Filter Bank Multicarrier for Massive MIMO

This paper introduces filter bank multicarrier (FBMC) as a potential candidate in the application of massive MIMO communication. It also points out the advantages of FBMC over OFDM (orthogonal frequency division multiplexing) in the application of massive MIMO. The absence of cyclic prefix in FBMC increases the bandwidth efficiency. In addition, FBMC allows carrier aggregation straightforwardly. Self-equalization, a property of FBMC in massive MIMO that is introduced in this paper, has the impact of reducing (i) complexity; (ii) sensitivity to carrier frequency offset (CFO); (iii) peak-to-average power ratio (PAPR); (iv) system latency; and (v) increasing bandwidth efficiency. The numerical results that corroborate these claims are presented.Comment: 7 pages, 6 figure

Waveform Advancements and Synchronization Techniques for Generalized Frequency Division Multiplexing

To enable a new level of connectivity among machines as well as between people and machines, future wireless applications will demand higher requirements on data rates, response time, and reliability from the communication system. This will lead to a different system design, comprising a wide range of deployment scenarios. One important aspect is the evolution of physical layer (PHY), specifically the waveform modulation. The novel generalized frequency division multiplexing (GFDM) technique is a prominent proposal for a flexible block filtered multicarrier modulation. This thesis introduces an advanced GFDM concept that enables the emulation of other prominent waveform candidates in scenarios where they perform best. Hence, a unique modulation framework is presented that is capable of addressing a wide range of scenarios and to upgrade the PHY for 5G networks. In particular, for a subset of system parameters of the modulation framework, the problem of symbol time offset (STO) and carrier frequency offset (CFO) estimation is investigated and synchronization approaches, which can operate in burst and continuous transmissions, are designed. The first part of this work presents the modulation principles of prominent 5G candidate waveforms and then focuses on the GFDM basic and advanced attributes. The GFDM concept is extended towards the use of OQAM, introducing the novel frequency-shift OQAM-GFDM, and a new low complexity model based on signal processing carried out in the time domain. A new prototype filter proposal highlights the benefits obtained in terms of a reduced out-of-band (OOB) radiation and more attractive hardware implementation cost. With proper parameterization of the advanced GFDM, the achieved gains are applicable to other filtered OFDM waveforms. In the second part, a search approach for estimating STO and CFO in GFDM is evaluated. A self-interference metric is proposed to quantify the effective SNR penalty caused by the residual time and frequency misalignment or intrinsic inter-symbol interference (ISI) and inter-carrier interference (ICI) for arbitrary pulse shape design in GFDM. In particular, the ICI can be used as a non-data aided approach for frequency estimation. Then, GFDM training sequences, defined either as an isolated preamble or embedded as a midamble or pseudo-circular pre/post-amble, are designed. Simulations show better OOB emission and good estimation results, either comparable or superior, to state-of-the-art OFDM system in wireless channels

Frequency Domain Independent Component Analysis Applied To Wireless Communications Over Frequency-selective Channels

In wireless communications, frequency-selective fading is a major source of impairment for wireless communications. In this research, a novel Frequency-Domain Independent Component Analysis (ICA-F) approach is proposed to blindly separate and deconvolve signals traveling through frequency-selective, slow fading channels. Compared with existing time-domain approaches, the ICA-F is computationally efficient and possesses fast convergence properties. Simulation results confirm the effectiveness of the proposed ICA-F. Orthogonal Frequency Division Multiplexing (OFDM) systems are widely used in wireless communications nowadays. However, OFDM systems are very sensitive to Carrier Frequency Offset (CFO). Thus, an accurate CFO compensation technique is required in order to achieve acceptable performance. In this dissertation, two novel blind approaches are proposed to estimate and compensate for CFO within the range of half subcarrier spacing: a Maximum Likelihood CFO Correction approach (ML-CFOC), and a high-performance, low-computation Blind CFO Estimator (BCFOE). The Bit Error Rate (BER) improvement of the ML-CFOC is achieved at the expense of a modest increase in the computational requirements without sacrificing the system bandwidth or increasing the hardware complexity. The BCFOE outperforms the existing blind CFO estimator [25, 128], referred to as the YG-CFO estimator, in terms of BER and Mean Square Error (MSE), without increasing the computational complexity, sacrificing the system bandwidth, or increasing the hardware complexity. While both proposed techniques outperform the YG-CFO estimator, the BCFOE is better than the ML-CFOC technique. Extensive simulation results illustrate the performance of the ML-CFOC and BCFOE approaches

Multi-stage Wireless Signal Identification for Blind Interception Receiver Design

Protection of critical wireless infrastructure from malicious attacks has become increasingly important in recent years, with the widespread deployment of various wireless technologies and dramatic growth in user populations. This brings substantial technical challenges to the interception receiver design to sense and identify various wireless signals using different transmission technologies. The key requirements for the receiver design include estimation of the signal parameters/features and classification of the modulation scheme. With the proper identification results, corresponding signal interception techniques can be developed, which can be further employed to enhance the network behaviour analysis and intrusion detection. In detail, the initial stage of the blind interception receiver design is to identify the signal parameters. In the thesis, two low-complexity approaches are provided to realize the parameter estimation, which are based on iterative cyclostationary analysis and envelope spectrum estimation, respectively. With the estimated signal parameters, automatic modulation classification (AMC) is performed to automatically identify the modulation schemes of the transmitted signals. A novel approach is presented based on Gaussian Mixture Models (GMM) in Chapter 4. The approach is capable of mitigating the negative effect from multipath fading channel. To validate the proposed design, the performance is evaluated under an experimental propagation environment. The results show that the proposed design is capable of adapting blind parameter estimation, realize timing and frequency synchronization and classifying the modulation schemes with improved performances

An enhanced multicarrier modulation system for mobile communications

PhD ThesisThe recent revolution in mobile communications and the increased demand on more efficient transmission systems influence the research to enhance and invent new modulation techniques. Orthogonal frequency division multiplexing with offset quadrature amplitude modulation (OFDM/OQAM) is one of the multicarrier modulations techniques that overcomes some of the weaknesses of the conventional OFDM in term of bandwidth and power efficiencies. This thesis presents a novel multicarrier modulation scheme with improved performance in mobile communications context. Initially, the theoretical principles behind OFDM and OFDM/OQAM are discussed and the advantages of OFDM/OQAM over OFDM are highlighted. The time-frequency localization of pulse shapes is examined over different types of pulses. The effect of the localization and the pulse choice on OFDM/OQAM performance is demonstrated. The first contribution is introducing a new variant of multicarrier modulation system based on the integration of the Walsh-Hadamard transform with the OFDM/OQAM modulator. The full analytical transmission model of the system is derived over flat fading and frequency selective channels. Next, because of the critical requirement of low implementation complexity in mobile systems, a new fast algorithm transform is developed to reduce the implementation complexity of the system. The introduced fast algorithm has demonstrated a remarkable 60 percent decrease in the hardware requirement compared to the cascaded configuration. Although, the problem of high peak to average power ratio (PAPR) is one of the main drawbacks that associated with most multicarrier modulation techniques, the new system achieved lower values compared to the conventional systems. Subsequently, three new algorithms to reduce PAPR named Walsh overlapped selective mapping (WOSLM) for a high PAPR reduction, simplified selective mapping (SSLM) for a very low implementation complexity and Walsh partial transmit sequence (WPTS), are developed. Finally, in order to assess the reliability of the presented system in this thesis at imperfect environments, the performance of the system is investigated in the presence of high power amplifier, channel estimation errors, and carrier frequency offset (CFO). Two channel estimations algorithms named enhanced pair of pilots (EPOP) and averaged enhanced pair of pilots (AEPOP), and one CFO estimator technique called frequency domain (FD) CFO estimator, are suggested to provide reliable performance.Ministry of Higher Education and Scientific Research (MOHSR) of Ira

New OFDM schemes based on orthogonal transforms for mobile communications systems :

PhD ThesisIn this thesis, two new orthogonal frequency division multiplexing (OFDM) systems are presented. The first scheme proposes a new OFDM system transceiver based on the C-transform, which is termed C-OFDM. Over multipath channels, the C-OFDM achieves 10 dB signal-to-noise ratio (SNR) gain at 10−4 bit-error-rate (BER), in comparison to the OFDM that based on the is discrete cosine transform (DCT-OFDM) and the conventional OFDM schemes. It also reduces the peak-to-average power ratio (PAPR) of the OFDM signal by about 1 dB and in some cases up to 3 dB. In the second scheme, a new fast, orthogonal X-transform is produced. The proposed X-transform is then used in a new OFDM named X-OFDM to greatly reduce the complexity, the PAPR and the BER. The proposed scheme achieves around 15 dB SNR gain in comparison to the conventional OFDM at 10−4 BER and reduces the average PAPR (over 105 OFDM symbol) by about 6 dB for N =1024 subcarriers. Furthermore, in this study, the X-transform is utilized to produce a new Alamouti space-time OFDM (ST-OFDM). The proposed ST-X-OFDM scheme reduces the transmitter complexity and achieves important SNR gain over the conventional ST-OFDM systems. The BER performance of the proposed schemes in the presence of solid-state power amplifiers (SSPAs) is also investigated analytically and by simulation. It shows that the X-OFDM is resilient to the SSPAs nonlinear distortion whereas the C-OFDM may lead to BER impairment in the presence of the SSPA. Furthermore, a coding technique to mitigate the sensitivity of the COFDM scheme to the SSPA is also proposed in this study. In this research, mathematical models for the proposed C-OFDM, XOFDM and ST-X-OFDM, which tightly match the simulation results over a diverse range of transmission scenarios and mapping schemes, are also derived. In addition, the BER performance of the proposed COFDM and X-OFDM schemes in the presence of the carrier frequency offset (CFO), with and without frequency synchronization algorithm, are also investigated. The proposed C-OFDM and X-OFDM schemes are more sensitive to the CFO than the conventional schemes. However, when frequency synchronization algorithm is used, both the proposed schemes retain their significant BER improvement in comparison to the conventional schemes.Ministry of Higher Education and Scientific Research (MOHSR), Iraq and to the Iraqi cultural attach- London for supporting me financially during my study in England

Advanced methods in automatic modulation classification for emerging technologies

Modulation classification (MC) is of large importance in both military and commercial communication applications. It is a challenging problem, especially in non-cooperative wireless environments, where channel fading and no prior knowledge on the incoming signal are major factors that deteriorate the reception performance. Although the average likelihood ratio test method can provide an optimal solution to the MC problem with unknown parameters, it suffers from high computational complexity and in some cases mathematical intractability. Instead, in this research, an array-based quasi-hybrid likelihood ratio test (qHLRT) algorithm is proposed, which depicts two major advantages. First, it is simple yet accurate enough parameter estimation with reduced complexity. Second the incorporation of antenna arrays offers an effective ability to combat fading. Furthermore, a practical array-based qHLRT classifier scheme is implemented, which applies maximal ratio combining (MRC) to increase the accuracy of both carrier frequency offset (CFO) estimation and likelihood function calculation in channel fading. In fact, double CFO estimations are executed in this classifier. With the first the unknown CFO, phase offsets and amplitudes are estimated as prerequisite for MRC operation. Then, MRC is performed using these estimates, followed by a second CFO estimator. Since the input of the second CFO estimator is the output of the MRC, fading effects on the incoming signals are removed significantly and signal-to-noise ratio (SNR) is augmented. As a result, a more accurate CFO estimate is obtained. Consequently, the overall classification performance is improved, especially in low SNR environment. Recently, many state-of-the-arts communication technologies, such as orthogonal frequency division multiplexing (OFDM) modulations, have been emerging. The need for distinguishing OFDM signal from single carrier has become obvious. Besides, some vital parameters of OFDM signals should be extracted for further processing. In comparison to the research on MC for single carrier single antenna transmission, much less attention has been paid to the MC for emerging modulation methods. A comprehensive classification system is proposed for recognizing the OFDM signal and extracting its parameters. An automatic OFDM modulation classifier is proposed, which is based on the goodness-of-fittest. Since OFDM signal is Gaussian, Cramer-von Mises technique, working on the empirical distribution function, has been applied to test the presence of the normality. Numerical results show that such approach can successfully identify OFDM signals from single carrier modulations over a wide SNR range. Moreover, the proposed scheme can provide the acceptable performance when frequency-selective fading is present. Correlation test is then applied to estimate OFDM cyclic prefix duration. A two-phase searching scheme, which is based on Fast Fourier Transform (FFT) as well as Gaussianity test, is devised to detect the number of subcarriers. In the first phase, a coarse search is carried out iteratively. The exact number of subcarriers is determined by the fine tune in the second phase. Both analytical work and numerical results are presented to verify the efficiency of the proposed scheme

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed