Multiple-input Multiple-output Orthogonal Frequency Division Research Articles

Performance trade-off for integrated communication and data-assisted positioning in MIMO-OFDM system

Communication and positioning, the two pillars of mobile communication systems, are currently being integrated together. The development of communication technologies is the driving force of the positioning progress. In turn, the location information provided by positioning improves the communication performance in various ways. However, the competition of these two functions in terms of resource allocation is a critical issue hindering their integration. In this article, we investigate the trade-off for the integrated communication and data-assisted positioning in multiple-input multiple-output orthogonal frequency division multiplexing systems. A data-assisted positioning method is designed first, which uses both positioning reference signals (PRSs) and data signals for positioning. The positioning and communication performance are theoretically evaluated respectively, then combined to obtain an integrated performance metric. The trade-off is analyzed and the integrated performance is optimized considering the priority of different functions. Numerical simulations show that the data-assisted positioning can not only improve the positioning accuracy, but also reduce the PRS overhead. And the established integrated performance metric can identify the optimal performance and the corresponding resource allocation schemes.

Proactive Eavesdropping in Massive MIMO-OFDM Systems via Deep Reinforcement Learning

It is challenging to monitor suspicious links in massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems with directional beamforming. When the monitor and suspicious user are not in the coverage of the same beam, to implement legitimate monitoring, we propose the proactive eavesdropping scheme in which the monitor acts as a spoofing relay to realize beam misleading and data eavesdropping. In the phase of beam sweep, the beam misleading algorithm induces the transmitter to choose the beam which is beneficial to the monitor by optimizing the relay precoding matrices. In the phase of data transmission, the data eavesdropping algorithm elevates the monitoring rate by optimizing the relay power split factors and power gain factors of all subcarriers. Due to the unknown channel state information between suspicious pairs, we propose to find the optimal precoders and power split factors by the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The simulation results verify the effectiveness and superiority of the proposed eavesdropping scheme.

Robust fingerprint localization based on massive MIMO systems

Location-based services have become an important part of the daily life. Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich scattering environment. In this paper, a single-site multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is modeled, from which an angle delay channel power matrix (ADCPM) is extracted. Considering the changing environment, auto encoders are used to generate new fingerprints based on ADCPM fingerprints to improve the robustness of the fingerprints. When the scattering environment has changed beyond a certain extent, the robustness will not be able to make up for the positioning error. Under this circumstance, an updating of the fingerprint database is imperative. A new fingerprint database updating algorithm which combines a new clustering method and an updating rule based on probability is proposed. Simulation results show the desirable performance of the proposed methods.

A systematic literature review on channel estimation in MIMO-OFDM system: performance analysis and future direction

Abstract Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a familiar modern wireless broadband technology due to its resistance against multipath fading, high data transmission rate, and spectral efficiency. This technology delivers dependable communication as well as a large range of coverage. The precise recovery of Channel State Information (CSI) and synchronization among the receiver and transmitter are two major challenges for MIMO-OFDM systems. Several estimate procedures, like blind, pilot-aided, and semi-blind channel estimating, are used to recover channel state information. Yet, those systems have several flaws that cause them to perform poorly. Hence, this paper describes the basic introduction of the Channel Estimation (CE) process in the MIMO-OFDM system. The main goal of this survey is to study analyzing and categorizing the channel estimation algorithms, and simulation tools in different contributions. Further, the performance analysis with different performance metrics from diverse contributions is pointed out. Thus, this review article presents a detailed overview of the various channel estimation schemes that have been exploited in the OFDM channel to enhance the estimation of the CSI in the MIMO-OFDM systems. This work presents and discusses the relevant comparative results and computational complexity for all of these CE systems. Furthermore, there is a list of open study directions for further exploration.

Performance Analysis of ML-based Low Complex CFO Estimation for MIMO-OFDMA Uplink Systems

Carrier frequency offset (CFO) estimation in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is investigated in this study. MIMO-OFDM is very sensitive to CFOs due to oscillator frequency mismatch and/or Doppler shift. Inaccurate CFO estimation results in intercarrier interference (ICI) through the loss of orthogonality among subcarriers. In this paper, the performance of the ML and APFE algorithm is analyzed for CFO estimation. ML becomes extremely complex due to the multidimensional exhaustive search issue, which is the basic concern in ML estimation. However, making use of the iterative low complex APFE method, here this multidimensional search is replaced with a sequence of mono-dimensional searches. This results in an estimation algorithm of reasonable complexity which is suitable for practical applications. In addition, ML accuracy is compared with the Cramer-Rao bound (CRB).

Adaptive Pilot Allocation for Estimating Sparse Uplink MU-MIMO-OFDM Channels

We consider uplink multiuser multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication. The transmit (Tx) side of the envisaged system consists of several single-antenna users or/and several multiple-antenna users. At the receive side, a multiple-antenna access point employs compressive sensing techniques to estimate the channel impulse response from the preamble portion of the observed packets. The traditional approach is that of orthogonal pilot allocation: during a short training period, each OFDM subcarrier is assigned exclusively to a single Tx antenna. In this case, the channel state information can conveniently be acquired on a per Tx antenna basis. To the best of our knowledge, all related research imposes that all Tx antennas are allocated the same amount of pilots (which must then be tailored for the most extreme channel conditions). However, in the considered system, Tx antennas may experience totally different channel conditions. Under these circumstances, the use of a fixed number of pilots per Tx antenna results in a lot of unnecessary overhead. To tackle this problem, our work addresses the design of efficient algorithms for adaptive orthogonal pilot allocation. The following design principles are applied: orthogonal pilot allocation, constant-modulus modulation, minimum measurement matrix mutual coherence optimization, and the condition that the number of pilot subcarriers allocated to each Tx antenna is adjusted to the channel conditions experienced by that Tx antenna. The paper tackles the problem of determining the optimal number of pilot subcarriers as well as the optimal positions of the pilots. To facilitate adaptive operation, we propose a reduced-complexity method to determine the optimal pilot positions. The performance of our algorithms is demonstrated by means of computer simulations, using both theoretical channel models and results from our own channel measurement campaign.

Generalized superimposed training for RIS-aided cell-free massive MIMO-OFDM networks

In this paper, a generalized superimposed training (GST) is proposed for an uplink cell-free multiple-input multiple-output orthogonal frequency-division multiplexing (mMIMO-OFDM) system, which is aided by reconfigurable intelligent surfaces (RISs) to enhance the spectral efficiency in the system. For the GST scheme, the pilots and data symbols are transmitted simultaneously in the coherence time. This scheme is different from traditional separate transmission methods, such as regular pilots (RP) transmission. In the OFDM multi-carrier case, a part of the subcarriers is based on the GST, whereas the other part of subcarriers is used for data transmission only. The channel and data estimations are carried out and the normalized mean-squared error (NMSE), bit error rate (BER), and sum-rate in different schemes are compared. Different receiver cooperation levels are analyzed in this case, including fully centralized processing and local processing. The distributed time processing and iterative process are also used to improve the performance of the data estimation in this system.

Massive Connectivity Over MIMO-OFDM: Joint Activity Detection and Channel Estimation With Frequency Selectivity Compensation

In this paper, we study how to efficiently and reliably detect active devices and estimate their channels in a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) based grant-free non-orthogonal multiple access (NOMA) system to enable massive machine-type communications (mMTC). First, by exploiting the correlation of the channel frequency responses in narrow-band mMTC, we propose a block-wise linear channel model. Specifically, the continuous OFDM subcarriers in the narrow-band are divided into several sub-blocks and a linear function with only two variables (mean and slope) is used to approximate the frequency-selective channel in each sub-block. This significantly reduces the number of variables to be determined in channel estimation and the sub-block number can be adjusted to reliably compensate the channel frequency-selectivity. Second, we formulate the joint active device detection and channel estimation in the block-wise linear system as a Bayesian inference problem. By exploiting the block-sparsity of the channel matrix, we develop an efficient turbo message passing (Turbo-MP) algorithm to resolve the Bayesian inference problem with near-linear complexity. We further incorporate machine learning approaches into Turbo-MP to learn unknown prior parameters. Numerical results demonstrate the superior performance of the proposed algorithm over state-of-the-art algorithms.

DATA TRANSMISSION IN MIMO-OFDM TECHNOLOGIES FOR CURRENT AND FUTURE WIRELESS COMMUNICATION SYSTEMS: FROM THEORY TO PRACTICE

This paper presents a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) communication system with space-time and space-Frequency block coded (STBC/SFBC) of present generation downlink channel. Space-time block coding (STBC) and space-frequency block coding are transmit diversity schemes used in a wireless communication system to mitigate the effect of channel fading. The system is aimed to reduce the error rate (BER) by mitigating the effect of multipath fading which increase the quality of the system. In the proposed system, 2 transmit and 2 receive antennas are used, to transmit modulated symbols of block N across an OFDM channel, using SFBC Alamouti’s based encoding algorithm (SFBC-OFDM) derives from the basic criteria for space-time block codes (STBC). The signals from the first and second antennas are encoded and transmitted simultaneously in space and frequency over frequency-selective Rayleigh fading channel using two adjacent carriers. The work examines the SFBC and spatial diversity benefits realized based on the BER performance. The result is simulated effectively using MATLAB application software and the BER performance is compared with the conventional STBC-OFDM. It is shown that both STBC and SFBC techniques achieve similar performance with a diversity gain of four (4) over a slow fading channel with 2 antennas at both ends of the link under simple processing across the transmit and receive antennas.

Sparse Bayesian Learning Based Channel Extrapolation for RIS Assisted MIMO-OFDM

Reconfigurable intelligent surface (RIS) is a revolutionary technology and can be used to assist communication systems by adaptively manipulating the wireless environment. In this paper, we propose a novel cascaded channel estimation algorithm for the RIS-assisted multiple-input multiple-output orthogonal frequency division multiplexing systems. Inspired by the channel compression idea, we can obtain a sub-sampled channel by turning off a fraction of RIS elements and then extrapolate it to the complete one, by which the pilot overhead is greatly reduced. The problem of channel extrapolation is transformed into recovering the physical parameters of the cascaded channel from the partial channel observations and is then formulated by the sparse Bayesian learning (SBL) framework. In order to circumvent the curse of high dimensional matrices inversion in the vector-matrix system, we further introduce the tensor structure into the SBL framework. Specially, by leveraging of the channel sparsity over the angle domain and delay domain, we derive the virtual channel expression in tensor form and model a Kronecker-structured prior distribution for the virtual channels. The multi-domain sparse properties of the virtual channel tensor can be effectively captured by a group of low-dimensional hyper-parameters, and thus reduce the computational complexity. In addition, the Cramer-Rao lower bound is derived for the proposed channel extrapolation. Simulation results show the superior performance of the proposed scheme.

Model-Driven Deep Learning-Based MIMO-OFDM Detector: Design, Simulation, and Experimental Results

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth consumed by the cyclic prefix (CP). We use the iterative orthogonal approximate message passing (OAMP) algorithm in this paper as the prototype of the detector because of its remarkable potential for interference suppression. However, OAMP is computationally expensive for the matrix inversion per iteration. We replace the matrix inversion with the conjugate gradient (CG) method to reduce the complexity of OAMP. We further unfold the CG-based OAMP algorithm into a network and tune the critical parameters through deep learning (DL) to enhance detection performance. Simulation results and complexity analysis show that the proposed scheme has significant gain over other iterative detection methods and exhibits comparable performance to the state-of-the-art DL-based detector at a reduced computational cost. Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver architecture to remove the CP overhead. This architecture first eliminates the intersymbol interference by buffering the previously recovered data and then detects the signal using the proposed detector. Numerical experiments demonstrate that the designed receiver offers a higher spectral efficiency than traditional receivers. Finally, over-the-air tests verify the effectiveness and robustness of the proposed scheme in realistic environments.

Impact of Subcarrier Allocation and User Mobility on the Uplink Performance of Multiuser Massive MIMO-OFDM Systems

This paper considers the uplink performance of a multi-user massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system with mobile users. Mobility brings two major problems to a MIMO-OFDM system: inter carrier interference (ICI) and channel aging. In practice, it is common to allot multiple contiguous subcarriers to a user as well as schedule multiple users on each subcarrier. Motivated by this, we consider a general subcarrier allocation scheme and derive expressions for the ICI power, uplink signal to interference plus noise ratio and the achievable uplink sum-rate, taking into account the ICI and the multi-user interference due to channel aging. We show that the system incurs a near-constant ICI power that depends linearly on the ratio of the number of users per subcarrier to the number of subcarriers per user, nearly independently of how the UEs distribute their power across the subcarriers. Further, we exploit the coherence bandwidth of the channel to reduce the length of the pilot sequences required for uplink channel estimation. We consider both zero-forcing and maximal-ratio combining at the receiver and compare the respective sum-rate performances. In either case, the proposed subcarrier allocation scheme leads to significantly higher sum-rates compared to previous work, owing to the near-constant ICI property as well as the reduced pilot overhead.

PAPR Reduction and Bit Error Rate Evaluation in OFDM System using Hybrid Techniques

Orthogonal frequency division multiplexing (OFDM) has become an important component of waveform generation in wideband transmission. it's a superior technology for the high-speed rate of wired and wireless communication systems. Currently, multiple-input multiple-output orthogonal frequency division multiplexing (MIMO OFDM) systems are crucial wireless communication systems like 4G and 5G networks & tactical communication. The OFDM has many advantages over other techniques like its high capacity and immunity against multipath fading channels. However, one amongst the foremost drawbacks of the OFDM system is that the high-peak-to-average power ratio (PAPR) that leads the system to provide in-band distortion and out-of-band radiation and reduces its efficiency. This problem increases with a rise within the number of transmit antennas. Therefore, it's highly desirable to cut back the PAPR of an OFDM signal. For this, numerous techniques are proposed to beat the PAPR problem like i) Selective mapping (SLM) ii) Partial transmit sequence (PTS), iii) Clipping, iv) Clipping and filtering. All of those are reduced the PAPR by generating alternative subcarrier vector that are statistically independent OFDM symbols for a given data frame and transmitting the symbol with rock bottom peak power. During this paper we also proposed, some hybrid techniques. The hybrid techniques are the technique of clipping is employed in conjunction with SLM and PTS to cut back computational complexity. And also the combination of SLMPTS to scale back PAPR. Simulations are acquainted with analyze the efficiency of the techniques used which signifies OFDM to be providing much better PAPR reduction and a way better Bit Error Rate (BER). it's shown in simulation results that the proposed scheme performs well reducing PAPR. But the proposed scheme is more complex than the prevailing techniques.

Conformal IRS-Empowered MIMO-OFDM: Channel Estimation and Environment Mapping

We consider the channel estimation and environment mapping problems in multiple-input multiple-output orthogonal frequency division multiplexing systems empowered by intelligent reconfigurable surfaces (IRSs). In order to acquire more in-depth environmental information, as well as, to flexibly take into account existing real-life infrastructure, we propose a novel three-dimensional conformal IRS architecture consisting of reflective unit cells distributed on curved surfaces. We model the training signal as a third-order canonical polyadic tensor and construct a tensor factorization problem. Given specific conditions on the allocated temporal-frequency training resources, we develop four channel estimation approaches, i.e., least squares, direct, wideband direct and wideband subspace methods, by leveraging tensor techniques and nonlinear system solvers. By fully exploiting the characteristics of conformal IRSs, we propose two decoupling modes to precisely recover the multipath parameters without ambiguities, which cannot be supported by the traditional IRS planar topologies. We implement scatterer mapping and user positioning tasks based on precise parameter estimates. Simulation results indicate that the proposed conformal IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of environment mapping.

Independent vector analysis based blind interference reduction and signal recovery for MIMO IoT green communications

In application to time convolutive mixing model or frequency domain blind separation model for wireless receiving applications, frequency domain independent component analysis (FDICA) has been a very popular method but with adverse random permutation ambiguity influence. In order to solve this inherent problem in FDICA assisted multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) based the Internet of Thing (IoT) systems, this paper proposes an new detection mechanism, named independent vector analysis (IVA), for realizing blind adaptive signal recovery in MIMO IoT green communication to reduce inter-carrier interference (ICI) and multiple access interference (MAI). IVA jointly implements separation work for different frequency bin data while the FDICA deals with it separately. In IVA, the dependencies of frequency bins can be exploited in mitigating the random permutation problem. In addition, multivariate prior distributions are employed to preserve the inter-frequency dependencies for individual sources, which can result in separation performance enhancement. Simulation results and analysis corroborate the effectiveness of the proposed method.

A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.

Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach

Multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) is a key technology component in the evolution towards cognitive radio (CR) in next-generation communication in which the accuracy of timing and frequency synchronization significantly impacts the overall system performance. In this paper, we propose a novel scheme leveraging extreme learning machine (ELM) to achieve high-precision synchronization. Specifically, exploiting the preamble signals with synchronization offsets, two ELMs are incorporated into a traditional MIMO-OFDM system to estimate both the residual symbol timing offset (RSTO) and the residual carrier frequency offset (RCFO). The simulation results show that the performance of the proposed ELM-based synchronization scheme is superior to the traditional method under both additive white Gaussian noise (AWGN) and frequency selective fading channels. Furthermore, comparing with the existing machine learning based techniques, the proposed method shows outstanding performance without the requirement of perfect channel state information (CSI) and prohibitive computational complexity. Finally, the proposed method is robust in terms of the choice of channel parameters (e.g., number of paths) and also in terms of “generalization ability” from a machine learning standpoint.

MIMO-OFDM Modulation Classification Using Three-Dimensional Convolutional Network

Automatic modulation classification (AMC) plays a vital role in cognitive radio to improve spectrum utilization efficiency, however, most of the existing works have focused on single-carrier communications in single-input single-output systems. In this paper, we propose an efficient AMC method for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) communication systems with the assumption of unknown frequency-selective fading channels and signal-to-noise ratio. At the receiver, the complex envelope samples of a burst signal acquired by multiple antennas are decomposed into in-phase and quadrature samples, which are then structured into a high-dimensional data array. To learn the modulation patterns from received signals, we develop a deep network, namely three-dimensional MIMO-OFDM convolutional neural network (MONet). With cuboidal convolution filters, the proposed MONet allows the network to capture underlying features as intra- and inter-antenna correlations at multi-scale signal representations. Relying on simulations, MONet achieves the classification accuracy of over 95% at 0 dB SNR under various channel impairments and shows the robustness with different MIMO antenna configurations.

The Effectiveness of LDPC Decoding Algorithms in 5G Channel Modelling of MIMO-OFDM System under the Influence of Spatial Correlation

This paper investigates the spatial cross-correlation properties of 5G channel modeling in non-line of sight (NLOS) of Urban Micro Cell (UMi), Rural Macro Cell (RMa) and Indoor Cell (InH) at 6 GHz frequency band as Third Generation Partnership Project (3GPP) specification. In our spatial correlation Multiple-input multiple-output (MIMO) channel, when increasing the distances of BS antenna elements, the correlation characteristics of the channel dramatically vary, which leads to altering the observed system behavior. By analyzing the 5G LDPC code and using different decoding algorithms and code rates, we evaluate the system’s performance by Bit Error Rate (BER) in a wideband correlated Multiple-input multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system. In our spatial correlation, the more rising of the distances of antenna elements on the BS side, the better the effectiveness of the LDPC decoding algorithms achieved. Of the Belief Propagation based (BP-based) Algorithm, Offset Minimum-Sum (OMS) Algorithm and Linear Approximation Minimum-Sum (LAMS) Algorithm, we propose to use the LAMS decoding algorithm with a high code rate of 5/6 in our spatial correlated wideband channel because of lower complexity and more efficiency in term of the system’s performance.

Channel Estimation and User Localization for IRS-Assisted MIMO-OFDM Systems

We consider the channel estimation problem and the channel-based wireless applications in multiple-input multiple-output orthogonal frequency division multiplexing systems assisted by intelligent reconfigurable surfaces (IRSs). To obtain the necessary channel parameters, i.e., angles, delays and gains, for environment mapping and user localization, we propose a novel twin-IRS structure consisting of two IRS planes with a relative spatial rotation. We model the training signal from the user equipment to the base station via IRSs as a third-order canonical polyadic tensor with a maximal tensor rank equal to the number of IRS unit cells. We present four designs of IRS training coefficients, i.e., random, structured, grouping and sparse patterns, and analyze the corresponding uniqueness conditions of channel estimation. We extract the cascaded channel parameters by leveraging array signal processing and atomic norm denoising techniques. Based on the characteristics of the twin-IRS structures, we formulate a nonlinear equation system to exactly recover the multipath parameters by two efficient decoupling modes. We realize environment mapping and user localization based on the estimated channel parameters. Simulation results indicate that the proposed twin-IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of user positioning.