FPGA-based Tabu search for detection in large-scale MIMO systems

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.

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.

Multiple Input Multiple Output (MIMO) low complexity receiver which utilizes the compressive sensing detection for Spatial Modulation in large scale MIMO system in order to reduce the system complexity. In conventional MIMO system; huge amount of antennas is used at both ends to exploit the multipath propagation. This system maximizes the throughput performance and data rates are increased but only at the cost of high hardware complexity and increased power-consumption. Spatial Modulation Matching Pursuit (SMMP) is the proposed enhanced CS technique used for the improvement of detection performance. Hence, this paper reviews recent research findings concerning normalized Compressive Sensing (CS) detection algorithm, used for Spatial Modulation (SM) in massive MIMO, to lowers the signal processing complexity, which in result improves the energy efficiency of system against that of conventional MIMO system.This strategy is achieved by involving additional structures and sparsity in which a single transmitter antenna or a subset of it is turned on at each case to transmit a certain data. The subset of antenna which is turned on for transmission depends on approaching data bits. Therefore, the total increase in the spectral efficiency of the system is given as base-two logarithm of whole antennas at the transmitter. It reduces the signal processing load at base station and doesn’t depend upon any synchronization between transmitters. The Spatial Modulation Matching Pursuit used prevents the Inter Channel Interference (ICI) of the system which in result improves the Bit Error Rate (BER) performance than the typical MIMO system.

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 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.

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.

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.

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.

In conventional ways of fast responding and high performing model predictive control using Field Programmable Gate Arrays (FPGA), the approaches developed are applicable only to Single Input Single Output System (SISO). Many real time systems are Multiple Input Multiple Output (MIMO) systems and loop interaction is a major concern and has to be reduced in control of MIMO systems. Also, direct digital control with ability to solve the Quadratic Programming (QP) problem online, which can be applied to complex systems with fast response time and/or embedded applications and with limited computational resource for supervisory control approach, is required in many complex industrial MIMO systems. A novel direct digital control of Model predictive controller with FPGA for a MIMO system which considers and reduces loop interaction and which can also be implemented with very less investment is proposed here. The system was modeled from first principles and then simulated in Labview environment for MIMO system. Implementation of the system and response was verified for a stirred tank system. The proposed system has good performance in terms of reduction in loop interaction which is a major concern in implementation of controller for MIMO systems. Also, direct digital control of MIMO system instead of supervisory control was accomplished.

In the case of large-scale Multiple-Input Multiple-Output (MIMO) systems, the Time-Frequency Training (TFT) of OFDM and their transmission schemes can solve the high-dimensional problems of channel estimation in the wireless communication systems. In the wireless technologies, their physical layer can carry out the process of transmission inside the variable channels. The noise that was induced by these channels may also degrade the performance of the system that concerns Bit Error Rate (BER). So, in this paper, a Selection Mapping (SLM) technique in the TFT-OFDM for the mobile large-scale MIMO systems has been proposed for minimising the BER. This SLM technique tends to produce the data with these phase vectors for discovering the candidate vectors and also the IFFT operation which is done on the TFT-OFDM symbol that obtains a minimum BER that has to be transmitted. This scheme performance has been compared with that of the existing TDM-OFDM with BER. The performance analysis of MIMO TFT-OFDM is considered for biomedical signal transmission through high-speed wireless transmission. The results of this experiment prove that the proposed scheme achieves a better performance than that of the existing schemes.

Consider a multiple input multiple output (MIMO) system with pulse amplitude modulation (PAM), where each transmit antenna is allowed to choose a different transmission delay. (We refer to this system as a MIMO system with asynchronous PAM.) Such MIMO systems have been studied before for increasing diversity and avoiding unintentional beamforming. We quantify the capacity/outage capacity improvement due to asynchronism and the corresponding spectral leakage (if any) for MIMO point-to-point and MIMO broadcast channels. We show that under commonly used transmitter spectral mask constraints, the channel has unbounded degrees of freedom. Synchronous PAM exploits only a finite number of these degrees of freedom, and asynchronism helps to improve capacity. For most cases of interest, two distinct shifts attain most of the capacity improvement. These gains are robust to interference from adjacent carriers. Analysis of the spectrum of the asynchronous PAM suggests that optimal water-filling based schemes are useful for improving capacity only in directional links due to possible spectral leakage, while uniform power allocation leads to capacity improvement without any extra spectral leakage.

For large-scale multiple-input multiple-output (MIMO) systems, linear minimum mean square error (MMSE) method is one of the most near-optimal ways for signal detection. However, MMSE involves matrix inversion which is of high complexity for computation. In this paper, a Lanczos-based method is proposed to solve the problem by transferring the matrix inversion computation into an iteration process of solving a linear equation. The simulation result proves that the BER of Lanczos-based method outperforms the recently proposed Neumann series approximation algorithm with the same SNR. At the same time, the Lanczos-based method could reduce the computational complexity from O(K3) to O(K2), where K is the number of users. Then the convergence speed is analysed by Kaniel-Paige-Saad theory. Finally, the result shows that the performance of Lanczos-based method achieves the classical MMSE exact matrix inversion methods with only a small number of iterations.

Time division duplexing (TDD) operation is usually used for distributed large-scale multiple-input multiple-output (MIMO) systems to perform the downlink precoding by the uplink channel estimation due to the channel reciprocity. However, the channel reciprocity is often disabled by the non-symmetric transceiver radio frequency (RF) circuits at both sides of the link. This paper is focused on the reciprocity calibration for the distributed large-scale MIMO systems where both access points (APs) and user equipments (UEs) have multiple antennas. Theoretical analysis show that RF mismatches at the UEs still bring the inter-stream interference (ISI) and lead to a significant performance loss, although with the perfect partial calibration. Then, to avoid eigenvalue decomposition (EVD) operation of the total least squares (TLS) method, an iterative algorithm named as iterative coordinate descent (ICD) method is proposed to perform the full calibration, which significantly reduces the complexity and essentially achieves the performance of the TLS method.