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.

Low-complexity signal detection for massive MIMO systems via trace iterative method

Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

Multiuser detection algorithm based on belief propagation in multiple-input multiple-output systems

In MIMO (multiple-input, multiple-output) systems, signals from differenttransmitting antennas interfere at each receiving antenna and multiuser detection (MUD)algorithms may be adopted to improve the system performance. This paper proposes anovel multiuser detection algorithm in MIMO systems based on the idea of 'beliefpropagation' which has achieved great accomplishment in decoding of low-densityparity-check codes. The proposed algorithm has a low computation complexityproportional to the square of transmitting/receiving antenna number. Simulation resultsshow that under low signal-to-noise ratio (SNR) circumstances, the proposed algorithmoutperforms the traditional linear minimum mean square error (MMSE) detector while itencounters a 'floor' of bit error rate under high SNR circumstances. So the proposedalgorithm is applicable to MIMO systems with channel coding and decoding. Although inthis paper the proposed algorithm is derived in MIMO systems, obviously it can be appliedto ordinary code-division multiple access (CDMA) systems.

SEMI-BLIND CHANNEL ESTIMATION OF MULTIPLE-INPUT/MULTIPLE-OUTPUT SYSTEMS BASED ON MARKOV CHAIN MONTE CARLO METHODS

This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and the transmitted signals. The deduced algorithms can work well under circumstances of low Signal-to-Noise Ratio (SNR). Simulation results are presented to demonstrate their effectiveness.

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.

Low complexity iterative differential decoding algorithm for multiband UWB communication systems

Due to not requiring channel state information （CSI） at both the transmitter and the receiver, noncoherent ultra-wideband （UWB） incurs a performance penalty of approximately 3 dB in the required signal to noise ratio （SNR） compared to the coherent case. To overcome the gap, an effective differential encoding and decoding scheme for multiband UWB systems is proposed. The proposed scheme employs the parallel concatenation of two recursive differential unitary space-frequency encoders at the transmitter. At the receiver, two component decoders iteratively decode information bits by interchanging soft metric values between each other. To reduce the computation complexity, a decoding algorithm which only uses transition probability to calculate the log likelihood ratios （LLRs） for the decoded bits is given. Simulation results show that the proposed scheme can dramatically outperform the conventional differential and even coherent detection at high SNR with a few iterations.

Chip-level space-time equalization receiver scheme for MIMO HSDPA systems

A chip-level space-time equalization receiver scheme is proposed for multiple-input multiple-output high-speed downlink packet access (MIMO HSDPA) systems to jointly combat the co-channel interference and the inter-code interference. A fractional sample equalizer is also derived to further improve the performance of the receiver. Performance analysis and the calculation of the output signal to interference ratio (SINR) at each receiver antenna are presented to help direct the design of equalization weight in a more optimal manner. System simulations demonstrate the significant performance gain over conventional Rake receiver and high potential of MIMO HSDPA for high-data-rate packet transmission.

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.

Selective transmission and channel estimation in massive MIMO systems

Massive MIMO systems have got extraordinary spectral efficiency using a large number of base station antennas,but it is in the challenge of pilot contamination using the aligned pilots.To address this issue,a selective transmission is proposed using time-shifted pilots with cell grouping,where the strong interfering users in downlink transmission cells are temporally stopped during the pilots transmission in uplink cells.Based on the spatial characteristics of physical channel models,the strong interfering users are selected to minimize the inter-cell interference and the cell grouping is designed to have less temporally stopped users within a smaller area.Furthermore,a Kalman estimator is proposed to reduce the unexpected effect of residual interferences in channel estimation,which exploits both the spatial-time correlation of channels and the share of the interference information.The numerical results show that our scheme significantly improves the channel estimation accuracy and the data rates.

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.

An Improved Semi-Orthogonal User Selection Algorithm Based on Condition Number for Multiuser MIMO Systems

In Multiple-Input Multiple-Out （MIMO） systems, the user selection algorithm plays an important role in the realization of multiplexing gain. In this paper, an improved Semi-orthogonal User Selection algorithm based on condition number is proposed. Besides, a new MIMO pre- coding scheme is designed. The proposed SUS- CN （SUS with condition number） algorithm outperforms the SUS algorithm for the selection of users with better matrix inversion property, thus a higher information rate for selected user pair is achieved. The designed MIMO precoding matrix brings benefits of the power equality at transmitted terminals, the limited dynamic range of the power over time, and a better power efficiency. The simulation results give the key insights into the im- pact of the different condition number value and users on the sum-rate capacity.

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.

Generalized 3D Scattering Channel Model with MIMO Antenna Systems

In this paper, a generalized three-dimensional(3D) scattering channel model for macrocellular land mobile environments is considered. This model simultaneously describes angular arrival of multi-path signals in the azimuth and elevation planes in an environment where uniformly distributed scatterers are assumed to be present in hemispheroids around the base station(BS) and mobile station(MS). Using this channel model, we first derive the closed-form expression for the joint and marginal probability density functions of the angle-of-arrival and time-of-arrival measured at the BS and the MS corresponding to the azimuth and elevation angles. Next, we derive an expression for the Doppler spectral distribution caused by motion of the MSs. Furthermore, we analyze the performance of multiple-input multiple-output antenna systems numerically. The results show that the proposed 3D scattering channel model performs better than previously proposed two-dimensional(2D) models for indoor and outdoor environments. We compare the results with previous scattering channel models and measurement results to validate the generalizability of our model.

ESTIMATION OF CARRIER FREQUENCY OFFSETS FOR MIMO SYSTEMS WITH DISTRIBUTED TRANSMIT ANTENNAS

The problem of estimating the carrier frequency offsets in Multiple-Input Multiple-Output (MIMO) systems with distributed transmit antennas is addressed. It is supposed that the transmit antennas are distributed while the receive antennas are still centralized, and the general case where both the time delays and the frequency offsets are possibly different for each transmit antenna is considered. The channel is supposed to be frequency flat, and the macroscopic fading is also taken into consideration. A carrier frequency offset estimator based on Maximum Likelihood (ML) is proposed, which can separately estimate the frequency offset for each transmit antenna and exploit the spatial diversity. The Cramer-Rao Bound (CRB) for synchronous MIMO (i.e., the time delays for each transmit antenna are all equal) is also derived. Simulation results are given to illustrate the per- formance of the estimator and compare it with the CRB. It is shown that the estimator can provide satisfactory frequency offset estimates and its performance is close to the CRB for the Signal-to-Noise Ratio (SNR) below 20dB.

Robust Blind Separation for MIMO Systems against Channel Mismatch Using Second-Order Cone Programming

To improve the deteriorated capacity gain and source recovery performance due to channel mismatch problem,this paper reports a research about blind separation method against channel mismatch in multiple-input multiple-output(MIMO) systems.The channel mismatch problem can be described as a channel with bounded fluctuant errors due to channel distortion or channel estimation errors.The problem of blind signal separation/extraction with channel mismatch is formulated as a cost function of blind source separation(BSS) subject to the second-order cone constraint,which can be called as second-order cone programing optimization problem.Then the resulting cost function is solved by approximate negentropy maximization using quasi-Newton iterative methods for blind separation/extraction source signals.Theoretical analysis demonstrates that the proposed algorithm has low computational complexity and improved performance advantages.Simulation results verify that the capacity gain and bit error rate(BER) performance of the proposed blind separation method is superior to those of the existing methods in MIMO systems with channel mismatch problem.