Multi-input Multi-output Orthogonal Frequency Division Multiplexing Systems Research Articles

MIMO Signal Detection Based on IM-LSTMNet Model

Signal detection is crucial in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, yet classical detection methods often struggle with nonlinear issues in wireless channels. To handle this challenge, we propose a novel signal detection method for MIMO-OFDM system based on the fractional Fourier transform (FrFT), leveraging the robust time series processing capabilities of long short-term memory (LSTM) networks. Our innovative approach, termed IM-LSTMNet, integrates LSTM with convolutional neural networks (CNNs) and incorporates a Squeeze and Excitation Network to emphasize critical information, enhancing neural network performance. The proposed IM-LSTMNet is applied to the FrFT-based MIMO-OFDM system to improve signal detection performance. We compare the detection results of IM-LSTMNet with zero forcing (ZF), minimum mean square error (MMSE), simple LSTM neural network, and CNN–LSTM network by evaluating the bit error rate. Experimental results demonstrate that IM-LSTMNet outperforms ZF, MMSE, LSTM, and other methods, significantly enhancing system signal detection performance. This work offers a promising advancement in MIMO-OFDM signal detection, presenting a deep learning-based solution that effectively improves the system signal detection performance.

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.

Hierarchical BEM Based Channel Estimation With Very Low Pilot Overhead for High Mobility MIMO-OFDM Systems

Channel estimation for multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is a challenging issue in high mobility scenarios due to prohibitive pilot overhead and complexity. In this paper, a hierarchical basis expansion model (BEM) based channel estimation scheme is proposed for doubly selective channels, which requires a low pilot overhead and computational complexity. A mixed-BEM is proposed for time-varying channels alongside a complex exponential-BEM (CE-BEM), resulting in very few unknown parameters required for channel estimation. Based on the hierarchical BEM structure, an analytical channel estimation model is derived. By employing the block-structured sparsity and a small number of mixed-BEM coefficients in the antenna-time-BEM domain, a low-complexity estimator is proposed to recover the mixed-BEM coefficients accurately. The lower bound on the mean square error (MSE) of channel estimation is derived to verify the effectiveness of the proposed scheme. Simulation results show that the proposed channel estimation scheme significantly outperforms the existing methods in terms of MSE of channel estimation and bit error rate, with much lower pilot overhead and computational complexity.

DFT-Based Channel Estimation with Channel Response Mirroring for MIMO OFDM Systems

In this paper, DFT-Based channel estimation with channel response mirroring is proposed and analyzed. In General, pilot symbols for channel estimation in MIMO(Multi-Input Multi-Output) OFDM(Orthogonal Frequency-Division Multiplexing) Systems have a diamond shape in the time-frequency plane. An interpolation technique to estimate the channel response of sub-carriers between reference symbols is needed. Various interpolation techniques such as linear interpolation, low-pass filtering interpolation, cubic interpolation and DFT interpolation are employed to estimate the non-pilot sub-carriers. In this paper, we investigate the conventional DFT-based channel estimation for noise reduction and channel response interpolation. The conventional method has performance degradation by distortion called “edge effect” or “border effect”. In order to mitigate the distortion, we propose an improved DFT-based channel estimation with channel response mirroring. This technique can efficiently mitigate the distortion caused by the DFT of channel response discontinuity. Simulation results show that the proposed method has better performance than the conventional DFT-based channel estimation in terms of MSE.

CFO robust blind receivers for MIMO-OFDM systems based on PARALIND factorizations

Carrier frequency offsets (CFO) is a well known impairment that destroys the orthogonality amongst subcarriers in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, and the resulting inter-carrier interference significantly degrades the performance of such systems. This paper proposes the CFO robust blind receivers for MIMO-OFDM systems based on parallel profiles with linear dependencies (PARALIND) factorizations. First, we model MIMO-OFDM signal in the presence of CFO to satisfy the PARALIND model, where the symbols, the CFO, and the MIMO channel matrices can be viewed as three independent factor matrices. Such a hybrid tensorial modeling enables a blind and joint estimation of symbols and CFO. Then, in order to improve the fitting precision of the PARALIND model, we introduce two algorithms for fitting the PARALIND model. The first one, called delta alternating least squares (DALS) algorithm, where we predict coarsely by exploiting the increment values between two iterations of the factor matrices, refine these predictions by using the enhanced line search and use these refined values to initialize factor matrices, this procedure helps us avoid the random initialization found in the traditional ALS algorithm. The second one, called orthogonal DALS (ODALS) algorithm that uses the solution of orthogonal constraint for the DALS algorithm to provide the initialization for one more factor matrix. The performance of proposed receivers is illustrated by means of simulation results, and a comparison is made with other approaches that allow to estimate symbols and CFO in two separated steps. Beside a robust to CFO characteristics, our approach gives a better BER performance than other compared blind approaches.

Power Optimization in MIMO-OFDM Systems with Mixed Orthogonal Frequency Division and Space Division Multiple Access Scheme

In recent years, energy efficiency has become a critical metric for green system design and drawn universal attention. In this paper, we propose a new radio resource allocation algorithm to minimize the power consumption of the base station for the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with different quality of service constraints of users. The discontinuous transmission (DTX) technique is applied in the systems with mixed orthogonal frequency division access and space division multiple access. Radio resources in time, frequency and space domain are optimally allocated to users in two steps: In the first step, the active time in the DTX mode is estimated under the assumption that every user experiences slow time-varying fading in an OFDM frame. In the second step, subcarriers, bits and power are allocated in the active time slots. Although the proposed algorithm is suboptimal due to high computation complexity, simulation results show that the proposed algorithm can reduce the power consumption of the base station and improve resource utilization.

Pilot Design for Sparse Channel Estimation in Large-Scale MIMO-OFDM System

The pilot design problem in large-scale multi-input-multioutput orthogonal frequency division multiplexing (MIMO-OFDM) system is investigated from the perspective of compressed sensing (CS). According to the CS theory, the success probability of estimation is dependent on the mutual coherence of the reconstruction matrix. Specifically, the smaller the mutual coherence is, the higher the success probability is. Based on this conclusion, this paper proposes a pilot design algorithm based on alternating projection and obtains a nonorthogonal pilot pattern. Simulation results show that applying the proposed pattern gives the better performance compared to applying conventional orthogonal one in terms of normalized mean square error (NMSE) of the channel estimate. Moreover, the bit error rate (BER) performance of the large-scale MIMO-OFDM system is improved.

Energy-Efficient Resource Allocation for MIMO-OFDM Systems Serving Random Sources With Statistical QoS Requirement

This paper optimizes resource allocation that maximizes the energy efficiency (EE) of wireless systems with statistical quality of service (QoS) requirement, where a delay bound and its violation probability need to be guaranteed. To avoid wasting energy when serving random sources over wireless channels, we convert the QoS exponent, a key parameter to characterize statistical QoS guarantee under the framework of effective bandwidth and effective capacity, into multi-state QoS exponents dependent on the queue length. To illustrate how to optimize resource allocation, we consider multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. A general method to optimize the queue length based bandwidth and power allocation (QRA) policy is proposed, which maximizes the EE under the statistical QoS constraint. A closed-form optimal QRA policy is derived for massive MIMO-OFDM system with infinite antennas serving the first order autoregressive source. The EE limit obtained from infinite delay bound and the achieved EEs of different policies under finite delay bounds are analyzed. Simulation and numerical results show that the EE achieved by the QRA policy approaches the EE limit when the delay bound is large, and is much higher than those achieved by existing policies considering statistical QoS provision when the delay bound is stringent.

Signal Detection in MIMO-OFDM Systems Based on SSDE Algorithm

Device-to-device communication enables to improve the application performance of multi-input multi-output (MIMO) technology. In a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, its performance is largely reflected by the signal detection algorithm used in receiver. As a sub-optimal maximum likelihood (ML) detection method, the Selective Spanning with Fast Enumeration algorithm can be successfully applied in MIMO-OFDM systems with high-order modulation. However, its Fast Enumeration scheme calculates constellation points based on fixed formula, which tends to yield pseudo constellation points outside of constellation maps, and consequently cannot work well in low-order modulation. To address this, a selective spanning with direct enumeration (SSDE) algorithm is proposed in this paper. Simulation results proved that the SSDE can achieve similar detection performance at a much lower computational cost in comparison with the ML method. The performance in terms of bit error rate (BER) obtained by SSDE method is also superior to those from the Minimum mean square error and Zero forcing detection algorithms with a huge savings in computational load. By adjusting the parameters used in the SSDE, the tradeoff between BER and computation complexity can be flexibly changed to satisfy specific design requirements in different applications.

VLSI Design and Investigation of an Area Efficient and Low Power MOD-R2MDC FFT for MOMO-OFDM

In this paper, an area-efficient low power fast Fourier transform processor is proposed for multi input multi output-orthogonal frequency division multiplexing (MIMO-OFDM) in wireless communication system. It consists of a modified architecture of radix-2 (R2) algorithm which is described as modified R2 multipath delay commutation (MOD-R2MDC). OFDM is a popular method for high data rate wireless transmission. This paper describes the very large scale integration design of an area efficient MOD-R2MDC FFT for MIMO-OFDM system targeted to future wireless communication systems. The very high speed integrated hardware description language simulation results have been tested practically by implementing in the Altera development and education-2 field programmed gate array (FPGA) development board. Also the existing OFDM system has been tested with these FFT algorithms and their performances were analyzed with respect to occupation of area in FPGA and power consumption. A low-power and area efficient architecture enables the real-time operations of MIMO-OFDM system.

Dynamic pilot allocation with channel estimation in closed‐loop multi‐input–multi‐output orthogonal frequency division multiplexing systems

Dynamic pilot allocation (DPA) for discrete Fourier transform (DFT)-based channel estimation in multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with spatial multiplexing can significantly improve the bit error rate performance compared with systems with uniform pilot allocation. However, the exhaustive search for optimum pilot allocation leads to very high complexity. The authors devise a multi-input-multi-output iterative pilot search (MIPS) algorithm applied with different MIMO-OFDM receivers (linear, successive interference cancellation (SIC) and maximum likelihood (ML)), which significantly reduces the complexity of DPA. Exact derivations are also given based on the receivers. They also propose a novel stacked vector quantisation technique to reduce feedback burdens for DPA in MIMO-OFDM system. Simulation results illustrate that the proposed MIPS algorithm with limited feedback can improve the performance of MIMO-OFDM systems.

A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system

In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.

Pilot Allocation for Sparse Channel Estimation in MIMO-OFDM Systems

The frequency-selective channel-estimation problem in multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is investigated from the perspective of compressed sensing (CS). By minimizing the mutual coherence of the measurement matrix in CS theory, two pilot allocation methods for the CS-based channel estimation in MIMO-OFDM systems are proposed. Simulation results show that using the pilot patterns designed by the two proposed methods gives a much better performance than using other pilot patterns in terms of the mean square error of the channel estimate as well as the bit error rate of the system. Moreover, the optimal pilot patterns obtained by the proposed second method based on genetic algorithm and shift mechanism could offer a larger performance gain than those by the first method based on minimizing the largest element in the mutual coherence set possessed by pilot patterns for all multiple antenna ports.

MIMO-OFDM Transceivers With Dual-Polarized Division Multiplexing and Diversity for Multimedia Broadcasting Services

This brief paper presents multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with dual-polarized division multiplexing (PDM) and diversity for multimedia broadcasting services. In particular, the polarized diversity is realized by transmitting two independent and distributed space-frequency coded signals through two sets of dual-polarized transmit antennas. In the corresponding polarized receptions, MIMO zero-forcing (ZF) detection and ZF detection combined with a group-wise or a symbol-wise successive cancelation (SIC) are used to minimize a depolarization effect caused by a cross-polarization discrimination (XPD). Furthermore, we analyze the output signal-to-interference-plus-noise ratio (SINR) of such MIMO detections in terms of the XPD effect. It is shown that the output SINR of MIMO ZF detection increases as the absolute XPD value increases. It is also shown that the output SINR of ZF-SIC detection converges to that of ZF detection as the absolute XPD value increases. Finally, we compare the performances of uni-polarized and dual-polarized MIMO-OFDM systems over a multipath Rician channel with XPD. From simulation results, we conclude that as the absolute XPD value increases, the polarized MIMO-OFDM systems provide a lower symbol error rate than uni-polarized MIMO-OFDM systems.

An RF Signal Processing Based Diversity Scheme for MIMO-OFDM Systems

This paper proposes a diversity scheme for Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) based on Radio Frequency (RF) signal processing. Although a 2×2 MIMO-OFDM system can double the capacity without expanding the occupied frequency bandwidth, we cannot get additional diversity gain using the linear MIMO decomposition method. The proposed method improves the bit error rate (BER) performance by making efficient use of RF signal processing. Computer simulation results show that the proposed scheme gives additional diversity gain.

Experiments on MIMO-OFDM system combined with adaptive beamforming based on IEEE 802.16e WMAN standard

This paper presents field experiments on a Multi-Input Multi-Output (MIMO) system that combines Adaptive Beamforming (ABF) and Spatial Multiplexing (SM) procedures. The combination of SM signal processing with ABF is applied to WiBro, the South Korean Orthogonal Frequency Division Multiplexing (OFDM) system that follows the IEEE 802.16e standard. The field experimental results show that ABF-MIMO OFDM system outperforms a simple MIMO OFDM system by 2 dB (1.5 dB) in the signal to noise ratio (SNR) for 16-QAM (64-QAM) under low correlated fading channel and 4 dB (2.5 dB) in the SNR for 16-QAM (64-QAM) under highly correlated fading channel, respectively, at the frame error rate (FER) of 1%. Details on the implementation of ABF-MIMO OFDM system is also presented in this paper. Through the system implementation and its field experimental results, we verify that the combination of MIMO OFDM system with ABF provides improved performance over a simple MIMO OFDM system in real propagation channel environment and, in particular, it is more effective in highly correlated fading channel.

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.

MIMO OFDM systems with digital RF impairment compensation

Multi-input multi-output (MIMO) systems are often realized with low cost front-end architectures, e.g. the so-called direct conversion (or zero IF) architectures. However, such systems are very sensitive to imperfections in the analog front-end resulting in radio frequency (RF) impairments such as in-phase/quadrature-phase (IQ) imbalance and carrier frequency offset (CFO). These RF impairments can result in a severe performance degradation. In this paper we propose RF impairment compensation techniques for orthogonal frequency division multiplexing (OFDM) based MIMO systems. We consider a digital compensation scheme for joint transmitter/receiver frequency selective IQ imbalance, CFO and channel distortion. We also show that in the case where there is no transmitter IQ imbalance, the receiver IQ imbalance compensation can be de-coupled from the channel equalization resulting in a compensation in two stages. The two-stage scheme results in an overall lower computational requirement. The various compensation schemes are demonstrated to provide a performance close to the ideal case without RF impairments.

Blind Carrier Frequency Offset Estimation via Power Spectrum Analysis in MIMO OFDM Systems

As a generalization of orthogonal frequency-division multiplexing (OFDM) systems, multi-input multi-output (MIMO) OFDM systems are very sensitive to carrier frequency offset (CFO). This paper proposes a blind CFO estimation method based on power spectrum analysis, which has high bandwidth efficiency and is much less complex. This method can be used to estimate the residual CFO, which is less than half of the subcarrier spacing. The method uses a cosine cost function to get a closed-form CFO estimate. Simulation results illustrate that the method is effective for MIMO OFDM systems.

Peak-to-average power ratio reduction in space–time block coded multi-input multi-output orthogonal frequency division multiplexing systems using a small overhead selective mapping scheme

The selective mapping (SLM) scheme is one of the most popular peak-to-average power ratio (PAPR) reduction techniques proposed for multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. One of the major disadvantages of this scheme is the need for the transmission of side information (SI) bits to enable the receiver to recover the transmitted data. The authors present a small overhead SLM (s-SLM) scheme for space-time block coded (STBC) MIMO-OFDM systems. This proposed scheme improves the system bandwidth efficiency and achieves a significantly lower bit error rate (BER) than the individual SLM (i-SLM) and direct SLM (d-SLM) schemes. In addition, approximate expressions for the complementary cumulative distribution function (CCDF) of the PAPR and the average BER of the proposed s-SLM scheme are derived. The simulation results show that the proposed s-SLM scheme improves the detection probability of the SI bits and hence gives a better performance than the i-SLM and the d-SLM schemes.