Multi-input Multi-output Orthogonal Frequency Research Articles

MIMO–OFDM System Analysis and Achievement

Multi-Input–Multi-Output (MIMO) technology has already become crucial in modern communication study, but the needs of anti-interference capability and spectral efficiency are always growing, which poses a significant challenge in future development. In the paper, by analyzing and summarizing the respective characteristics and problems of MIMO system and Orthogonal Frequency Division Multiplexing (OFDM), we investigate the knowledge of MIMO–OFDM system. The theoretical analysis proves that this system can effectively solve the shortcomings of MIMO system and improve the performance of communication system. In addition, in the experimental simulation part, we also evaluate the system performance mainly by analyzing the Bit Error Rate (BER) metrics, while other methods such as channel estimation algorithms and synchronization techniques are also considered.

Statistical modelling of a non‐linear high‐power amplifier with memory in multi‐input–multi‐output orthogonal frequency division multiplexing systems

A simple and accurate statistical model (SASM) for a high-power amplifier (HPA) with memory in multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed. This model is statistically equivalent to the more complex generalised memory polynomial (GMP) model, which is one of the most accurate models. The HPA distortion is modelled using stationary white Gaussian noise sources that pass through finite impulse response (FIR) filters. The filter coefficients and the variance of noise sources are derived analytically from the GMP model parameters by spectral decomposition of the GMP noise power spectrum. The number of FIR filters is equal to the memory length of the GMP model. The HPA effect on the OFDM signal, considered as frequency-dependent attenuation coefficients, is modelled using another FIR filter. These FIR filters with small lengths make the model computationally inexpensive and mathematically tractable. The main characteristics of this model are identical bit error rate (BER) and error vector magnitude of subcarriers with those in the GMP model. The proposed model is the simplest representation of a HPA with memory effects. An excellent agreement between the simulation results of the SASM and GMP model is observed. This verifies the accuracy and efficiency of the proposed method.

Optimal control with monotonicity constraints for a parallel-server loss channel serving multi-class jobs

We study a parallel-server loss channel serving multi-class jobs, which appears in many real-world systems, e.g., cloud computing, multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM), and call centre. An -discounted optimal control with monotonicity constraints (OCMC) model over infinite time horizon is established by using the physical queueing model with linear revenue function. Existence of a solution to the OCMC model is proved, whose optimal value provides an upper bound of the corresponding values of the physical queueing model under Markovian decision rules. Algorithms with lower complexity in solving the OCMC model are proposed, which are further used to design an admission control policy for the loss channel. Furthermore, a simulation algorithm is proposed to implement the designed policy. Performance comparisons through numerical examples are conducted among our newly designed policy, the first-in first-out (FIFO) policy, an arbitrarily selected (AS) policy, and the Markov decision process (MDP) based threshold policy. Advantages and disadvantages of these policies are identified under different channel parameters and channel (e.g., Markovian and non-Markovian) conditions. Particularly, we find out that our designed policy outperforms the other three policies when the traffic intensity is relatively large, and the differences of the revenues per unit of time and the penalty costs among different classes of jobs are large.

Decoupled compensation of IQ imbalance in MIMO OFDM systems

The direct-conversion architecture is an attractive front-end design for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. These systems are typically small in size and provide a good flexibility to support growing number of wireless standards. However, direct-conversion based OFDM systems are generally very sensitive to front-end component imperfections. These imperfections are unavoidable especially when cheaper components are used in the manufacturing process and can lead to radio frequency (RF) impairments such as in-phase/quadrature-phase (IQ) imbalance. These RF impairments can result in a severe performance degradation. In this paper, we propose training based efficient compensation schemes for MIMO OFDM systems impaired with transmitter and receiver frequency selective IQ imbalance. The proposed schemes can decouple the compensation of the transmitter and receiver IQ imbalance from the compensation of the channel distortion. It is shown that the proposed schemes result in an overall lower training overhead and a lower computational requirement as compared to a joint estimation/compensation of IQ imbalance and the channel distortion.

Blind carrier frequency offset estimator for multi-input multi-output-orthogonal frequency division multiplexing systems over frequency-selective fading channels

This study presents a new blind carrier frequency offset (CFO) estimation technique for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems employing space-time coding (STC). CFO estimation is crucial for OFDM systems to avoid the performance degradation because of the inter-carrier interference that results when the CFO is not estimated and compensated accurately. Based on the assumptions that the data symbols are selected from a constant modulus constellation and the channel is varying slowly over time, a new blind CFO estimator is proposed by minimising the power difference between all subcarriers in two consecutive STC blocks. Therefore the proposed system exploits all subcarriers in time and frequency domain, which provides a remarkable performance improvement over other techniques reported in the literature. The complexity of the proposed estimator is substantially reduced by approximating the cost function by a sinusoid that can be minimised using direct closed-form computations within one OFDM symbol period. Monte Carlo simulations are used to assess the performance of the proposed system by means of mean squared error (MSE) in both static and time-varying frequency-selective fading channels. The simulation results demonstrate that the proposed estimator can eliminate the MSE error floors that usually appear at moderate and high signal-to-noise ratios for the estimators that work only in frequency domain.

OPTIMAL TRAINING DESIGN FOR MIMO–OFDM SYSTEMS UNDER SPATIALLY CORRELATED DOUBLY SELECTIVE FADING CHANNELS

In this paper, we design an optimal training scheme for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems under spatially correlated time- and frequency- (doubly) selective fading channels. We first develop the optimal pilot symbols and placement of pilot clusters with respect to the minimum mean square error (MMSE) of the linear channel estimate. We then derive the optimal power allocation for pilot symbols in a two-water-level way: by maximizing the averaged capacity lower bound, how much power to be allocated for training is determined subject to the global water level (or the constraint of total transmit power); subsequently, pouring power to the pilot symbols with an approximately optimal water-filling scheme subject to the local water level (or the constraint of assigned power for training). In addition, for a particular OFDM size, the optimal number of pilot clusters is derived by maximizing the capacity lower bound and by minimizing the channel estimate's MMSE.

Doubly selective fading channel estimation in MIMO OFDM systems

High data transmission rates and high mobility give rise to time- and frequency-selectivity in wireless communication channels. This paper investigated time and frequency doubly selective channel estimation using pilot tones among Multi-Input Multi-Output (MIMO) orthogonal frequency division multiplexing (OFDM). Firstly, a complex exponential basis expansion channel model (BECM) was introduced to represent doubly selective channel during an OFDM symbol period; then, based on BECM, an effective MIMO-OFDM doubly selective channel estimation method was presented; finally, the optimality in designing pilot tones parameters was done according to the minimization of channel estimation MSE, mainly including the number, the placement and the structure of pilot tones. Simulation results show that the proposed estimation method has good performance in doubly selective channel scenarios and confirms the theoretical analysis findings.