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

Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years

Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions

Channel estimation, data detection and carrier frequency offset estimation in OFDM systems

Orthogonal Frequency Division Multiplexing (OFDM) plays an important role in the implementation of high data rate communication. In this thesis, the problems of data detection and channel and carrier frequency offset estimation in OFDM systems are studied. Multi-symbol non-coherent data detection is studied which performs data detection by processing multiple symbols without the knowledge of the channel impulse response (CIR). For coherent data detection, the CIR needs to be estimated. Our objective in this thesis is to work on blind channel estimators which can extract the CIR using just one block of received OFDM data. A blind channel estimator for (Single Input Multi Output) SIMO OFDM systems is derived. The conditions under which the estimator is identifiable is studied and solutions to resolve the phase ambiguity of the proposed estimator are given.A channel estimator for superimposed OFDM systems is proposed and its CRB is derived. The idea of simultaneous transmission of pilot and data symbols on each subcarrier, the so called superimposed technique, introduces the efficient use of bandwidth in OFDM context. Pilot symbols can be added to data symbols to enable CIR estimation without sacrificing the data rate. Despite the many advantages of OFDM, it suffers from sensitivity to carrier frequency offset (CFO). CFO destroys the orthogonality between the subcarriers. Thus, it is necessary for the receiver to estimate and compensate for the frequency offset. Several high accuracy estimators are derived. These include CFO estimators, as well as a joint iterative channel/CFO estimator/data detector for superimposed OFDM. The objective is to achieve CFO estimation with using just one OFDM block of received data and without the knowledge of CIR

Data Detection and Channel Estimation of OFDM Systems Using Differential Modulation

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation technique which is robust against multipath fading and very easy to implement in transmitters and receivers using the inverse fast Fourier transform and the fast Fourier transform. A guard interval using cyclic prefix is inserted in each OFDM symbol to avoid the inter-symbol interference. This guard interval should be at least equal to, or longer than the maximum delay spread of the channel to combat against inter-symbol interference properly. In coherent detection, channel estimation is required for the data detection of OFDM systems to equalize the channel effects. One of the popular techniques is to insert pilot tones (reference signals) in OFDM symbols. In conventional method, pilot tones are inserted into every OFDM symbols. Channel capacity is wasted due to the transmission of a large number of pilot tones. To overcome this transmission loss, incoherent data detection is introduced in OFDM systems, where it is not needed to estimate the channel at first. We use differential modulation based incoherent detection in this thesis for the data detection of OFDM systems. Data can be encoded in the relative phase of consecutive OFDM symbols (inter-frame modulation) or in the relative phase of an OFDM symbol in adjacent subcarriers (in-frame modulation). We use higher order differential modulation for in-frame modulation to compare the improvement of bit error rate. It should be noted that the single differential modulation scheme uses only one pilot tone, whereas the double differential uses two pilot tones and so on. Thus overhead due to the extra pilot tones in conventional methods are minimized and the detection delay is reduced. It has been observed that the single differential scheme works better in low SNRs (Signal to Noise Ratios) with low channel taps and the double differential works better at higher SNRs. Simulation results show that higher order differential modulation schemes don¡¯t have any further advantages. For inter-frame modulation, we use single differential modulation where only one OFDM symbol is used as a reference symbol. Except the reference symbol, no other overhead is required. We also perform channel estimation using differential modulation. Channel estimation using differential modulation is very easy and channel coefficients can be estimated very accurately without increasing any computational complexity. Our simulation results show that the mean square channel estimation error is about ¡¼10¡½^(-2) at an SNR of 30 dB for double differential in-frame modulation scheme, whereas channel estimation error is about ¡¼10¡½^(-4) for single differential inter-frame modulation. Incoherent data detection using classical DPSK (Differential Phase Shift Keying) causes an SNR loss of approximately 3 dB compared to coherent detection. But in our method, differential detection can estimate the channel coefficients very accurately and our estimated channel can be used in simple coherent detection to improve the system performance and minimize the SNR loss that happens in conventional method

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe

Signal Structure of the Starlink Ku-Band Downlink

We develop a technique for blind signal identification of the Starlink downlink signal in the 10.7 to 12.7 GHz band and present a detailed picture of the signal's structure. Importantly, the signal characterization offered herein includes the exact values of synchronization sequences embedded in the signal that can be exploited to produce pseudorange measurements. Such an understanding of the signal is essential to emerging efforts that seek to dual-purpose Starlink signals for positioning, navigation, and timing, despite their being designed solely for broadband Internet provision