Golay Pair Aided Timing Synchronization Algorithm for Distributed MIMO-OFDM System

In order to solve the problem of inaccurate synchronization for distributed multiple-input multiple-output(MIMO) orthogonal frequency division multiplexing(OFDM) system in both multipath and low signal to noise ratio(SNR) channels, a golay pair aided timing synchronization(GPATS) method is proposed in this paper. A new synchronous training sequence based on the golay pair with guard interval is designed in GPATS method. By utilizing the unique properties of the new training sequence, the different timing point and the inter-transmitter delays(ITD) are obtained at the receiver. Simulation results show that, compared with the traditional synchronization approaches, the proposed algorithm can provide high accuracy in detecting different time offsets caused by the distributed transmitters of the MIMO-OFDM system, especially over multipath and low SNR channels.

Improved Hybrid Precoding Technique with Low-Resolution for MIMO-OFDM System

This paper proposes an improved hybrid beamforming system based on multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system.The proposed beamforming system improves energy efficiency compared to the conventional hybrid beamforming system.Both sub-connected and full-connected structure are considered to apply the proposed algorithm.In the conventional hybrid beamforming,the usage of radio frequency(RF)chains and phase shifter(PS)gives high power and hardware complexity.In this paper,the phase over sampling(POS)with switches(SW)is used in hybrid beamforming system to improve the energy efficiency.The POS-SW structure samples the value of analog beamformer to make lower resolution than conventional system.The number of output data in POS is decided by the resolution of POS system.The limited number of POS decides the resolution of antenna array and the values of POSs are designed from maximum and minimum phase angle antenna array.Energy efficiency without the phase shifter is high although channel capacity is nearly similar with conventional system.Also,the amplifier with POS-SW system is proposed to improve the BER performance.According to the data bits,the output signals of POS are decided.The system with 2,3 and 4 bits is simulated to prove the proposed algorithm.In order to overcome the loss of low-resolution system,the amplifier with POS-SW system using channel information is proposed.The average sum-rate of 4 bits system shows the similar performance with the conventional hybrid beamforming system.This structure can play an important role by increasing the energy efficiency of the wireless communication system that many antennas are used.It is shown that the BER,average sum rate and energy efficiency of the proposed scheme are more improved than the conventional hybrid beamforming system.

Extended Kalman filtering-based channel estimation for space-time coded MIMO-OFDM systems

A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.

RLS channel estimation with adaptive forgetting factor in space-time coded MIMO-OFDM systems

Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.

Bayesian sequential state estimation for MIMO-OFDM systems

For the estimation of MIMO frequency selective channel, to mitigate the curse of dimensionality, a novel particle filtering scheme combined with time delay domain processing is proposed. In order to extract the time delay domain channel impulse response from the observed signal, the least-squares （LS） and minimum mean squared error （MMSE） criteria are discussed and the comparable performance of LS with MMSE for sample- spaced channel is revealed. Incorporated the dynamical channel model, gradient particle filtering is further introduced to improve the estimation performance. The robustness of the channel estimator for underestimated Doppler frequency and the effectiveness of the new estimation scheme are illustrated through simulation at last.

E^2KF Based Joint Multiple CFOs and Channel Estimate for MIMO-OFDM Systems over High Mobility Scenarios

An enhanced extended Kalman filtering （E2KF） algorithm is proposed in this paper to cope with the joint multiple carrier frequency offsets （CFOs） and time-variant channel estimate for MIMO-OFDM systems over high mobility scenarios. It is unveiled that, the auto-regressive （AR） model not only provides an effective method to capture the dynamics of the channel parameters, which enables the prediction capability in the EKF algorithm, but also suggests an method to incorporate multiple successive pilot symbols for the improved measurement update.

Sparse channel estimation for MIMO-OFDM systems using distributed compressed sensing

A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.

Channel estimation for MIMO-OFDM systems in rapid fading channels

A channel estimation approach for orthogonal frequency division multiplexing with multiple-input and multipleoutput （MIMO-OFDM） in rapid fading channels is proposed. This approach combines the advantages of an optimal training sequence based least-square （OLS） algorithm and an expectation-maximization （EM） algorithm. The channels at the training blocks are estimated using an estimator based on the OLS algorithm. To compensate for the fast Rayleigh fading at the data blocks, a time domain based Gaussian interpolation filter is presented. Furthermore, an EM algorithm is introduced to improve the performance of channel estimation by a few iterations. Simulations show that this channel estimation approach can effectively track rapid channel variation.

Low Complexity Multiuser Detection with Recursively Successive Zero-Forcing and SIC Based on Nullspace for Multiuser MIMO-OFDM System

To improve the spectrum efficiency, this paper considers the multiuser detection with the MU-MIMO technology for multiuser MIMO-OFDM system uplink with the same subcarrier shared by multiple users. A low complexity multiuser detection algorithm with recursively successive zero-forcing and successive interference cancellation(RSZF-SIC) based on nullspace is proposed. The RSZF process based on the block diagonalization(BD) technique eliminates the co-channel interference(CCI) by a recursive method based on the nullspace orthogonal theorem. The SIC process detects the user signals respectively with the reasonable user detection sequence based on the results of the RSZF process. The computational complexity of the proposed algorithm is effectively reduced by reducing the total number of singular value decomposition(SVD) operations and the dimension of the SVD matrix in the recursive procedure. The performance of the proposed algorithm is improved in terms of bit error rate and sum capacity of the system, especially in the highSNR regime.

Deep learning-based time-varying channel estimation with basis expansion model for MIMO-OFDM system

For high-speed mobile MIMO-OFDM system,a low-complexity deep learning(DL) based timevarying channel estimation scheme is proposed.To reduce the number of estimated parameters,the basis expansion model(BEM) is employed to model the time-varying channel,which converts the channel estimation into the estimation of the basis coefficient.Specifically,the initial basis coefficients are firstly used to train the neural network in an offline manner,and then the high-precision channel estimation can be obtained by small number of inputs.Moreover,the linear minimum mean square error(LMMSE) estimated channel is considered for the loss function in training phase,which makes the proposed method more practical.Simulation results show that the proposed method has a better performance and lower computational complexity compared with the available schemes,and it is robust to the fast time-varying channel in the high-speed mobile scenarios.

Adaptive bit and power loading algorithm with low complexity in MIMO-OFDM systems

Two adaptive power and bit loading algorithms to maximize the throughput of MIMO-OFDM systems in frequency selective fading environment are proposed. The two algorithms allocate bit based on maximizing the overall throughput. One algorithm allocates power based on guaranteeing that the bit error rate （BER） of each sub-carrier and the total allocated power remain below a target BER threshold and a power threshold, respectively; another one allocates power based on guaranteeing that the mean BER of sub-carriers and the total allocated power remain below a target BER threshold and a power threshold, respectively. The simulation results show that the proposed algorithms can achieve faster throughput with lower computational complexity, which indicates that the proposed algorithms are effective when compared to some existing algorithms.

Channel estimation for MIMO-OFDM systems in mobile wireless channels

A channel estimation method is proposed for nmltiple-input multiple-output orthogonal frequency division muhiplexing （MIMO-OFDM） systems in time-varying fading channels. In this method, a decision-directed space-alternating generalized expectation-maximization （SAGE） algorithm is introduced to the tracking of time-varying fading. In order to improve the estimation performance of the SAGE algorithm, a low rank approximation method is presented by using the signal subspace of the channel frequency autocorrelation matrix. The study reveals that this method can be incorporated into the SAGE algorithm. Furthermore, a modified fast sub- space tracking algorithm is given to adaptively estimate the signal subspace by utilizing training OFDM blocks sent at regular interval. Simulation results demonstrate the considerable benefits of the proposed channel estimation method.

Inter-carrier Interference Analysis for MIMO-OFDM Systems in High-Speed Train Environment

The orthogonality between the subcarriers of multipleinput multiple-output orthogonal frequency division multiplexing( MIMO-OFDM) systems is destroyed due to the Doppler frequency offset,particularly in the high-speed train( HST) environment,which leads to severe inter-carrier interference( ICI). Therefore,it is necessary to analyze the mechanism and influence factor of ICI in HST environment. In this paper, by using a non-stationary geometry-based stochastic model( GBSM) for MIMO HST channels, ICI is analyzed through investigating the channel coefficients and the carrier-to-interference power ratio( CIR). It is a fact that most of signal energy spreads on itself and its several neighborhood subcarriers. By investigating the amplitude of subcarriers, CIR is used to evaluate the ICI power level. The simulation results show that the biggest impact factor for the CIR is the multipath number L and the minimum impact factor K; when the train speed υR> 400 km / h,the normalized Doppler frequency offset ε > 0. 35,the CIR tends to zero,and the communication quality will be very poor at this condition. Finally,bit error rate( BER) is investigated by simulating a specific channel environment.

PCA Application in Channel Estimation in MIMO-OFDM System

Initial estimation is a considerable issue in channel estimation techniques, since all of the following processes depends on it, which in this paper its improvement is discussed. Least Square (LS) method is a common simple way to estimate a channel initially but its efficiency is not as significant as more complex approaches. It is possible to enhance channel estimation performance by using some methods such as principal component analysis (PCA), which is not prevalent in channel estimation, and its adaptation to channel information can be challenging. PCA method improves initial estimation performance by projecting data onto direction of eigenvectors by means of using simple algebra. In this paper, channel estimation is examined in Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, with significant advantages such as an acceptable performance in frequency selective fading channel. Moreover the proposed channel estimation method manipulates the benefits of MIMO channel by using the information, gained by all channels to estimate the information of each receiver.

Accurate channel estimation for MIMO-OFDM system by exploiting cyclic prefix

Multiple-input-multiple-output orthogonal frequency division multiplexing （MIMO-OFDM） systems promise to provide significant increase in system capacity for future wireless communication systems. The channel state information is required to achieve the high capacity of an MIMO-OFDM system. In this pa-per, an improved channel estimation scheme is proposed for MIMO-OFDM system by making full use of the training sequence and CP （cyclic prefix）. The method improves the performance of the channel esti-mator because of using the redundant information in CP. Also, the theoretical mean square error （MSE） bound of the improved estimator is derived. The effectivity of the algorithm is demonstrated by the simula-tion results of MIMO-OFDM systems with two transmit and two receive antennas. The MSE gain is en-hanced by about 1dB.

JOINT ESTIMATION OF CARRIER FREQUENCY OFFSET AND POWER DELAY PROFILE FOR MIMO-OFDM SYSTEMS

This paper develops a Cyclic Prefix(CP)based joint Maximum-Likelihood(ML)estima-tion algorithm of Carrier Frequency Offset(CFO)and Power Delay Profile(PDP)for Multi-InputMulti-Output Orthogonal Frequency Division Multiplexing(MIMO-OFDM)systems.However,theexact solution of the joint ML estimation is very complex since it needs a search over amulti-dimensional domain.Thus a simplified method is proposed to estimate the CFO and the PDPiteratively via the alternating-projection method which could induce the multidimensional searchproblem to a sequence of simple one-dimensional searches.Simulations show that the proposed algo-rithm is more accurate and robust than the existing algorithms.

Iterative Detection and Decoding with PIC Algorithm for MIMO-OFDM Systems

In this paper, we propose a new iterative detection and decoding scheme based on parallel interference cancel (PIC) for coded MIMO-OFDM systems. The performance of proposed receiver is improved through the joint PIC MIMO detection and iterative detection and decoding. Its performance is evaluated based on com-puter simulation. The simulation results indicate that the performance of the proposed receiver is greatly im-proved compared to coded MIMO-OFDM systems based on VBLAST detection scheme.

CHANNEL ESTIMATION WITH CIRCULARLY SLIPPING WINDOW IN MIMO-OFDM SYSTEMS

Channel estimation is very important for MIMO (Multiple Input Multiple Output) OFDM (Or-thogonal Frequency Division Multiplexing) systems, but its precision is reduced due to the noise in channel. In this letter, circularly slipping window is introduced to resist the noise. It can be proved by simulation that with the same channel model, optimal slipping window length is the same with different vehicle speed. MSE (Minimum Square Error) of channel is greatly reduced with circularly slipping window, and performance of the system is closed to that with correct channel estimation.

Efficient Bandwidth and Power Allocation Algorithms for Multiuser MIMO-OFDM Systems

This paper studies the problem of finding an effective subcarrier and power allocation strategy for downlink communication to multiple users in a MIMO-OFDM system with zero-forcing beamforming. The problem of minimizing total power consumption with constraint on transmission rate for users is formulated. The problem of joint allocation is divided into two stages. In the first stage, the number of subcarriers that each user will get is determined based on the users’ average signal-to-noise ratio. In the second stage, it finds the best assignment of subcarriers to users. The optimal method is a complex combinatorial problem which can only be assuredly solved through an Exhaustive Search (ES). Since the ES method has high computational com-plexity, the normalized user selection algorithm and the simplified-normalized user selection algorithm are proposed to reduce the computational complexity. Simulation results show that the proposed low complexity algorithms offer better performance compared with an existing algorithm.

A NEW UEP SCHEME FOR ROBUST VIDEO TRANSMISSION IN LDPC CODED MIMO-OFDM SYSTEM

In order to improve the video transmission performance in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing （MIMO-OFDM） system, a new scheme, which integrates Multiple Description Coding （MDC）, Low Density Parity Check （LDPC） coding and hybrid space time coding, is proposed in this letter. In particular, a hybrid space time coding algorithm is combined with LDPC codes to perform Unequal Error Protection （UEP） of MDC encoded video streams. Comparing with the UEP transmission with only LDPC codes, the proposed scheme achieves more than ldB gain in terms of Signal to Noise Ratio （SNR） when the Peak Signal to Noise Ratio （PSNR） of reconstructed video is above 30dB.