Turbo Message Passing Based Burst Interference Cancellation for Data Detection in Massive MIMO-OFDM Systems

This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.

Nonbinary ldpc-coded probabilistic shaping scheme for MIMO systems based on signal space diversity

This paper first describes a binary Low-Density Parity-Check(LDPC)-coded Probabilistic Shaping(PS)scheme for Multiple-Input Multiple-Output(MIMO)systems based on Signal Space Diversity(SSD).Second,a Nonbinary(NB)LDPC-coded PS scheme for MIMO systems based on SSD is proposed.The first scheme can be used to obtain a shaping gain,whereas the second can also realize a coding gain.The theoretical average mutual information of the optimized rotated quadrature amplitude modulation constellations is analyzed and the simulated error per-formance with 22 and 44 MIMO schemes is investigated.The theoretical average mutual information analysis and simulation results show that the proposed NB LDPC-coded PS scheme for MIMO systems based on SSD is reliable and robust,and is therefore suitable for future wireless communication systems.

A Survey on Channel Measurements and Models for Underground MIMO Communication Systems

The high reliability of the communication system is critical in metro and mining applications for personal safety,channel optimization,and improving operational performance.This paper surveys the progress of wireless communication systems in underground environments such as tunnels and mines from 1920 to 2022,including the evolution of primitive technology,advancements in channel modelling,and realization of various wireless propagation channels.In addition,the existing and advanced channel modeling strategies,which include the evolution of different technologies and their applications;mathematical,analytical,and experimental techniques for radio propagation;and significance of the radiation characteristics,antenna placement,and physical environment of multiple-input multiple-output(MIMO)communication systems,are analyzed.The given study introduces leaky coaxial cable(LCX)and distributed antenna system(DAS)designs for improving narrowband and wideband channel capacity.The paper concludes by figuring out open research areas for the future technologies.

Shannon information capacity of time reversal wideband multiple-input multiple-output system based on correlated statistical channels

Utilizing channel reciprocity, time reversal（TR） technique increases the signal-to-noise ratio（SNR） at the receiver with very low transmitter complexity in complex multipath environment. Present research works about TR multiple-input multiple-output（MIMO） communication all focus on the system implementation and network building. The aim of this work is to analyze the influence of antenna coupling on the capacity of wideband TR MIMO system, which is a realistic question in designing a practical communication system. It turns out that antenna coupling stabilizes the capacity in a small variation range with statistical wideband channel response. Meanwhile, antenna coupling only causes a slight detriment to the channel capacity in a wideband TR MIMO system. Comparatively, uncorrelated stochastic channels without coupling exhibit a wider range of random capacity distribution which greatly depends on the statistical channel. The conclusions drawn from information difference entropy theory provide a guideline for designing better high-performance wideband TR MIMO communication systems.

Hybrid Precoder and Combiner Design with Finite Resolution PSs for mmWave MIMO Systems

Hybrid precoding and combining have been considered as a promising technology, which can provide a compromise between hardware complexity and system performance in millimeter wave multiple-input multiple-output systems. However, most existing hybrid precoder and combiner designs generally assume that infinite resolution phase shifters(PSs) are used to produce the analog beamformers. In a practical scene, the design with accurate PSs can lead to high hardware cost and power consumption. In this paper, we investigate the hybrid precoder and combiner design with finite resolution PSs in millimeter wave systems. We employ alternate optimization as the main strategy to jointly design analog precoder and combiner. In addition, we propose a low complexity algorithm, where the analog beamformers are implemented only by finite resolution PSs to maximize spectral efficiency. Then, the digital precoder and combiner are designed based on the obtained analog beamformers to improve the spectral efficiency. Finally, simulation results and mathematical analysis show that the proposed algorithm with low-resolution PSs can achieve near-optimal performance and have low complexity.

Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.

Joint timing synchronization and frequency offset acquisition algorithm for MIMO OFDM systems

For multiple-input multiple-output （MIMO） orthogonal frequency division multiplexing （OFDM） systems, a joint timing synchronization and frequency offset acquisition algorithm based on fractional Fourier transform （FRFT） is proposed. The linear frequency modulation signals superimposed on the data signals are used as the training signals. By performing FRFT on the received signals and searching the peak value of the FRFT results, the receiver can realize timing synchronization and frequency offset acquisition simultaneously. Compared with the existing methods, the proposed algorithm can provide better timing synchronization performance and larger frequency offset acquisition range even under multi-path channels with low signal to noise ratio. Theoretical analysis and simulation results prove this point.

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.

A Low-Complexity Signal Detection Utilizing AOR Iterative Method for Massive MIMO Systems

Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error(MMSE) signal detection using the accelerated overrelaxation(AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.

A new layered space-time detection algorithm for frequency selective fading multiple-input multiple-output channels based on particle filter

In this paper, a new observation equation of non-Gaussian frequency selective fading Bell Labs layered space time （BLAST） architecture system is proposed, which is used for frequency selective fading channels and non-Gaussian noise in an application environment of BLAST system. With othogonal matrix triangularization （QR decomposition） of the channel matrix, the static observation equation of frequency selective fading BLAST system is transformed into a dynamic state space model, and then the particle filter is used for space-time layered detection. Making the full use of the finite alphabet of the digital modulation communication signal, the optimal proposal distribution can be chosen to produce particle and update the weight. Incorporated with current method of reducing error propagation, a new space-time layered detection algorithm is proposed. Simulation result shows the validity of the proposed algorithm.

Evaluation of Preamble Based Channel Estimation for MIMO-FBMC Systems

Filter-bank multicarrier （FBMC） with offset quadrature amplitude modulation （OQAM） is a candidate waveform for future wireless communications due to its advantages over orthogonal frequency division multiplexing （OFDM） systems. However, because of or-thogonality in real field and the presence of imaginary intrinsic interference, channel estimation in FBMC is not as straightforward as OFDM systems especially in multiple antenna scenarios. In this paper, we propose a channel estimation method which employs intrinsic interference cancellation at the transmitter side. The simulation results show that this method has less pilot overhead, less peak to average power ratio （PAPR）, better bit error rate （BER）, and better mean square error （MSE） performance compared to the well-known intrinsic approximation methods （IAM）.

Downlink MBER Transmit Beamforming Design Based on Uplink MBER Receive Beamforming for TDD-SDMA Induced MIMO Systems

The downlink minimum bit error rate (MBER) transmit beamforming is directly designed based on the uplink MBER receive beamforming solution for time division duplex (TDD) space-division multiple-access (SDMA) induced multiple-input multiple-output (MIMO) systems, where the base station (BS) is equipped with multiple antennas to support multiple single-antenna mobile terminals (MTs). It is shown that the dual relationship between multiuser detection and multiuser transmission can be extended to the rank-deficient system where the number of users supported is more than the number of transmit antennas available at the BS, if the MBER design is adopted. The proposed MBER transmit beamforming scheme is capable of achieving better performance over the standard minimum mean square error transmit beamforming solution with the support of low-complexity and high power-efficient MTs, particularly for rank-deficient TDD-SDMA MIMO systems. The robustness of the proposed MBER transmit beamforming design to the downlink and uplink noise or channel mismatch is investigated using simulation.

Throughput-based mode switching for MIMO ARQ systems in presence of transmit correlation

The mode switching between spatial multiplexing （SM） and space-time block code （STBC） diversity is investigated for the multiple-input multiple-output （MIMO） automatic repeat request （ARQ） system. Five important practical factors are considered in the proposed switching scheme： transmit correlation, ARQ technique, packet loss probability （PLP） constraint, discrete rate transmission （DRT） and channel coding. Under the spatially correlated channel, the distributions of the post signal-to-interference-plus- noise ratio （SiNR） for the SM mode and the STBC mode are obtained by using Gamma approximations. Then this paper derives the closed-form expressions of the PLP and the throughput for different modes when the ARQ technique is employed, based on which the mode switching algorithm is proposed to improve the spectral efficency. In the simulation, the correction of the expressions is first verified. Then, the significant gain observed by the proposed algorithm is presented. Since the switching point is the key parameter to implement the mode switching, this paper also shows how the switching point is affected by the practical factors considered.

Energy-efficient resource management for CCFD massive MIMO systems in 6G networks

This paper presents a co-time co-frequency fullduplex(CCFD)massive multiple-input multiple-output(MIMO)system to meet high spectrum efficiency requirements for beyond the fifth-generation(5G)and the forthcoming the sixth-generation(6G)networks.To achieve equilibrium of energy consumption,system resource utilization,and overall transmission capacity,an energy-efficient resource management strategy concerning power allocation and antenna selection is designed.A continuous quantum-inspired termite colony optimization(CQTCO)algorithm is proposed as a solution to the resource management considering the communication reliability while promoting energy conservation for the CCFD massive MIMO system.The effectiveness of CQTCO compared with other algorithms is evaluated through simulations.The results reveal that the proposed resource management scheme under CQTCO can obtain a superior performance in different communication scenarios,which can be considered as an eco-friendly solution for promoting reliable and efficient communication in future wireless networks.

Joint resource allocation scheme for target tracking in distributed MIMO radar systems

A joint resource allocation scheme concerned with the sensor subset,power and bandwidth for range-only target tracking in multiple-input multiple-output(MIMO)radar systems is proposed.By selecting an optimal subset of sensors with the predetermined size and implementing the power allocation and bandwidth strategies among them,this algorithm can help achieving a better performance within the same resource constraints.Firstly,the Bayesian Cramer-Rao bound(BCRB)is derived from it.Secondly,a criterion for minimizing the BCRB at the target location among all targets tracking in a certain range is derived.Thirdly,the optimization problem involved with three variable vectors is formulated,which can be simplified by deriving the relationship between the optimal power allocation vector and the bandwidth allocation vector.Then,the simplified optimization problem is solved by the cyclic minimization algorithm incorporated with the sequential parametric convex approximation(SPCA)algorithm.Finally,the validity of the proposed method is demonstrated with simulation results.

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.

Codebook selected beamforming algorithm for multiuser MIMO systems

For reducing the inter-user interference in multi-user multiple-input multiple-output（MU-MIMO） wireless communication systems,e.g.,MIMO-orthogonal frequency division multiplexing（MIMO-OFDM） systems,it is often desirable to the complex preprocessing at the transmitter.This paper proposes a multi-user beamforming algorithm with sub-codebook selection.Based on the minimal leakage criterion,the codebook selection,limited feed-forward and minimum mean square error（MMSE） detection are combined in the proposed algorithm.This avoids the complex channel matrix decomposition and inversion.Consequently,the computational complexity at the transmitter is significantly reduced.Simulation results show that the proposed algorithm performs better than existing beamforming algorithms.

Design of concatenated turbo-SPC coded space-time systems

Multiple antenna wireless systems can provide larger channel capacity and enable spatial diversity to combat fading. In this paper we conduct an investigation into the design of coded space-time system obtained by serially concatenating channel code module and space-time code module with an interleaver in between. As an example, the system is constructed by employing low decoding complexity turbo-SPC （single parity check） code as outer module and linear complex field space-time code as inner module, which achieves full diversity and lossless equivalent channel capacity. Simulation results prove that our designed system performs well and it only loses 0.8 dB from multiple-input multiple-output （MIMO） capacity at BER = 10^-5 in the case of information bit length 6048. Compared with turbo code-based systems, it also has lower error floor.

Component fault diagnosis for nonlinear systems

In the field of fault diagnosis, the state equation of nonlinear system, including the actuator and the component, has been established. When the faults in the system appear, it is difficult to observe the fault isolation between the actuator and the component. In order to diagnose the component fault in the nonlinear systems, a novel strategy is proposed. The nonlinear state equation with only the component system is built on mathematical equations. The nonlinearity of the component equation is expanded and estimated with Taylor series. If the actuator is perfect, the anomaly of the state equations reflects the component fault. The fault feature index is defined to detect the component fault and the initial fault. The numerical examples of the component faults are simulated for multiple-input multiple-output(MIMO)nonlinear systems. The results show that the component faults,as well as the incipient faults, can be detected. Furthermore, the effectiveness of the proposed strategy is verified. This method can also provide a foundation for the component fault reconfiguration control.

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.