Performance of multi-user transmitter preprocessing assisted MIMO system over correlated frequency-selective channels

In this contribution we present the performance of a multi-user transmitter preprocessing (MUTP) assisted multiple-input multiple-output (MIMO) space division multiple access (SDMA) system, aided by double space time transmit diversity (DSTTD) and space time block code (STBC) processing for downlink (DL) and uplink (UL) transmissions respectively. The MUTP is invoked by singular value decomposition (SVD) which exploits the channel state information (CSI) of all the users at the base station (BS) and only an individual user’s CSI at the mobile station (MS). Specifically, in this contribution, we investigate the performance of multi-user MIMO cellular systems in frequency-selective channels from a transmitter signal processing perspective, where multiple access interference (MAI) is the dominant channel impairment. In particular, the effects of three types of delay spread distributions on MUTP assisted MIMO SDMA systems pertaining to the Long Term Evolution (LTE) channel model are analyzed. The simulation results demonstrate that MUTP can perfectly eliminate MAI in addition to obviating the need for complex multi-user detectors (MUDs) both at the BS and MS. Further, SVD-based MUTP results in better achievable symbol error rate (SER) compared to popularly known precoding schemes such as block diagonalization (BD), dirty paper coding (DPC), Tomlinson–Harashima precoding (THP) and geometric mean decomposition (GMD). Furthermore, when turbo coding is invoked, coded SVD aided MUTP results in better achievable SER than an uncoded system.     

The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.     

Design of pre-processing algorithms for efficient MIMO–OFDM receiver architectures

Modern wireless communication applications are characterized by the need for advanced signal processing techniques such as Multiple-Input Multiple-Output (MIMO) technology for achieving high throughput and diversity and Orthogonal Frequency Division Multiplexing (OFDM) for achieving robustness to multipath fading. The implementation of such techniques at the transceiver level typically involves the design of algorithms with high processing complexity.This paper considers the efficient design of MIMO–OFDM receivers in preamble-based systems and addresses the problem of large processing delays associated with pre-computations and symbol detection. The existence of large processing delays has a huge impact on the performance and resource requirements (vector processing, increased clock rates and increased power consumption) of modern receivers. More specifically, we address the performance and complexity bottleneck introduced by the pre-computations involved for MIMO–OFDM channel decomposition. We propose a redesign of channel decomposition algorithms which achieves a better matching of the processing rate of MIMO–OFDM receivers to the real-time processing deadlines imposed by the structure of the incoming data packets. It is demonstrated that for a specific MIMO-OFDM channel training frame structure (alternating antenna preamble), simple modifications to typical channel decomposition algorithms can achieve significant processing performance and complexity gains compared to typical receiver designs.     

A Generalized Spatial Modulation System Using Massive MIMO Space Time Coding Antenna Grouping

Massive multiple input multiple output (MIMO), also known as a very large-scale MIMO, is an emerging technology in wireless communications that increases capacity compared to MIMO systems. The massive MIMO communication technique is currently forming a major part of ongoing research. The main issue for massive MIMO improvements depends on the number of transmitting antennas to increase the data rate and minimize bit error rate (BER). To enhance the data rate and BER, new coding and modulation techniques are required. In this paper, a generalized spatial modulation (GSM) with antenna grouping space time coding technique (STC) is proposed. The proposed GSM-STC technique is based on space time coding of two successive GSM-modulated data symbols on two subgroups of antennas to improve data rate and to minimize BER. Moreover, the proposed GSM-STC system can offer spatial diversity gains and can also increase the reliability of the wireless channel by providing replicas of the received signal. The simulation results show that GSM-STC achieves better performance compared to conventional GSM techniques in terms of data rate and BER, leading to good potential for massive MIMO by using subgroups of antennas.     

Near optimal performance joint semi-blind channel estimation and data detection techniques for Alamouti coded single-carrier (SC) MIMO communication systems

In this paper, two novel joint semi-blind channel estimation and data detection techniques are proposed and investigated for Alamouti coded single-carrier (SC) multiple-input multiple-output (MIMO) communication system using Rayleigh flat fading channel model. In the first novel semi-blind technique, blind channel estimation can be performed by using singular value decomposition (SVD) of received output autocorrelation matrix and training based channel estimation for orthogonal training symbols can be performed by using orthogonal pilot maximum likelihood (OPML) algorithm. Further using, that semi-blind channel estimate and received output, data detection is performed by using Maximum likelihood (ML) detection. Finally we derived new training symbols from error covariance matrix of estimated data and known orthogonal training symbols, which further applied to OPML algorithm for final channel estimate. In the second novel semi-blind technique, blind channel estimation can be performed by using matrix triangularization based on householder QR decomposition (H-QRD) of received output autocorrelation matrix instead of SVD decomposition. Other steps are same as the first novel technique to calculate data detection and final channel estimation. Simulation results are presented under 2-PSK, 4-PSK, 8-PSK and 16-QAM data modulation schemes using 2 transmitters and different combinations of receiver antennas to investigate the performances of novel techniques compare to conventional whitening rotation (WR) and rotation optimization maximum likelihood (ROML) based semi-blind channel estimation techniques. Result demonstrates that novel techniques outperform others by achieving near optimal performance.     

Two-level-enforced security with hybrid precoding for multicast massive MIMO wiretap systems

Multicast hybrid precoding reaches a compromise among hardware complexity, transmission performance and wireless resource efficiency in massive MIMO systems. However, system security is extremely challenging with the appearance of eavesdroppers. Physical layer security (PLS) is a relatively effective approach to improve transmission and security performance for multicast massive MIMO wiretap systems. In this paper, we consider a transmitter with massive antennas transmits the secret signal to many legitimate users with multiple-antenna, while eavesdroppers attempt to eavesdrop the information. A fractional problem aims at maximizing sum secrecy rate is proposed to optimize secure hybrid precoding in multicast massive MIMO wiretap system. Because the proposed optimized model is an intractable non-convex problem, we equivalently transform the original problem into two suboptimal problems to separately optimize the secure analog precoding and secure digital precoding. Secure analog precoding is achieved by applying singular value decomposition (SVD) of secure channel. Then, employing semidefinite program (SDP), secure digital precoding with fixed secure analog precoding is obtained to ensure quality of service (QoS) of legitimate users and limit QoS of eavesdroppers. Complexity of the proposed SVD-SDP algorithm related to the number of transmitting antennas squared is lower compared with that of constant modulus precoding algorithm (CMPA) which is in connection with that number cubed. Simulation results illustrate that SVD-SDP algorithm brings higher sum secrecy rate than those of CMPA and SVD-SVD algorithm.     

Downlink channel estimation in FDD cell-free massive MIMO

In frequency-division duplexing (FDD) cell-free massive multiple-input multiple-output (MIMO) systems, an excessive channel estimation overhead is a critical issue that limits the system performance. In this paper, by exploiting the sparse channel characteristics of such a cell-free system, we apply compressive sensing to estimate the channel state information and solve the excessive pilot overhead problem. The proposed algorithm estimates several channel coefficients with significant gains in the power domain and ignores the approximately zero coefficients. Compared to minimum mean square error (MMSE) estimation with orthogonal pilots, the proposed method significantly reduces the pilot overhead in an FDD cell-free massive MIMO system. The access points (APs) that contribute low gains feature reduced energy consumption because the power coefficients corresponding to zero gains in the sparse channel are assigned zeros in the power control process. Therefore, to improve the energy efficiency, the ignored channel coefficients reduce the power overhead.     

On superimposed pilot for channel estimation in massive MIMO uplink

In this paper, we consider the uplink (UL) of multiuser multi-cell massive MIMO systems, and present a transmission-efficient channel estimation technique by using time-superimposed (TS) pilots, where pilots are superimposed onto data symbols in time domain. In large-antenna regime, we mathematically characterize the UL achievable rate of massive MIMO as a closed-form expression. Concerning the asymptotic case, we show that the UL achievable rate is a monotonically increasing function of pilot power, and also depends on the time allocation between pilot and data. Theoretical analysis and simulation results demonstrate the superiority of the proposed design in comparison with both the conventional TS and time-multiplexed pilots.     

Low-complexity soft-output signal detector based on AI-SSOR preconditioned conjugate gradient method over massive MIMO correlated channel

Massive multiple-input multiple-output (MIMO) techniques with a large number of antenna elements at base station (BS) have been proved as an alternative to provide potential opportunity to increase the spectrum and energy efficiency. However, in the system, there generally exists a spatial correlation effect due to insufficient antenna elements spacing and/or the lack of rich scattering at BS. The minimum mean square error (MMSE) method performs signal detection at the expense of large-scale matrix inversion operation. Thus, the conjugate gradient (CG) method has received a lot of attentions to realize the MMSE detection efficiently. Unfortunately, this efficiency can be compromised due to the ill-conditioned equalization matrix of MMSE method over the correlated channel environments. Moreover, the hard output signal detection exhibits a sharply degradation in performance for higher-order quadrature amplitude modulation (QAM). Therefore, the modern communication systems use the soft-output information, i.e., log-likelihood ratio (LLR) along with the forward error-correcting code (FEC) to achieve satisfactory performance. The LLR computation along with a higher-order QAM remains challenging due to the exhaustive search of symbol in the modulation constellation. In this paper, a low-complexity soft-output signal detector based on approximate inverse symmetric successive over-relaxation preconditioned conjugate gradient (AI-SSOR-CG-SOD) method is proposed to realize MMSE method detection for uplink multiuser massive MIMO correlated channel. In the proposed method, a new preconditioner, an AI-SSOR, which is based on the Neumann series approximation of the inverse of the conventional SSOR preconditioner is firstly developed to handle ill-conditioned matrix, and then incorporated with CG method to improve the convergence rate and performance. According to the characteristic of the Gray-coding that adjacent symbols in the constellation set have only one different bit, the constellation set is divided multiple times based on the bits of the inphase and the quadrature components of the symbol, which reduces the complexity of the LLR computation of the transmitted bits by avoiding the exhaustive search process. Simulation results show that the AI-SSOR preconditioner is robust against spatial correlation effect, and the proposed detector converges at 3 iterations. Simulation results also show that the proposed detector achieves a better trade-off between the complexity and the performance compared to other existing detectors.     

Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

In massive multiple-input multiple-output (MIMO), it is much challenging to obtain accurate channel state information (CSI) after radio frequency (RF) chain reduction due to the high dimensions. With the fast development of machine learning(ML), it is widely acknowledged that ML is an effective method to deal with channel models which are typically unknown and hard to approximate. In this paper, we use the low complexity vector approximate messaging passing (VAMP) algorithm for channel estimation, combined with a deep learning framework for soft threshold shrinkage function training. Furthermore, in order to improve the estimation accuracy of the algorithm for massive MIMO channels, an optimized threshold function is proposed. This function is based on Gaussian mixture (GM) distribution modeling, and the expectation maximum Algorithm (EM Algorithm) is used to recover the channel information in beamspace. This contraction function and deep neural network are improved on the vector approximate messaging algorithm to form a high-precision channel estimation algorithm. Simulation results validate the effectiveness of the proposed network.     

Performance of passive decoy-state quantum key distribution with mismatched local detectors

In quantum key distribution(QKD),the passive decoy-state method can simplify the intensity modulation and reduce some of side-channel information leakage and modulation errors.It is usually implemented with a heralded single-photon source.In Wang et al 2016(Phys.Rev.A 96032312),a novel passive decoy-state method is proposed by Wang et al,which uses two local detectors to generate more detection events for tightly estimating channel parameters.However,in the original scheme,the two local detectors are assumed to be identical,including the same detection efficiency and dark count rate,which is often not satisfied in the realistic experiment.In this paper,we construct a model for this passive decoy-state QKD scheme with two mismatched detectors and explore the effect on QKD performance with certain parameters.We also take the finite-size effect into consideration,showing the performance with statistical fluctuations.The results show that the efficiencies of local detectors affect the key rate more obviously than dark count rates.     

Outage capacity analysis of the massive MIMO diversity channel

We consider the massive Multiple Input Multiple Output (MIMO) channel affected by independent and identically distributed Rayleigh fading, with linear processing at both transmitter and receiver sides to pursue full diversity, and analyze its outage capacity for large number of antennas. We first discuss the classical Single Input Multiple Output (SIMO) diversity channel that encompasses Maximal Ratio Combining (MRC) or Selection Combining (SC). For MRC, a numerical computation and a Gaussian Approximation (GA) are considered, whereas for SC an exact evaluation is presented. The analysis is then straightforwardly extended to the Multiple Input Single Output (MISO) diversity channel that encompasses Maximal Ratio Transmission (MRT) or transmit antenna selection. The general full diversity MIMO channel is finally considered, with optimal linear processing or simple antenna selection at both transmitter and receiver. If the number of antennas is sufficiently large on at least one side, the outage capacity of each considered diversity channel approaches that of a reference Additive White Gaussian Noise (AWGN) channel with properly defined Signal-to-Noise Ratio (SNR), which provides a performance benchmark. This conclusion is valid for large but realistic number of antennas compatible with the assumption of independent fading.     

A novel blind spatial-based random access to pilots (BSB-RAP) in overloaded massive MIMO systems

In this study, to increase the success rate of active user admission in overloaded massive multi-input multi-output (MIMO) systems, a new spatially based random access to pilots (RAP) is proposed to assign orthogonal pilots to the users requesting network access. Therefore, by increasing the acceptance rate of users in a cell, this approach reduces the training overhead and waste of resources. In the massive MIMO for crowd scenarios, the main issue is the limited number of available orthogonal pilots employed by the users in the channel estimation process. This novel approach as spatially based random access enables us to have more connected users during every coherence interval (CI) despite the mentioned limitation. Intrinsic angular domain sparsity of massive MIMO channels and the sporadic traffic of users can help us obtain the spatial features of active UEs in a blind continuous compressed sensing (CCS) approach. Proposed approach is to use a continuous compressed sensing technique based on a prior optimization that provides users’ angle of arrival (AoA) and an innovative space-based RAP protocol to assign orthogonal pilots to active users in coherent transmission. Unlike the previous works, this strategy does not need to limit the number of users to the number of available orthogonal pilots due to the employed spatial degrees of freedom.     

Upper Bounds for the Capacity for Severely Fading MIMO Channels under a Scale Mixture Assumption

A cornerstone in the modeling of wireless communication is MIMO systems, where a complex matrix variate normal assumption is often made for the underlying distribution of the propagation matrix. A popular measure of information, namely capacity, is often investigated for the performance of MIMO designs. This paper derives upper bounds for this measure of information for the case of two transmitting antennae and an arbitrary number of receiving antennae when the propagation matrix is assumed to follow a scale mixture of complex matrix variate normal distribution. Furthermore, noncentrality is assumed to account for LOS scenarios within the MIMO environment. The insight of this paper illustrates the theoretical form of capacity under these key assumptions and paves the way for considerations of alternative distributional choices for the channel propagation matrix in potential cases of severe fading, when the assumption of normality may not be realistic.     

Performance analysis of massive MIMO systems with low-resolution ADCs and IQI

In massive multiple-input multiple-output (MIMO) systems, the substantial increase in the number of antennas leads to a surge in hardware cost and power consumption. Meanwhile, the impact of IQ imbalance (IQI) on system performance also tends to be serious. In this paper, closed-form expressions for the achievable rates of maximum-ratio combining (MRC) receivers are derived for uplink massive MIMO systems with both low-resolution analog-to-digital converters (ADCs) and IQI. Based on the derived closed-form expression, the influence of system parameters on the achievable rate is analyzed. The simulation results verify the accuracy of the theoretical results. It is found that low-resolution ADC and IQI will degrade the achievable rate compared with employing high resolution ADCs, but this loss can be compensated for by increasing the number of base station (BS) antennas, so as to significantly increase the energy efficiency.     

Characterization,modeling and simulation of the MIMO propagation channel

This article deals with several aspects relative to the MIMO propagation channel. Based on simulations and/or measurements, different approaches are used to model the propagation channel. These models are useful for the MIMO system design. Several studies are performed in order to realize realistic simulation of MIMO channel. Different measurement techniques are used in characterizing the propagation channel in various environments. Measurement campaigns made in different situations have been analyzed to obtain the relevant statistical parameters of the channel. Simulation of MIMO channel is then presented. Measurement and simulation results provide an evaluation of the capacity of MIMO channel. Obtained results show feasibility in the integration of MIMO techniques in practical wireless communication systems.     

Time reversal multiple-input/multiple-output acoustic communication enhanced by parallel interference cancellation

Multiple-input/multiple-output (MIMO) techniques can lead to significant improvements of underwater acoustic communication capabilities. In this paper, receivers based on time reversal processing are developed for high frequency underwater MIMO channels. Time reversal followed by a single channel decision feedback equalizer, aided by frequent channel updates, is used to compensate for the time-varying inter-symbol interference. A parallel interference cancellation method is incorporated to suppress the co-channel interference in the MIMO system. The receiver performance is demonstrated by a 2008 shallow water experiment in Kauai, Hawaii. In the experiment, high frequency MIMO signals centered at 16 kHz were transmitted every hour during a 35 h period from an 8-element source array to a wide aperture 16-element vertical receiving array at 4 km range. The interference cancellation method is shown to generate significant performance enhancement, on average 2-4 dB in the output signal-to-noise ratio per data stream, throughout the 35 h MIMO transmissions. Further, communication performance and achieved data rates exhibit significant changes over the 35 h period as a result of stratification of the water column.     

Exploiting joint sparsity for underwater acoustic MIMO communications

MIMO communication has been recognized as a potential solution for high speed underwater acoustic communication, which unfortunately encounters significant difficulties posed by simultaneous presence of multipath and Co-channel interference (CoI). Sparsity contained in the multipath structure of underwater acoustic channels offers an effective way for improving channel estimation quality and thus enhancing the communication performance in the form of time reversal or channel estimation based equalization. However, for MIMO channels with extensive multipath and CoI, the performance gain achieved by classic sparsity exploitation channel estimation methods such as orthogonal matching pursuit (OMP) is still not enough to yield satisfactory performance. Under quasi-stationary assumption, underwater acoustic channels of adjacent data blocks exhibit correlated multipath structure, namely, multipath arrivals with similar time delay but different magnitude, which has not been exploited. In this paper, a joint sparse recovery approach is proposed to exploit the sparse correlation among adjacent data blocks to improve the performance of channel estimation. Under the framework of distributed compressed sensing (DCS), a joint sparse model which treats the multipath arrivals as sparse solutions with common time support is adopted to derive a joint sparse recovery algorithm for efficient channel estimation, the results of which are used to initialize and periodly update a channel estimation based time reversal receiver. Finally, underwater MIMO communication experimental results obtained in a shallow water channel are provided to demonstrate the effectiveness of the proposed method, compared to the same type of receiver that do not exploit the joint sparse.     

Rate analysis of ZF receiver for uplink cell-free massive MIMO systems with D2D communications

This paper proposes a transmission structure of zero forcing (ZF) receiver for uplink cell-free massive multiple-input multiple-output (MIMO) systems with device-to-device (D2D) communications, followed by a rate analysis. We assumed that D2D users (DUEs) can utilize orthogonal radio resources to improve the efficiency of the scarce utilization or repurpose the time–frequency-spectrum resources currently used by the cell-free users (CFUEs). Assuming that the imperfect channel state information (CSI) is realizable, after that, the use-and-forget bounding technique is then used to respectively obtain the closed-form expressions of the CFUEs and DUEs, which provide the lower bounds on the ergodic approximate realizable rate of both communication links. First, we calculate the minimum-mean-square error (MMSE) estimation for all channels. Then, the derived results of the achievable uplink sum rate provide us with a tool that enables us to explain how some important parameters, such as the number of access points (APs)/CFUEs, each AP/CFUE/antenna, and the density of DUEs, affect system performance, highlighting the significance of cooperation between cell-free massive MIMO and D2D communication.     

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.