ICD reciprocity calibration for distributed large-scale MIMO systems with BD precoding

A subspace decomposition based algorithm for joint frequency of arrival (FOA) and direction of arrival (DOA) estimation is presented in this paper. The proposed method termed as QR-TLS ESPRIT, estimates the signal subspace by decomposing the received signal in a specially formed Toeplitz matrix. The structure of the matrix favors the application of QR factorization, which yields the signal subspace at the expense of less number of computations. A Total Least Squares (TLS) method is applied to obtain the frequency and angle estimates from the signal space. The proposed QR-TLS ESPRIT method provides several benefits: 1) It avoids the computations of applying computationally complex Eigen Value Decomposition (EVD) or Singular Value Decomposition (SVD) methods to obtain signal subspace alike conventional methods, 2) QR decomposition method, which is intrinsically applicable to numerical rank deficient problems, is used to extract signal subspace from the observation data matrix, 3) the TLS method is integrated with the QR which enhances the estimation accuracy in the presence of noisy measurements, 4) the proposed method can estimate the FOA and DOA from a small snapshot length of data which further reduces the computational time, and 5) the method works for both coherent as well as noncoherent sources without employing pre-whitening or spatial smoothing techniques. The performance of the proposed method is evaluated through simulations and the results are illustrated with respect to various received signal strength.

Reconfigurable intelligent surface (RIS) is thought to be a potential key technique for future wireless communications due to its ability for manipulating the electromagnetic environment smartly. This paper focuses on the rank and capacity analysis when a RIS is introduced into a multiple-input multiple-output (MIMO) system. By establishing a system model for this communication system, various simulations are conducted for identifying the characteristics of the channel. The simulations of different distance between the access point (AP) and user equipment (UE) show that the condition number of the channel is worsen when the distance increases and the role of the RIS in rank improvement is weaken. The spatial distributions of the reciprocal condition number of the channel which corresponds to the RIS locations are obtained and rank-deficient zones are found in different AP, UE and RIS configurations, which depicts the spatial characteristics of the RIS-assisted MIMO channel. The simulations also indicate that the condition number of the channel not only varies with the RIS location, but is also affected by the antenna array size and orientation of AP and UE. In addition, when the AP has a larger amount of antennas than UE, it is advantageous to place the RIS near the UE rather than the AP to achieve better channel condition. Modulation and coding schemes are applied in the simulations for comparison and capacity improvement is witnessed. Beneficial suggestions for RIS deployment in the MIMO system are concluded according to the simulation results.

There are several methods used for AC-impedance estimation in Electrical Bioimpedance measurements. In this paper we propose a novel method for digital estimation of electrical impedance based in the Total Least Square (TLS) technique and we carry out a performance comparison between the proposed method and the typical Digital Sine Correlation (DSC) method. The TLS method has been implemented using the Singular Value Decomposition approach and the performance of both methods have been compared in terms of robustness against noise and size of the data set. The results indicate that the TLS method shows a better performance for impedance estimation than the DSC with reduced set of measurement samples and high SNR levels, while its performance worsen for increasing number of samples and decreasing SNR. The DSC method exhibits a better robustness against noise, especially for increasing the number of data samples. The main observed advantage of the TLS method is that it suffers from less mathematical constrains than the DSC method, and since the noise levels in bioimpedance applications are not expected to be very high we conclude that the TLS method is a good choice as impedance estimator.

Time division duplexing (TDD) is adopted to exploit the uplink and downlink channel reciprocity in most of studies on massive multiple-input multiple-output (MIMO) systems. However, even in TDD, a base station (BS) still requires to transmit downlink training signals, which are named in the 3GPP standard as channel state information reference signals (CSI-RSs), to fully support spatial multiplexing in practice. This is because user equipments (UEs) may deploy less number of transmit antennas than receive antennas due to practical issues. Since uplink sounding reference signals (SRSs) are transmitted from only the transmit antennas of the UE, the BS is not able to obtain full downlink MIMO CSI by using channel reciprocity for spatial multiplexing. Hence, after reception of the downlink CSI-RSs, the UE still needs to feed back quantized CSI using a codebook to support spatial multiplexing. Taking practical antenna structures into account for reducing downlink CSI-RS overhead, this paper proposes possible approaches for downlink MIMO CSI reconstruction at the BS using the SRS with quantized downlink CSI to support spatial multiplexing. Numerical results show that the proposed techniques outperform the conventional one, i.e., solely based on the quantized CSI, in terms of the spectral efficiencies of spatial multiplexing.

Large‐scale multiple‐input multiple‐output (MIMO) system is considered one of promising technologies to realise next‐generation wireless communication system (5G). So far, channel estimation problem is a big obstacle to develop large‐scale MIMO system design due to high computational complexity and curse of dimensionality, which are caused by the long delay spread as well as a large number of antennas. Hence, devising any low‐complexity channel estimation method could promote the successful development of the large‐scale MIMO system. Due to the fact that, large‐scale MIMO channels often exhibit sparse or/and cluster‐sparse structure, in this study, the authors propose an effective low‐complexity large‐scale MIMO channel estimation method by using affine combination of sparse adaptive filtering filters. First, problem formulation and standard affine combination of adaptive least mean square (LMS) filters are introduced. Then they propose an effective affine combination method with two sparse LMS filters and design an approximate optimum affine combiner according to stochastic gradient search method. Later, to verify the proposed algorithm for large‐scale MIMO channel estimation, both theoretical analysis and numerical simulations are provided to confirm effectiveness of the proposed algorithm which can achieve better estimation performance than the traditional methods.

Large-scale multiple-input multiple-output (MIMO) communication systems can bring substantial improvement in spectral efficiency and/or energy efficiency, due to the excessive degrees-of-freedom and huge array gain. However, large-scale MIMO is expected to deploy lower-cost radio frequency (RF) components, which are particularly prone to hardware impairments. Unfortunately, compensation schemes are not able to remove the impact of hardware impairments completely, such that a certain amount of residual impairments always exists. In this paper, we investigate the impact of residual transmit RF impairments (RTRI) on the spectral and energy efficiency of training-based point-to-point large-scale MIMO systems, and seek to determine the optimal training length and number of antennas which maximize the energy efficiency. We derive deterministic equivalents of the signal-to-noise-and-interference ratio (SINR) with zero-forcing (ZF) receivers, as well as the corresponding spectral and energy efficiency, which are shown to be accurate even for small number of antennas. Through an iterative sequential optimization, we find that the optimal training length of systems with RTRI can be smaller compared to ideal hardware systems in the moderate SNR regime, while larger in the high SNR regime. Moreover, it is observed that RTRI can significantly decrease the optimal number of transmit and receive antennas.

The scaling up of antennae and terminals in large-scale multiple-input multiple-output (MIMO) systems helps increase the spectral efficiency at the penalty of prohibitive computational complexity. In conventional linear detection such as the minimum mean square error (MMSE) signal detection, the high complexity is mainly caused by solving the inversion of random matrix, especially in large-scale MIMO systems. In order to reduce the complexity of the matrix inversion, we proposed a low-complexity MMSE detection scheme based on symmetric successive over relaxation (SSOR) method, referred to as MSSD method. The proposed method exploits the channel hardening phenomenon, which means the off-diagonal terms of the HHH matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix H increases. In addition, we point out that the channel estimation scheme used in channel hardening-exploiting message passing receiver (CHEMP) is very suitable for the MSSD algorithm. For the considered large MIMO settings, simulation results show that the performance of the MSSD algorithm is very close to the classical MMSE detection algorithm with a small number of iterations.

The doubly selective (DS) channel estimation in the large-scale multiple-input multiple-output (MIMO) systems is a challenging problem due to the large number of the channel coefficients to be estimated, which requires unaffordable and prohibitive pilot overhead. In this paper, first we conduct the analysis about the common sparsity of the basis expansion model (BEM) coefficients among all the BEM orders and all the transmit–receive antenna pairs. Then, a novel pilot pattern is proposed, which inserts the guard pilots to deal with the intercarrier interference under the superimposed pilot pattern. Moreover, by exploiting the common sparsity of the BEM coefficients among different BEM orders and different antennas, we propose a block distributed compressive sensing-based DS channel estimator for the large-scale MIMO systems. Its structured sparsity leads to the reduction of the pilot overhead under the premise of guaranteeing the accuracy of the estimation. Furthermore, taking consideration of the block structure, a pilot design algorithm referred to as block discrete stochastic optimization is proposed. It optimizes the pilot positions by reducing the coherence among different blocks of the measurement matrix. Besides, a linear smoothing method is extended to large-scale MIMO systems to improve the accuracy of the estimation. Simulation results verify the performance gains of our proposed estimator and the pilot design algorithm compared with the existing schemes.

In this letter, we consider a large-scale multiple-input multiple-output (MIMO) system where the receiver should harvest energy from the transmitter by wireless power transfer to support its wireless information transmission. The energy beamforming in the large-scale MIMO system is utilized to address the challenging problem of long-distance wireless power transfer. Furthermore, considering the limitation of the power in such a system, this letter focuses on the maximization of the energy efficiency of information transmission (bit per Joule) while satisfying the quality-of-service (QoS) requirement, i.e. delay constraint, by jointly optimizing transfer duration and transmit power. By solving the optimization problem, we derive an energy-efficient resource allocation scheme. Numerical results validate the effectiveness of the proposed scheme.

In large-scale multiple-input multiple-output (MIMO) systems, high cost and high power consumption of RF chains typically prohibit the use of traditional baseband beamforming which requires one distinct radio-frequency (RF) chain per antenna. One possible architecture to reduce the number of RF chains is hybrid beamforming in which the overall beamformer consists of a concatenation of an analog RF beamformer implemented using phase shifters (PSs) and a low-dimensional baseband digital beamformer. However, conventional hybrid beamforming designs require high-resolution PSs, which are expensive. In this paper, we consider transceiver design for maximizing the spectral efficiency of a large-scale MIMO system with hybrid beamforming architecture where only finite-resolution PSs are available at both ends. We propose a heuristic transceiver design for the critical case where the number of RF chains is equal to the number of data streams. We show that the proposed hybrid beamforming design can achieve a rate close to that of optimal exhaustive search. We also suggest how to generalize the algorithm for the setting where the number of RF chains exceeds the number of data streams. We show that the generalized algorithm can use the extra RF chains to significantly improve the system performance in the case of low-resolution PSs.

In this paper, an improved and low-complexity signal detection approach for large-scale multiple-input multiple-output (MIMO) systems has been proposed. This approach utilizes the preconditioning technique to accelerate the conventional detection algorithm based on Gauss-Seidel (GS) iterative method, and achieves a detection performance close to the minimum mean square error (MMSE) detection algorithm with relatively small iteration counts. It also outperforms the counterparts based on the Neumann series (NS) expansion and the conjugate gradient (CG) method in poor propagation environments, such as MIMO systems with large loading or correlated factors. The corresponding architecture is also proposed with both novelty and scalability. It takes advantage of the cyclic-shift property of the GS method, and therefore facilitates the hardware implementation. Both numerical results and complexity analysis demonstrate that the proposed detector is efficient and suitable for large-scale MIMO systems.

Radio-frequency (RF) impairments in the transceiver hardware of communication systems (e.g., phase noise (PN), high power amplifier (HPA) nonlinearities, or in-phase/quadrature-phase (I/Q) imbalance) can severely degrade the performance of traditional multiple-input multiple-output (MIMO) systems. Although calibration algorithms can partially compensate these impairments, the remaining distortion still has substantial impact. Despite this, most prior works have not analyzed this type of distortion. In this paper, we investigate the impact of residual transceiver hardware impairments on the MIMO system performance. In particular, we consider a transceiver impairment model, which has been experimentally validated, and derive analytical ergodic capacity expressions for both exact and high signal-to-noise ratios (SNRs). We demonstrate that the capacity saturates in the high-SNR regime, thereby creating a finite capacity ceiling. We also present a linear approximation for the ergodic capacity in the low-SNR regime, and show that impairments have only a second-order impact on the capacity. Furthermore, we analyze the effect of transceiver impairments on large-scale MIMO systems; interestingly, we prove that if one increases the number of antennas at one side only, the capacity behaves similar to the finite-dimensional case. On the contrary, if the number of antennas on both sides increases with a fixed ratio, the capacity ceiling vanishes; thus, impairments cause only a bounded offset in the capacity compared to the ideal transceiver hardware case.

TLS Method for Pulse Transfer Function Estimation in the Presence of Input and Output Noise

Next generation 5G network provides solution for rigorous demand for data with increasing network speeds. Now-a-days cell free 5G network can resolve many issues such as interference that appear in cellular configuration. Massive MIMO with cell free configuration is one of the solutions for massive-MIMO with conventional cellular network. This is also called cell free (CF) MIMO in massive. A CF Massive-MIMO system consists of number of access points (APs) which are distributed uniformly. It serves few numbers of user equipment at same frequency or time resources. It is based on characteristics of channel which is measured directly. Eachuser and APs possess only single antenna. The channel state information is acquired by APs through time division duplex(TDD)operation. Users transmits uplink pilot signals. The Multiplexing/de-multiplexing performed by APs through matched filtering during uplink and conjugate beamforming during downlink. The simplified expressions for uplink and downlink throughputs of single user would lead to maximum/minimum power control algorithms. The objective is to focus a comparative analysis on optimized cell free network which has maximum the coverage area, and a minimum transmission power. The interference problem can be resolved in operation of cell free network and it can resolve by using cell free network that appear normally in cellular network. The major challenge is to achieve the benefits of cell-free configuration that it can scalable to large network with increase in number of users. A frame work could arrive for scalable massive MIMO in cell free system by using the concept of dynamic cooperation cluster (DCC). Several algorithms are used for jointly initial access to appoints master AP, assignment of pilot signals to invite other APs, and cluster formation. It has been proved that it can be scalable. Also, the standard channel estimation, different precoding methods, and combining methods have been adopted for making the system scalable and robust.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.