Differential Coding for MIMO and Cooperative Communications

Multiple-input multiple-output (MIMO) wireless communication systems have been studied a lot in the last ten years. They have many promising features like array gain, diversity gain, spatial multiplexing gain, interference reduction, and improved capacity as compared to a single-input single-output (SISO) systems. However, the increasing demand of high data-rate in current wireless communications systems motivated us to investigate new rate-efficient channel coding techniques. In this dissertation, we study differential modulation for MIMO systems. Differential modulation is useful since it avoids the need of channel estimation by the receiver and saves valuable bandwidth with a slight symbol error-rate (SER) performance loss. The effect of channel correlation over differential MIMO system has not been studied in detail so far. It has been shown in the literature that a linear memoryless precoder can be used to improve the performance of coherent MIMO system over correlated channels. In this work, we implement precoded differential modulation for non-orthogonal and orthogonal space-time blocks codes (STBCs) over arbitrarily correlated channels. We design precoders based on pair-wise error probability (PEP) and approximate SER for differential MIMO system. The carrier offsets, which result because of the movement of the receiver or transmitter and/or scatterers, and mismatch between the transmit and receive oscillators, are a big challenge for the differential MIMO system. The carrier offsets make the flat fading channel behave as a time-varying channel. Hence, the channel does not remain constant over two consecutive STBC block transmission time-intervals, which is a basic assumption for differential systems and the differential systems break down. Double-differential coding is a key technique which could be used to avoid the need of both carrier offset and channel estimation. In this work, we propose a double-differential coding for full-rank and square orthogonal space-time block codes (OSTBC) with M-PSK constellation. A suboptimal decoder for the double-differentially encoded OSTBC is obtained. We also derive a simple PEP upper bound for the double-differential OSTBC. A precoder is also designed based on the PEP upper bound for the double-differential OSTBC to make it more robust against arbitrary MIMO channel correlations. Cooperative communication has several promising features to become a main technology in future wireless communications systems. It has been shown in the literature that the cooperative communication can avoid the difficulties of implementing actual antenna array and convert the SISO system into a virtual MIMO system. In this way, cooperation between the users allows them to exploit the diversity gain and other advantages of MIMO system at a SISO wireless network. A cooperative communication system is difficult to implement in practice because it generally requires that all cooperating nodes must have the perfect knowledge of the channel gains of all the links in the network. This is infeasible in a large wireless network like cellular system. If the users are moving and there is mismatch between the transmit and receive oscillators, the resulting carrier offset may further degrade the performance of a cooperative system. In practice, it is very difficult to estimate the carrier offset perfectly over SISO links. A very small residual offset error in the data may degrade the system performance substantially. Hence, to exploit the diversity in a cooperative system in the presence of carrier offsets is a big challenge. In this dissertation, we propose double-differential modulation for cooperative communication systems to avoid the need of the knowledge of carrier offset and channel gain at the cooperating nodes (relays) and the destination. We derive few useful SER/bit error rate (BER) expressions for double-differential cooperative communication systems using decode-and-forward and amplify-and-forward protocols. Based on these SER/BER expressions, power allocations are also proposed to further improve the performance of these systems. List of papers included in the dissertation This dissertation is based on the following five papers, referred to in the text by letters (A-E)

Antenna selection and performance analysis of MIMO spatial multiplexing systems

Multiple-input multiple-output spatial multiplexing (MIMO-SM) systems offer an essential benefit referred to as spatial multiplexing gain. Two important signal reception techniques for MIMO-SM systems are the zero-forcing (ZF) and ordered successive interference cancellation (OSIC) as, for example, in the case of the decision-feedback detector (DFD). This thesis studies the communication and signal processing aspects of MIMO-SM. We first investigate the bit error rate (BER) performance of the ZF receiver over transmit correlated Ricean flat-fading channels. In particular, for a MIMO channel with M transmit and N receive antennas, we derive an approximation for the average BER of each sub-stream. A closed-form expression for the optimal transmit correlation coefficient, which achieves the maximum capacity (i.e., uncorrelated case) of two-input two-output spatial multiplexing (TITO-SM) systems, is presented. We further propose an antenna selection (AS) approach for the DFD over independent Rayleigh flat-fading channels. The selected transmit antennas are those that maximize both the post-processing signal-to-noise ratio (SNR) at the receiver end, and the system capacity. An upper bound on the outage probability for the AS approach is derived. It is shown that the AS approach achieves a performance comparable to optimal capacity-based selection based on exhaustive search, but at a lower complexity. Finally, we investigate a cross-layer transmit AS approach for the DFD over spatially correlated Ricean flat-fading channels. The selected transmit antennas are those that maximize the link layer throughput of correlated MIMO channels. A closed-form expression for the system throughput with perfect channel estimation is first derived. We further analyze the system performance with pilot-aided channel estimation. In that, we derive a closed-form expression for the post-detection signal-to-noise-plus-interference ratio (SNIR) of each transmitted substream, conditioned on the estimated channels. The derived SNIR is then used to evaluate the overall system throughput. It is observed that the cross-layer AS approach always assigns the transmission to the antenna combination which sees better channel conditions, resulting in a substantial improvement over the optimal capacity-based AS approach. Considering a training-based channel estimation technique, we compare the performance of the proposed cross-layer AS with that of optimal capacity-based AS when employed with a training-based channel estimation. Our results show that the latter is more robust to imperfect channel estimation. However, in all cases, the cross-layer AS delivers higher throughput gains than the capacity-based A

MIMO Systems: Principles, Iterative Techniques, and advanced Polarization

International audienceThis chapter considers the principles of multiple-input multiple-output (MIMO) wireless communication systems as well as some recent accomplishments concerning their implementation. By employing multiple antennas at both transmitter and receiver, very high data rates can be achieved under the condition of deployment in a rich-scattering propagation medium. This interesting property of MIMO systems suggests their use in the future high-rate and high-quality wireless communication systems. Several concepts in MIMO systems are reviewed in this chapter. We first consider MIMO channel models and recall the basic principles of MIMO structures and channel modeling. We next study the MIMO channel capacity and present the early developments in these systems concerning the information theory aspect. Iterative signal detection is considered next; it considers iterative techniques for space-time decoding. As the capacity is inversely proportional to the spatial channel correlation, MIMO antennas should be sufficiently separated, usually by several wavelengths. In order to minimize antennas' deployment, we present advanced polarization diversity techniques for MIMO systems and explain how they can help to reduce the spatial correlation in order to achieve high transmission rates. We end the chapter by considering the application of MIMO systems in local area networks, as well as their potential in enhancing range, localization, and power efficiency of sensor networks

Performance analysis of MIMO-OFDM systems using complex Gaussian quadratic forms

En este trabajo se proponen aportaciones originales para el análisis de prestaciones en sistemas multiantena con múltiples portadoras, mediante el desarrollo de nuevas técnicas matemáticas para el cálculo de probabilidades de error. Así, ha sido posible analizar el efecto de no idealidades (estimación de canal imperfecta, offset de continua, desbalanceo I/Q…) en las prestaciones de sistemas de comunicaciones móviles e inalámbricas

Characterisation and Modelling of Indoor and Short-Range MIMO Communications

Over the last decade, we have witnessed the rapid evolution of Multiple-Input Multiple-Output (MIMO) systems which promise to break the frontiers of conventional architectures and deliver high throughput by employing more than one element at the transmitter (Tx) and receiver (Rx) in order to exploit the spatial domain. This is achieved by transmitting simultaneous data streams from different elements which impinge on the Rx with ideally unique spatial signatures as a result of the propagation paths’ interactions with the surrounding environment. This thesis is oriented to the statistical characterisation and modelling of MIMO systems and particularly of indoor and short-range channels which lend themselves a plethora of modern applications, such as wireless local networks (WLANs), peer-to-peer and vehicular communications. The contributions of the thesis are detailed below. Firstly, an indoor channel model is proposed which decorrelates the full spatial correlation matrix of a 5.2 GHzmeasuredMIMO channel and thereafter assigns the Nakagami-m distribution on the resulting uncorrelated eigenmodes. The choice of the flexible Nakagami-m density was found to better fit the measured data compared to the commonly used Rayleigh and Ricean distributions. In fact, the proposed scheme captures the spatial variations of the measured channel reasonably well and systematically outperforms two known analytical models in terms of information theory and link-level performance. The second contribution introduces an array processing scheme, namely the three-dimensional (3D) frequency domain Space Alternating Generalised Expectation Maximisation (FD-SAGE) algorithm for jointly extracting the dominant paths’ parameters. The scheme exhibits a satisfactory robustness in a synthetic environment even for closely separated sources and is applicable to any array geometry as long as its manifold is known. The algorithm is further applied to the same set of raw data so that different global spatial parameters of interest are determined; these are the multipath clustering, azimuth spreads and inter-dependency of the spatial domains. The third contribution covers the case of short-range communications which have nowadays emerged as a hot topic in the area of wireless networks. The main focus is on dual-branch MIMO Ricean systems for which a design methodology to achieve maximum capacities in the presence of Line-of-Sight (LoS) components is proposed. Moreover, a statistical eigenanalysis of these configurations is performed and novel closed-formulae for the marginal eigenvalue and condition number statistics are derived. These formulae are further used to develop an adaptive detector (AD) whose aim is to reduce the feasibility cost and complexity of Maximum Likelihood (ML)-based MIMO receivers. Finally, a tractable novel upper bound on the ergodic capacity of the above mentioned MIMO systems is presented which relies on a fundamental power constraint. The bound is sufficiently tight and applicable for arbitrary rank of the mean channel matrix, Signal-to-Noise ratio (SNR) and takes the effects of spatial correlation at both ends into account. More importantly, it includes previously reported capacity bounds as special cases

Near-capacity MIMOs using iterative detection

In this thesis, Multiple-Input Multiple-Output (MIMO) techniques designed for transmission over narrowband Rayleigh fading channels are investigated. Specifically, in order to providea diversity gain while eliminating the complexity of MIMO channel estimation, a Differential Space-Time Spreading (DSTS) scheme is designed that employs non-coherent detection. Additionally, in order to maximise the coding advantage of DSTS, it is combined with Sphere Packing (SP) modulation. The related capacity analysis shows that the DSTS-SP scheme exhibits a higher capacity than its counterpart dispensing with SP. Furthermore, in order to attain additional performance gains, the DSTS system invokes iterative detection, where the outer code is constituted by a Recursive Systematic Convolutional (RSC) code, while the inner code is a SP demapper in one of the prototype systems investigated, while the other scheme employs a Unity Rate Code (URC) as its inner code in order to eliminate the error floor exhibited by the system dispensing with URC. EXIT charts are used to analyse the convergence behaviour of the iteratively detected schemes and a novel technique is proposed for computing the maximum achievable rate of the system based on EXIT charts. Explicitly, the four-antenna-aided DSTSSP system employing no URC precoding attains a coding gain of 12 dB at a BER of 10-5 and performs within 1.82 dB from the maximum achievable rate limit. By contrast, the URC aidedprecoded system operates within 0.92 dB from the same limit.On the other hand, in order to maximise the DSTS system’s throughput, an adaptive DSTSSP scheme is proposed that exploits the advantages of differential encoding, iterative decoding as well as SP modulation. The achievable integrity and bit rate enhancements of the system are determined by the following factors: the specific MIMO configuration used for transmitting data from the four antennas, the spreading factor used and the RSC encoder’s code rate.Additionally, multi-functional MIMO techniques are designed to provide diversity gains, multiplexing gains and beamforming gains by combining the benefits of space-time codes, VBLASTand beamforming. First, a system employing Nt=4 transmit Antenna Arrays (AA) with LAA number of elements per AA and Nr=4 receive antennas is proposed, which is referred to as a Layered Steered Space-Time Code (LSSTC). Three iteratively detected near-capacity LSSTC-SP receiver structures are proposed, which differ in the number of inner iterations employed between the inner decoder and the SP demapper as well as in the choice of the outer code, which is either an RSC code or an Irregular Convolutional Code (IrCC). The three systems are capable of operating within 0.9, 0.4 and 0.6 dB from the maximum achievable rate limit of the system. A comparison between the three iteratively-detected schemes reveals that a carefully designed two-stage iterative detection scheme is capable of operating sufficiently close to capacity at a lower complexity, when compared to a three-stage system employing a RSC or a two-stage system using an IrCC as an outer code. On the other hand, in order to allow the LSSTC scheme to employ less receive antennas than transmit antennas, while still accommodating multiple users, a Layered Steered Space-Time Spreading (LSSTS) scheme is proposed that combines the benefits of space-time spreading, V-BLAST, beamforming and generalised MC DS-CDMA. Furthermore, iteratively detected LSSTS schemes are presented and an LLR post-processing technique is proposed in order to improve the attainable performance of the iteratively detected LSSTS system.Finally, a distributed turbo coding scheme is proposed that combines the benefits of turbo coding and cooperative communication, where iterative detection is employed by exchanging extrinsic information between the decoders of different single-antenna-aided users. Specifically, the effect of the errors induced in the first phase of cooperation, where the two users exchange their data, on the performance of the uplink in studied, while considering different fading channel characteristics

Classification and comparison of massive MIMO propagation channel models

Considering great benefits brought by massive multiple-input multiple-output (MIMO) technologies in Internet of things (IoT), it is of vital importance to analyze new massive MIMO channel characteristics and develop corresponding channel models. In the literature, various massive MIMO channel models have been proposed and classified with different but confusing methods, i.e., physical vs. analytical method and deterministic vs. stochastic method. To have a better understanding and usage of massive MIMO channel models, this work summarizes different classification methods and presents an up-to-date unified classification framework, i.e., artificial intelligence (AI)-based predictive channel models and classical non-predictive channel models, which further clarify and combine the deterministic vs. stochastic and physical vs. analytical methods. Furthermore, massive MIMO channel measurement campaigns are reviewed to summarize new massive MIMO channel characteristics. Recent advances in massive MIMO channel modeling are surveyed. In addition, typical non-predictive massive MIMO channel models are elaborated and compared, i.e., deterministic models and stochastic models, which include correlation-based stochastic model (CBSM), geometry-based stochastic model (GBSM), and beam domain channel model (BDCM). Finally, future challenges in massive MIMO channel modeling are given

Optimization of the Fading MIMO Broadcast Channel: Capacity and Fairness Perspectives

Multiple input multiple output (MIMO) systems are now a proven area in current and future telecommunications research. MIMO wireless channels, in which both the transmitter and receiver have multiple antennas, have been shown to provide high bandwidth efficiency. In this thesis, we cover MIMO communications technology with a focus on cellular systems and the MIMO broadcast channel (MIMO-BC). Our development of techniques and analysis for the MIMO-BC starts with a study of single user MIMO systems. One such single user technique is that of antenna selection. In this thesis, we discuss various flavours of antenna selection, with the focus on powerful, yet straightforward, norm-based algorithms. These algorithms are analyzed and the results of this analysis produce a powerful and flexible power scaling factor. This power scaling factor can be used to model the gains of norm-based antenna selection via a single signal-to-noise ratio (SNR)-based parameter. This provides a powerful tool for engineers interested in quickly seeing the effects of antenna selection on their systems. A novel low complexity power allocation scheme follows on from the selection algorithms. Named “Poor Man’s Waterfilling” (PMWF), this scheme can provide significant gains in low SNR systems with very little extra complexity compared to selection alone. We then compare a variety of algorithms for the MIMO-BC, ranging from selection to beamforming, to the optimal, yet complex, iterative waterfilling (ITWF) solution. In this thesis we show that certain algorithms perform better in different scenarios, based on whether there is shadow fading or not. A power scaling factor analysis is also performed on these systems. In the cases where the user’s link gains are widely varying, such as when shadowing and distance effects are present, user fairness is impaired when optimal and near optimal throughput occurs. This leads to a key problem in the MIMO-BC, the balance between user fairness and throughput performance. In an attempt to find a suitable balance between these two factors, we modify the ITWF algorithm by both introducing extra constraints and also by using a novel utility function approach. Both these methods prove to increase user fairness with only minor loss in throughput over the optimal systems. The introduction of MIMO systems to the cellular domain has been hampered by the effects of interference between the cells. In this thesis we move MIMO to the cellular domain, addressing the interference using two different methods. We first use power control, where the transmit power of the base station is controlled to optimize the overall system throughput. This leads to promising results using low complexity methods. Our second method is a novel method of collaboration between base stations. This collaboration transforms neighbouring cell sectors into macro-cells and this results in substantial increases in performance