Multiple Input And Multiple Output Orthogonal Frequency Division Multiplexing System Research Articles

BER And PAPR Performance Analysis Of Mimo-Ofdm Systems Using Equalizers

MIMO-OFDM (multiple input and multiple output Orthogonal Frequency Division Multiplexing) system is a new wireless broadband technology which became famous for being capable of high data rate transmission & robustness against multi-path fading.

PAPR reduction of MIMO-OFDM system with reduced computational complexity SLM scheme

Multiple input and multiple output orthogonal frequency division multiplexing (MIMO-OFDM) is a prominent technology for fourth generation mobile radio communication. OFDM is a special case of multi-carrier-modulation (MCM) technique that divides the high data rate input bit streams into large number of lower rate sub streams and each sub stream is transmitted over orthogonal subcarriers. The main advantage of MIMO-OFDM system is the efficient utilization of band width, multipath delay spread tolerance and mitigation of inter symbol interference (ISI) in frequency selective fading channels. However, the main drawback of MIMO-OFDM system is high peak to average power ratio (PAPR) for large number of sub carriers which causes serious degradation in performance. One of the well-known peak to average power ratio (PAPR) reduction schemes for MIMO-OFDM systems is selected mapping (SLM) scheme. In this work computational complexity of MIMO-OFDM system is reduced by adding mapping signal sequences to the OFDM signal sequences. It also increases the PAPR reduction performance of the MIMO-OFDM system.

Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network

Due to the nonlinear distortion caused by radio-frequency (RF) components in the transceiver, detecting transmitted symbols for multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems can be challenging and resource consuming. In this work, we introduce a Deep Echo State Network (DESN) to serve as the symbol detector for 5G communication networks. Our DESN employs memristive synapses as the dynamic reservoir layer to accelerate the learning algorithm and computation. By cascading multiple dynamic reservoir layers in a hierarchical processing structure, our DESN processes received signal in both spatial and temporal domains. The resulting hybrid memristor-CMOS co-design provides the nonlinear computation required by the reservoir layer while significantly reduces the power consumption. From the benchmark on nonlinear system prediction, our DESN exhibits 10.31 X reduction on the prediction error compared to state-of-the-art neural network designs. Moreover, our DESN records a bit error rate (BER) of $5.76 \times 10^{-2}$ on the high-speed transmitted symbol detection task for MIMO-OFDM systems, yielding 47.73% more precise than state-of-the-art techniques in the literate for 5G communication networks.

Kalman Filter Channel Estimation in 2 × 2 and 4 × 4 STBC MIMO-OFDM Systems

Channel estimation is a challenging problem for space time block coding (STBC) multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in dynamic environments. To handle this problem and improve the system performance, this paper proposes a Kalman filter (KF) based channel estimation method applied to $2\times2$ and $4\times4$ STBC MIMO-OFDM systems. The proposed method based on the dynamic tracking property of KF is well adopted for dynamic channel estimation. First, a new orthogonal space-time codeword is adopted, and the orthogonal pilot sequences are designed to suppress the interference among transmit antennas. Then, the prediction and update characteristics of KF are researched, and the state space model is established for STBC MIMO-OFDM system. Subsequently, the channel state information (CSI) is estimated iteratively according to the KF estimation equation. At last, to further improve the accuracy of KF channel estimation, the threshold is utilized to suppress the noise in the channel impulse response (CIR) estimated by KF method. Simulation results verify that the proposed KF channel estimation method with orthogonal pilots and STBC provides better bit error rate (BER) and normalized mean square error (NMSE) performance compared with other conventional channel estimation methods, and it can be effectively adapted to dynamic multipath propagation conditions with different low and high order modulations.

Deep Learning Based Channel Estimation Algorithm for Fast Time-Varying MIMO-OFDM Systems

Channel estimation is very challenging for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in high mobility environments with non-stationarity channel characteristics. In order to handle this problem, we propose a deep learning (DL)-based MIMO-OFDM channel estimation algorithm. By performing offline training to the learning network, the channel state information (CSI) generated by the training samples can be effectively utilized to adapt the characteristics of fast time-varying channels in the high mobility scenarios. The simulation results show that the proposed DL-based algorithm is more robust for the scenarios of high mobility in MIMO-OFDM systems, compared to the conventional algorithms.

Low Power Consumption Signal Detector Based on Adaptive DFSD in MIMO-OFDM Systems

For a low complexity signal detector to reduce the power consumption for multiple input and multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the depth-first sphere decoding (DFSD) detection scheme was proposed. However, the DFSD detection scheme still has high complexity in the hardware implementation. The complexity is especially high when the signal-to-noise ratio (SNR) is low. Therefore, this paper proposes an adaptive DFSD detection scheme. The proposed detection scheme arrays nodes, sorting by ascending order of squared Euclidean distance (ED) at the top layer of tree structure. Then, the proposed detection scheme uses the different number of nodes according to thresholds based on channel condition. In the simulation results, the proposed detection scheme has similar error performance and low complexity compared with the conventional DFSD detection scheme. Therefore, the proposed detection scheme reduces the power consumption in the signal detector.

Using artificial bee colony algorithm with crossover for power allocation in cognitive MIMO-OFDM system

Artificial bee colony (ABC) algorithm builds on simulating the intelligent behavior of honey bees. It shows good performance in many applications. As standard ABC algorithm does not employ any crossover operator, the dispersal of good genetic information amongst the solutions is undermined. In this paper, the impact of crossover operators on the performance of ABC is studied. Eight crossover operators, representing all kinds of crossover operators, are used in this study. A trial and error method is used to detect the most proper crossover operator and crossover rate for incorporation into the ABC algorithm on mathematical functions as an initial attempt. The overall best configuration of ABC with crossover which has been identified is then applied to solve power allocation problem in cognitive multiple input and multiple output orthogonal frequency division multiplexing (MIMO-OFDM) cognitive system. Promising performances are obtained when compared with those from genetic algorithm, particle swarm optimization and differential evolution algorithm.

Doppler Shift Impact On The MIMO OFDM System In Vehicular Channel Condition

Vehicle-to-vehicle (V2V) communication systems will play an important role in intelligent transportation systems (ITS). But in high mobility road condition, orthogonal frequency division multiplexing (OFDM) is very sensitive to Doppler shift. In this scenario multiple input and multiple output (MIMO) system combined with OFDM, make MIMO -OFDM techniques very attractive and productive for vehicle-to- vehicle communications. This paper deals with the bit error rate (BER) performance analysis of MIMO- OFDM system in high way road condition which is modeled based on the Nakagami fading characteristic. The system performance is analyzed with the change in m value of Nakagami channel and also with the variation in the modulation schemes.

Optimization of pilot tones using differential evolution algorithm in MIMO-OFDM systems

In this paper, we propose a differential evolution (DE) algorithm for optimizing the placement and power of the pilot tones that are utilized by a least square (LS) algorithm for channel estimation in multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Computer simulations demonstrated that the performance of the LS algorithm was increased by optimizing the pilot tones with the DE algorithm instead of locating them orthogonally. We used the upper bound of the mean square error (MSE) as a fitness function of the DE algorithm for optimization tasks. With the use of an upper bound, it is not necessary to compute the matrix inversion that is needed in computing the MSE.

Dual-turbo receiver architecture for turbo coded MIMO-OFDM systems

This paper presents a novel architecture of iterative receivers with two layers of iterations for turbo coded multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, where soft messages are passed not only between the MIMO detector and the turbo decoder, but also between the two component decoders within the turbo decoder. We first derive the factor graph representation of a turbo coded system as a basic building block for developing the iterative receivers. Then, a new soft message passing schedule over the factor graph is proposed, resulting in the proposed dual-turbo receiver architecture (DTRA). In DTRA, the MIMO detector and the turbo decoder work concurrently, and the soft messages for both layers of iterations are updated instantaneously, instead of the block-based exchange of soft messages in the conventional iterative receivers. In so doing, the processing latency can be greatly reduced while low computational complexity can be achieved.

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.