Multi-stage turbo equalization for MIMO systems with hybrid ARQ

Turbo equalization schemes based on minimum mean square error (MMSE) criteria available in the literature for multiple-input multiple-output (MIMO) systems are computationally expensive, as they require a relatively large matrix inversion. In this paper, we propose a low complexity turbo equalization (TE) scheme with successive interference cancellation for orthogonal frequency division multiplexing (OFDM) based MIMO receiver systems (TE-SIC-MIMO). TE-SIC-MIMO leverages on the soft feedback symbol estimate to selectively remove the intercarrier interference (ICI) and co-antenna interference (CAI) from the received data, thus turning the system matrix into an easily invertible form. Extrinsic Information Transfer (EXIT) chart analysis supplemented with numerical simulation results shows that TE-SIC-MIMO performance under static and rapidly time varying multipath conditions is mostly on par with MMSE turbo equalization scheme available in the literature, that is based on a sub-matrix of the system matrix at signal-to-noise ratios (SNR) of practical interest. However, the computation complexity of TE-SIC-MIMO turns out to be much lesser than the TE scheme considered.

A new hybrid automatic repeat request (HARQ) scheme is proposed for multiple-input multiple-output (MIMO) systems when the acknowledgment (ACK) bundling is applied for packet retransmissions. In the proposed HARQ scheme, both Chase combining (CC) and incremental redundancy (IR) are utilised for a retransmission request, and the proposed HARQ scheme is called a multiple retransmission strategy-based HARQ (MRS-HARQ). During the reception process of the MRS-HARQ, IR packets obtain signal-to-noise ratio gains using the extended detection with CC packets and the improved IR packets are utilised to eliminate the interference from the CC packets. This sequential reliability improvement allows the MRS-HARQ to decrease the occurrence of the deficient transmissions, and the proposed MRS-HARQ can achieve an improved error performance and throughput over the conventional CC-based and IR-based HARQ schemes with the ACK bundling.

In this paper, an enhanced linear minimum mean square error (LMMSE) turbo equalization scheme is proposed for multiple-input multiple-output (MIMO) communication systems with bit-interleaved coded modulation (BICM) in the time domain and multiplexing in the space domain. The proposed turbo equalization scheme outperforms the conventional LMMSE turbo equalization by adopting two new signal processing techniques. First, it performs hybrid soft interference cancellation (HSOIC) by subtracting the soft decisions of the interfering symbols, and the soft decisions are calculated by using a hybrid of the a priori information at the equalizer input and the a posteriori information at the equalizer output. Second, it employs a novel block-wise reliability-based ordering scheme such that more “reliable” symbols are detected before the less “reliable” ones to reduce error propagation in HSOIC. The symbol reliability information is based on the symbol a priori probability, as a unique byproduct of turbo detection, thus can be obtained with very small overhead. A low-complexity approximation of the enhanced MIMO LMMSE turbo equalization is also proposed to balance the tradeoff between complexity and performance. The performance of the enhanced MIMO LMMSE turbo equalization has been verified through both numerical simulations and the undersea experimental data collected in the SPACE08 experiment launched near Martha's Vineyard, Edgartown, MA, in 2008.

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.

This paper proposes a low-complexity turbo block decision feedback equalizer (BDFE) with reliability-based successive soft interference cancellation (SSIC) for multiple-input multiple-output (MIMO) systems. Turbo equalization in MIMO systems needs to combat both intersymbol interference (ISI) due to frequency-selective fading and multiplexing interference (MI) among data streams transmitted by different antennas. The proposed MIMO BDFE performs SSIC on both ISI in the time-domain and MI in the space-domain. For an SSIC receiver, the order in which the symbols are detected and cancelled is critical to the system performance, yet it is often overlooked in the literature. We propose a new reliability-based detection ordering scheme, where symbols with higher a priori reliability are detected before those with lower a priori reliability. The reliability information can be obtained from the a priori probability at the input of the soft-decision equalizer. The reliability-based ordering scheme is enabled by the turbo equalization structure, and it yields an extra equalization gain that is unavailable in a conventional equalization system. Furthermore, the adoption of non-linear BDFE in MIMO systems enables a low-complexity sequence-based log-likelihood ratio (LLR) evaluation, which is superior to the symbol-based LLR evaluation method employed by most other low-complexity turbo equalizers.

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 recent years, multiple-input multiple-output (MIMO) systems, which use several transmit and receive antennas, have attracted much attention for high performance radio systems. In MIMO systems, the channel estimation is important to distinguish transmit signals from multiple transmit antennas. While the space-alternating generalized expectation-maximization (SAGE) algorithm is known to offer good channel estimation and data detection. We proposed earlier the minimum mean square error (MMSE)-based SAGE algorithm for MIMO systems where the MMSE estimation is used for channel estimation. We showed that the proposed MMSE-based SAGE algorithm can achieve the better bit error rate (BER) than the maximum likelihood (ML) detection with training symbols. The MMSE channel estimation needs the knowledge of the maximum Doppler frequency F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> for deriving the covariance matrix of the channel and the variance sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of additive white Gaussian noise (AWGN). Additionally, the computation of the MMSE channel estimation requires O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations where L is the transmitted frame length. Thus, its computational complexity is high. In this paper, we propose a zero-forcing (ZF)-based SAGE algorithm for channel estimation and data detection in MIMO systems that does not need the knowledge of Fd and sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Since the computation of the proposed ZF-based SAGE algorithm requires O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations where N is the number of transmit antennas, its computational complexity is low. We show that the proposed ZF-based tracking SAGE algorithm with less computational complexity can achieve almost the same BER as that of the MMSE-based tracking SAGE algorithm

Orbital Angular Momentum (OAM) has been considered as a new dimension in wireless transmission. In Multiple Input Multiple Output (MIMO) system, OAM can also be utilized as the new Degrees of Freedom (DoF) to manipulate the beamforming instead of the traditional Channel State Information (CSI) feedback, so that the computation complexity of the beamforming dramatically decreases, as well as the capacity of the transmission is maintained approaching the upper bound of the MIMO system. For example, this beamforming manipulation with OAM is extremely efficient in the Line of Sight (LoS) MIMO system. In this paper, the mathematical model of the DoF in beamforming manipulation in MIMO with OAM is established. The computation complexity is evaluated, and compared in LoS environment within MIMO, MIMO-OAM, and OAM. The quantitatively simulation confirms the fact that MIMO-OAM has the best trade-off between the capacity and the computation complexity, which gives a consolidated evidence that the MIMOOAM scheme can be adopted as an efficient beamforming scheme in LoS backhaul links between the base station and the self backhaul small cell station, or Near Field Communications (NFC) directly between mobile terminals. Due to these promising applications, MIMO-OAM may become one of the candidate technologies in Beyond 5G (B5G) mobile communications.

Hybrid automatic repeat request (HARQ) can make efficient and reliable transmission possible for radio communications. However, in the HARQ scheme, packet control information (PCI), such as HARQ block identifiers (HARQ block ids) in selective repeat ARQ (SR-ARQ), are required and the reduction of PCI overhead is an issue. In particular, type-II HARQ handles a large amount of PCI because it includes the HARQ block ids and sub-block ids. The sub-block id identifies the set of parity bits in the HARQ block of the corresponding sub-block, and is used to combine the sub-blocks. This report proposes a method of PCI reduction suitable for type-II HARQ with SR-ARQ. The method can inform the HARQ block id and sub-block id of each HARQ block by sending only the information on how many HARQ blocks are contained in each 1st, 2nd, 3rd, and 4th transmission group. The estimated size of reduced PCI and simulation results of our TDD communication system confirms that the proposed method greatly reduces PCI and improves data throughput compared to a conventional method.

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.

The turbo equalization principle, based on Berrou's (1993) turbo principle, is a high performance receiver scheme to mitigate intersymbol interference (ISI) caused by a frequency selective channel. Several turbo equalization (TEQ) schemes, like Douillard's (1995) MAP/MLSE TEQ, Laot's (1997) TEQ scheme by means of soft ISI cancellation and turbo equalization based on MMSE linear equalization have been presented different in performance and computational complexity. One of these schemes, turbo equalization based on MMSE linear equalization achieves nearly MAP/MLSE performance, while reducing the computational complexity tremendously. We extend this receiver scheme to 16-QAM and present simulation results for different ISI channels.

A novel Frequency Domain Turbo Equalization (FD-TE) is proposed in multiple input multiple output (MIMO) systems, in which equalization is executed on Single Frequency Tone (SFT). It is proposed that received data from all receive antennas on one frequency tone are considered jointly, and then minimum mean square error (MMSE) equalization is implemented. This algorithm can reduce the calculation complexity greatly, and has the same performance as the conventional algorithms. After equalization, data from one transmit antenna on all frequency tones are processed together and transformed into time domain. Later, extrinsic Log-likelihood ratio (LLR) is calculated and sent to Soft-In Soft-Out (SISO) decoder. Theoretical deduction and simulation prove that it can obtain the same performance with a low complexity.

Model reference robust control (MRRC) is derived for MIMO systems that have a right Hermite normal form which is SPR and diagonal, and then for systems whose right Hermite normal form is diagonal but not SPR. It is shown that MRRC of multiple input multiple output (MIMO) systems is an extension of model reference control (MRC) of MIMO systems and MRRC of SISO systems. Unwanted coupling exists in many physical MIMO systems. The recursive backstepping procedure used in non-SPR SISO systems cannot be directly applied to diagonal MIMO non-SPR systems without the introduction of the augmented matrix or a precompensator. It is shown that MRRC decouples MIMO systems using only input and output measurements rather than state feedback.

This paper presents an optimized turbo equalizer based on Laot's turbo equalization scheme by means of soft intersymbol interference (ISI) cancellation. Due to Berrou's turbo-principle, common turbo equalization schemes use the extrinsic information as a-priori input for the equalizer. In contrast to this, we propose the use of the full soft output channel decoder's information for the turbo interference canceller. The performance of this improved turbo equalizer by means of soft interference cancellation is evaluated in the environment of two channels, which introduce severe ISI. Simulation results show that the proposed turbo equalizer achieves a performance gain of up to 4 dB compared to Laot's original approach. The performance gain is achieved without any increase of computational complexity. Moreover, we apply a modified extrinsic information transfer (EXIT) chart to the proposed turbo equalizer and illustrate the convergence advantages in comparison to Laot's approach.

In this research work, the authors propose an error constrained data transmission approach using non-orthogonal multiple access (NOMA) protocol with hybrid automatic repeat request (HARQ) and different packet combining schemes. In this proposed work, the transmitter simultaneously transmits two data packets to the users (UEs) by considering the superposition coding technique in the power domain. The decoding scheme of the receiver changes according to the message decoding status in the previous rounds. This combined protocol has the capability of realizing ultra-reliability, error correction power in high error prone wireless links. The receiver adopts successive interference cancelation (SIC) to decode its own packet. Retransmission process is performed when the data is erroneously received by the destination and the receiver corrects the error by applying HARQ with Packet Combining (HARQ-PC) and HARQ with Aggressive Packet Combining (HARQ-APC) schemes. This paper analyzes the data rates, outage probabilities and bit error rates of UEs for downlink NOMA systems with respect to the transmit power using some basic parameters. The proposed schemes can greatly outperform the conventional NOMA with HARQ (NOMA-HARQ) in terms of data rates, outage probability and bit error rate in dynamic wireless channels.