion which generally imposes heavy computational burden on the receiver. To obtain the LMMSE filters efficiently, we exploit the conjugate gradient method and the diagonalization properties of circulant matrices. The proposed scheme is based on fast Fourier transform operations and can be implemented in parallel, which makes it a promising candidate for real-time MIMO underwater acoustic communications. Both numerical and SPACE'08 experimental examples are presented to demonstrate the effectiveness of the proposed approach.

We generalize the multiuser (CDMA spread spectrum) communication systems to the fading environments. We first extend Verdu's (1986) conventional optimum receiver to Rayleigh-fading environments and then evaluate its performance. Having no knowledge of the received power at the receiving end, we therefore need an estimator to efficiently estimate the received signal strength of each user in fading environments. A linear minimum mean square error (LMMSE) unbiased estimator is proposed to attain this goal. By using the minimum mean square error (MMSE) Bayesian estimation, we further propose the LMMSE bit estimator for efficient demodulation. Its performance is close to the optimum multiuser receiver but with a much simpler polynomial complexity. To further reduce the complexity, we extend the LMMSE estimator to the sequential LMMSE estimator. In sequential estimation, we do not need to implement the matched filter banks and to perform the matrix inversion when estimating. In addition, it converges after approximately 2k iterations, where k is the number of users. With this fast convergence property and the simple structure, the sequential LMMSE estimator provides an attractive alternative to the implementation of a multiuser system.

In this contribution, we investigate the performance of dual-polarized multi input multi output (MIMO) multi-carrier interleave division multiple access system (MC-IDMA) over correlated frequency selective channels in the presence of channel state estimation error. In the code division multiple access (CDMA), orthogonal spreading sequence is used as user specific where as in IDMA, user specific interleaving pattern is used. In our considered work, Multi-carrier (MC) is realized by orthogonal frequency division multiplexing (OFDM). The channel is estimated at the mobile stations (MSs) by pilot assisted channel estimation (PACE) technique using minimum mean square error (MMSE) algorithm. Further, the dual-polarized MIMO system considered in our work which is realized with the aid of orthogonal polarizations, replaces two spatially separated uni-polarized antennas, thus resulting in polarization multiplexing. In particular, we evaluate the effects of three types of delay spread on MIMO MC-IDMA system in the context of downlink (DL) communications pertaining to the Standard University Interim (SUI) channel model. In a multi-user environment, users experience multi stream interference (MSI) in addition to the multiple access interference (MAI) and these interferences can degrade the system performance. Furthermore, we consider turbo code as channel encoder for MIMO MC-IDMA system to mitigate the effects of MAI and MSI. It is discerned from the simulation results that dual-polarized MIMO MC-IDMA system is capable of supporting many users and performs better in terms of achievable bit error rate (BER) in the context of DL communications with reduced number of antennas.

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.

Performance analysis of multicell multiuser MIMO MC DS/CDMA system with MMSE user-ordered SIC technique

Multicell joint processing has been shown to efficiently suppress inter-cell interference, while providing a high capacity gain due to spatial multiplexing across distributed Base Stations (BSs). However, the complexity of the optimal joint decoder in the multicell uplink channel grows exponentially with the number of users, making it prohibitive to implement in practice. In this direction, this paper investigates the uplink capacity performance of multicell joint linear minimum mean square error (LMMSE) filtering, followed by single-user decoding. The considered cellular multiple-access channel model assumes both Rayleigh and Rician flat fading, path loss, distributed users and correlated multiple antennas at the base station side. The case of Rayleigh fading is tackled using a free probability approach, while the case of Rician fading is addressed through a deterministic equivalent calculated using non-linear programming techniques. In this context, it is shown that LMMSE can provide high spectral efficiencies in practical macrocellular scenarios.

We propose the Optimum Power Allocation (OPA) scheme for Distributed Antenna Systems(DAS) in the time-varying Rayleigh fading channel. Recently, the OPA schemes which uses the Channel State Information (CSI) including a small scale (fast) fading have been proposed. However, the channel is changing vary fast over time due to small scale fading, therefore Bit Error Rate (BER) increases. Because of this reason, we derive the OPA for minimizing BER in DAS, which only uses a large scale fading to CSI and excepts a small scale fading. The simulation results show that the proposed OPA achieves better BER performance than conventional OPA considering a small scale fading in time-varying Rayleigh fading channel, and also has similar performance in Rayleigh flat-fading environment. The BER performance of proposed OPA which derived in Rayleigh fading channel is similar to minimum BER of Ricean fading channel which has small Line-of-Sight (LOS).

This paper proposes a new precoding approach that utilizes two-dimension (2D) Angle of Arrival (AOA) based precoding (beamforming) approach for multi-cell massive Multiple Input Multiple Output (m-MIMO) systems. The proposed approach enhances the performance of m-MIMO systems by overcoming pilot contamination that is caused by corrupted Channel State Information. It exploits the spatial properties of the propagated signals to distinguish between various users in the multi-cell m-MIMO system and selects the partial space for beamforming based on AOA information to suppress inter-cell and intra-cell interference. We mathematically analyze the impact of pilot contamination in the uplink training and determine the closed-form approximation of uplink Minimum Mean Square Error (MMSE) estimator. By using MMSE estimator as well as 2D AOA and channel estimation, a corresponding MMSE precoding which minimizes the effect of pilot contamination and improves the downlink achievable rate of the system is proposed. Furthermore, a comparison is made of the resolution between the various 2D AOA estimations for an m-MIMO and the optimum values closest bound for the proposed precoding was chosen to reduce pilot contamination. The performance of the proposed MMSE precoding based on 2D AOA estimation methods: 2D Unitary Estimation of Signal Parameters via Rotational Invariance Techniques (2D UESPRIT), 2D Fourier Domain Line Search MUSIC (2D FDLSM), and 2D Propagator Method (2D PM)) were evaluated and compared with pilot contaminated system and deterministic MMSE precoding in terms of their respective achievable rate. Simulation results reveal that the achievable sum rate gains of 2D UESPRIT, 2D FDLSM, and 2D PM based precoding which achieved 97.6%, 92.4%, 86.3% of the desired deterministic performance, respectively, compared to pilot contaminated system, which achieved only 90% even when in the best case of having only one contaminated user out of all users within the activated cell. Findings from our research indicate the feasibility of utilizing the AOA based beamforming to reduce pilot contamination effect that is inherent in a larger order m-MIMO system for the Fifth Generation (5G) wireless transmissions.

With the increasing rate of wireless devices and high bandwidth operations, wireless networking and communications are becoming over crowded. To cope with such crowded and messy situation, massive MIMO is designed to work with hundreds of low-cost serving antennas at a time as well as improve the spectral efficiency at the same time. Time Division Duplexing (TDD) has been used for gaining beamforming which is a major part of massive MIMO, to gain its best improvement to transmit and receive pilot sequences. All the benefits are only possible if the channel state information or channel estimation is gained properly. Least Square (LS), Minimum Mean Square Error (MMSE) and Linear Minimum Mean Square Error (LMMSE) are the estimators commonly used for estimating channel matrices. We have optimized these methods using genetic algorithm to minimize the mean squared error and finding the best channel matrix from existing algorithms with less computational complexity. Our simulation result has shown that the use of Genetic Algorithm (GA) worked well on existing algorithms in a Rayleigh slow fading channel and existence of Additive White Gaussian Noise. We found that the GA optimized LS is better than existing algorithms. GA provides optimal result in some few iterations in terms of Mean Square Error (MSE) with respect to SNRand computational complexity.

General-purpose processor (GPP), which is suitable for software radio processing because of its powerful processing capability and flexibility, has aroused industry-wide attention and exploration. By taking advantage of streaming single instruction multiple data extensions (SSE), this paper presents the GPP-based design of multi-input multi-output (MIMO) detection which is of high complexity in LTE downlink receiver. The performance simulation results illustrate that with dramatically reduced complexity soft minimum mean square error (MMSE) detection is able to approximate ML performance in low signal-to-noise-ratio (SNR), and about 4 dB lose in high SNR. The complexity comparison results show that the implementation of soft MMSE detection in parallel can reduce the processing time by about 86% compared with not in parallel.

The Spatial Modulation (SM) transmission scheme boosts the spectral efficiency and achieves the multiplexing gain by activating a single transmit antenna in each time slot. Radio wave propagation characteristics determined by the environment is a decisive factor for the SM system. In this paper, we investigate the performance of the SM scheme over real-world 4×4 Multi-Input Multi-Output (MIMO) channels measured in typical indoor scenarios. Firstly, a MIMO channel sounder is established. Based on the sounder, 15150 complex 4×4 MIMO channel matrices are measured inside a typical teaching building under both Line of Sight (LOS) and None Line of Sight (NLOS) scenarios. Secondly, by comparing the SM system over the measured channel and commonly-used channel models, we prove that both the Independently and Identically Distribute (i.i.d.) Rayleigh and the Spatial Correlation (SC) channel model are oversimplified, and that only practical experiences can yield definitive answers to the achievable real-world system performance. Thirdly, the Average Bit Error Probability (ABEP) performance of the SM system is studied based on the measured data under a variety of system configurations. The study of different receive antenna array settings (4×4, 4×2 and 4×1 MIMO setups) approves the significance of combining scheme at the receiver. SM systems employing different signal constellations (Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (16QAM)) are also investigated and some interesting results are revealed. Lastly, performance assessment of SM against State-Of-The-Art (SOTA) MIMO schemes (Space-Time Block Code (STBC) and Vertical Bell Labs Layered Space-Time (V-BLAST) code) is conducted. Results show that for a 4×4 MIMO, the low-complexity SM scheme outperforms both STBC and V-BLAST.

The two major obstacles toward high-capacity indoor wireless networks are distortion due to the indoor channel and the limited availability of bandwidth which necessitates a high spectral efficiency. A combined orthogonal-frequency division multiplexing/spatial-diversity multiple access (OFDM/SDMA) approach can effectively tackle both obstacles. The channel distortion due to multipath propagation is easily mitigated with OFDM while the bandwidth efficiency can be increased with the use of SDMA. In order to keep the network's cost acceptable, simplified SDMA processors are preferred over the exponentially complex optimal maximum-likelihood processors. However, these simplified processors perform significantly worse than the optimal ones in terms of average bit error rate (BER). In this paper, we show that by adapting the constellation sizes applied on the individual subcarriers to the channel conditions, the performance of OFDM/SDMA processors can be significantly enhanced. In the uplink, we derive simple closed-form equations for the optimal constellation sizes for both a simple linear minimum mean-square error (MMSE) detector and for a nonlinear MMSE decision feedback equalizer (DFE) detector. Furthermore, for each detector, we introduce a simplified loading algorithm which lowers the computational and signaling complexity substantially at a small performance penalty. In the downlink, we study the dual precoders of the uplink detectors, respectively, the linear MMSE precoder and the nonlinear Tomlinson-Harashima (TH) MMSE-based precoder. For both precoders, we derive expressions for the optimal and the simplified constellation sizes. Additionally, we show that in time-division duplexing systems, the constellation distribution of a set of dual detectors/precoders is identical for up- and downlink, which effectively halves the computational complexity of adaptive loading. In the fully loaded uplink, the proposed adaptive loading algorithm results in a gain of 9 dB for a BER=10/sup -3/ for the linear MMSE detector and a gain of 4.5 dB for the nonlinear MMSE-DFE detector. In the fully loaded downlink, a gain of 6.3 dB is achieved for the MMSE precoder and 5.5 dB for the TH-MMSE precoder.

The Global Positioning System (GPS) is one of the core technologies for the Location Based Service (LBS), and has various applications from the leisure business to the military. LBS requires accurate location information of an object, which GPS estimates using at least four satellites. In order to enhance the GPS signal quality, angle-of-arrival (AOA) information is required, but these are difficult to estimate because the system operates with a low signal-to-noise ratio (SNR) and in the presence of high-power interference. In this paper, we explore an extension of a recently proposed GPS AOA estimation technique based on a modified despreader for multiple GPS signals. Since the extended estimator does not require a singular value decomposition (SVD), unlike conventional AOA estimation algorithms such as Multiple Signal Classification (MUSIC), it has a relatively low computational complexity. Also, this technique does not need to choose the GPS AOAs from all estimated AOAs because those of the interference signals are not estimated. We demonstrate the performance of the GPS AOA estimation algorithm for multiple satellites via a computer simulation example.

The non-line-of-sight (NLOS) propagation caused by intermittently blocking of the direct path between the base station and the mobile station is the main difficulty for mobile user location in cellular wireless communication system. So it is indispensable to investigate the techniques about the NLOS error identification and correction. In this paper, we suggest a robust and accurate approach using probability density function (PDF) estimator to identify the NLOS situation, and then an improved biased Kalman filter which depends on PDF estimator as the nonlinearity weighting coefficient in recursive operation is used to mitigate the NLOS error for time of arrival (TOA) measurements. As the PDF estimator is calculated from actual TOA samples, it is adaptive for the complex practical NLOS environments. The simulation results also indicate that with the less prior information about communication environments, the approach we present has a good location performance even in severe NLOS situations.