Robust Digital Signal Recovery for LEO Satellite Communications Subject to High SNR Variation and Transmitter Memory Effects

This paper proposes a robust digital signal recovery (DSR) technique to tackle the high signal-to-noise ratio (SNR) variation and transmitter memory effects for broadband power efficient down-link in next-generation low Earth orbit (LEO) satellite constellations. The robustness against low SNR is achieved by concurrently integrating magnitude normalization and noise feature filtering using a filtering block built with one batch normalization (BN) layer and two bidirectional long short-term memory (BiLSTM) layers. Moreover, unlike existing deep neural network-based DSR techniques (DNN-DSR), which failed to effectively take into account the memory effects of radio-frequency power amplifiers (RF-PAs) in the model design, the proposed BiLSTM-DSR technique can extracts the sequential characteristics of the adjacent in-phase (I) and quadrature (Q) samples, and hence can obtain superior memory effects compensation compared with the DNN-DSR technique. Experimental validation results of the proposed BiLSTM-DSR with a 100 MHz bandwidth OFDM signal demonstrate an excellent performance of 11.83 dB and 9.4% improvement for adjacent channel power ratio (ACPR) and error vector magnitude (EVM), respectively. BiLSTM-DSR also outperforms the existing DNN-DSR technique in terms of the ACPR and EVM by 2.4 dB and 0.9%, which provides a promising solution for developing deep learning-assisted receivers for high-throughput LEO satellite networks

Transmitter Linearization for mm-Wave Communications Systems

There is an ever increasing need for enabling higher data rates in modern communication systems which brings new challenges in terms of the power consumption and nonlinearity of hardware components. These problems become prominent in power amplifiers (PAs) and can significantly degrade the performance of transmitters, and hence the overall communication system. Hence, it is of central importance to design efficient PAs with a linear operation region. This thesis proposes a methodology and a comprehensive framework to address this challenge. This is accomplished by application of predistortion to a mm-wave PA and an E-band IQ transmitter while investigating the trade-offs between linearity, efficiency and predistorter complexity using the proposed framework.In the first line of work, we have focused on a mm-wave PA. A PA has high efficiency at high input power at the expense of linearity, whereas it operates linearly for lower input power levels while sacrificing efficiency. To attain both linearity and efficiency, predistortion is often used to compensate for the PA nonlinearity. Yet, the trade-offs related to predistortion complexities are not fully understood. To address this challenge, we have used our proposed framework for evaluation of predistorters using modulated test signals and implemented it using digital predistortion and a mm-wave PA. This set-up enabled us to investigate the trade-offs between linearity, efficiency and predistorter complexity in a systematic manner. We have shown that to achieve similar linearity levels for different PA classes, predistorters with different complexities are needed and provided guidelines on the achievable limits in term linearity for a given predistorter complexity for different PA classes.In the second line of work, we have focused on linearization of an E-band transmitter using a baseband analog predistorter (APD) and under constraints given by a spectrum emission standard. In order to use the above proposed framework with these components, characterizations of the E-band transmitter and the APD are performed. In contrast to typical approaches in the literature, here joint mitigation of the PA and I/Q modulator impairments is used to model the transmitter. Using the developed models, optimal model parameters in terms of output power at the mask limit are determined. Using these as a starting point, we have iteratively optimized operating point of the APD and linearized the E-band transmitter. The experiments demonstrated that the analog predistorter can successfully increase the output power by 35% (1.3 dB) improvement while satisfying the spectrum emission mask

Equalization and detection for digital communication over nonlinear bandlimited satellite communication channels

This dissertation evaluates receiver-based methods for mitigating the effects due to nonlinear bandlimited signal distortion present in high data rate satellite channels. The effects of the nonlinear bandlimited distortion is illustrated for digitally modulated signals. A lucid development of the low-pass Volterra discrete time model for a nonlinear communication channel is presented. In addition, finite-state machine models are explicitly developed for a nonlinear bandlimited satellite channel. A nonlinear fixed equalizer based on Volterra series has previously been studied for compensation of noiseless signal distortion due to a nonlinear satellite channel. This dissertation studies adaptive Volterra equalizers on a downlink-limited nonlinear bandlimited satellite channel. We employ as figure of merits performance in the mean-square error and probability of error senses. In addition, a receiver consisting of a fractionally-spaced equalizer (FSE) followed by a Volterra equalizer (FSE-Volterra) is found to give improvement beyond that gained by the Volterra equalizer. Significant probability of error performance improvement is found for multilevel modulation schemes. Also, it is found that probability of error improvement is more significant for modulation schemes, constant amplitude and multilevel, which require higher signal to noise ratios (i.e., higher modulation orders) for reliable operation. The maximum likelihood sequence detection (MLSD) receiver for a nonlinear satellite channel, a bank of matched filters followed by a Viterbi detector, serves as a probability of error lower bound for the Volterra and FSE-Volterra equalizers. However, this receiver has not been evaluated for a specific satellite channel. In this work, an MLSD receiver is evaluated for a specific downlink-limited satellite channel. Because of the bank of matched filters, the MLSD receiver may be high in complexity. Consequently, the probability of error performance of a more practical suboptimal MLSD receiver, requiring only a single receive filter, is evaluated

Modeling of ADS-B data transmission via satellite

For modelling transmission of ADS-B messages via low-orbit satellite constellation Iridium, the original model of a communication channel “Aircraft-to-Satellite-to-Ground Station” was built using MATLAB Simulink. The model comprises “Aircraft Uplink Transmitter” (Bernoulli Random Binary Generator, Convolutional Encoder, BPSK Baseband Modulator, High Power Amplifier with a memoryless nonlinearity, Transmitter Dish Antenna Gain), “Uplink Path” (Free Space Path Loss, Phase/Frequency Offset), “Satellite Transponder” (Receiver Dish Antenna Gain, Satellite Receiver System Temperature, Complex Baseband Amplifier, Phase Noise, Transmitter Dish Antenna Gain), “Downlink Path” (Free Space Path Loss, Phase/Frequency Offset), “Ground Station Downlink Receiver” (Receiver Dish Antenna Gain, Ground Receiver System Temperature, Viterbi Decoder), “Error Rate Calculation” block and “Display”. The modelling was realized without and with convolutional coding (r = 3/4, K = 7) at different noise temperatures and free space losses. Dependencies of a Bit Error Rate on free space path losses, antenna's diameter, phase/frequency off-sets, satellite transponder linear gain, aircraft and satellite transponder high power amplifier back-off level, and phase noise were received and analysed

Digital communication receivers using Gaussian processes for machine learning

We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function of the received symbols for discrete inputs. GPR can be presented as a nonlinear MMSE estimator and thus capable of achieving optimal performance from MMSE viewpoint. Also, the design of digital communication receivers can be viewed as a detection problem, for which GPC is specially suited as it assigns posterior probabilities to each transmitted symbol. We explore the suitability of GPs as nonlinear digital communication receivers. GPs are Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which can then be set optimally. GPs outperform state-of-the-art nonlinear machine learning approaches that prespecify their hyperparameters or rely on cross validation. We illustrate the advantages of GPs as digital communication receivers for linear and nonlinear channel models for short training sequences and compare them to state-of-the-art nonlinear machine learning tools, such as support vector machines

Classical Capacity of Arbitrarily Distributed Noisy Quantum Channels

With the rapid deployment of quantum computers and quantum satellites, there is a pressing need to design and deploy quantum and hybrid classical-quantum networks capable of exchanging classical information. In this context, we conduct the foundational study on the impact of a mixture of classical and quantum noise on an arbitrary quantum channel carrying classical information. The rationale behind considering such mixed noise is that quantum noise can arise from different entanglement and discord in quantum transmission scenarios, like different memories and repeater technologies, while classical noise can arise from the coexistence with the classical signal. Towards this end, we derive the distribution of the mixed noise from a classical system's perspective, and formulate the achievable channel capacity over an arbitrary distributed quantum channel in presence of the mixed noise. Numerical results demonstrate that capacity increases with the increase in the number of photons per usage

PAPR and ICI reduction techniques for OFDM based satellite communication systems

Multi-carrier systems such as orthogonal frequency division multiplexing (OFDM) are significantly affected by peak-to-average-power ratio (PAPR). Unfortunately, the high PAPR inherent to OFDM signals envelopes will occasionally drive high power amplifiers (HPAs) to operate in the nonlinear region of their characteristic curve. The nonlinearity of the HPA exhibits amplitude and phase distortions, which cause loss of orthogonality among the subcarriers (SCs), and hence, inter-carrier interference (ICI) is introduced in the transmitted signal. The ICI power is proportional to the amplitude of the signal at the amplifier input and it may cause a considerable bit error rate (BER) degradation. A plethora of research has been devoted to reduce the performance degradation due to the PAPR problem inherent to OFDM systems. Some of the reported techniques such as amplitude clipping have low-complexity; on the other hand, they suffer from various problems such as in-band distortion and out-of-band expansion. Signal companding methods have low-complexity, good distortion and spectral properties; however, they have limited PAPR reduction capabilities. Advanced techniques such as coding, partial transmit sequences (PTS) and selected mapping (SLM) have also been considered for PAPR reduction. Such techniques are efficient and distortionless, nevertheless, their computational complexity is high and requires the transmission of several side information (SI) bits. In this thesis, a new low-complexity scheme is proposed based on the PTS that employs two inverse fast Fourier transforms (IFFTs) and two circulant transform matrices, in order to reduce complexity and improve the system performance. Furthermore, the low-complexity scheme is simplified by omitting one of the circulant transform matrices in order to reduce both the computational complexity and the number of SI bits at the cost of a small reduction in PAPR and BER performance. It is well known that, accurate PAPR estimation requires oversampling of the transmitted signal, which in turn results in increased complexity. More importantly, minimising the PAPR does not necessarily minimise the distortion produced by the nonlinearity of the HPA. Therefore, minimising PAPR does not necessarily imply that the BER will be minimised too. Efficient and less complex schemes for BER reduction of OFDM systems in the presence of nonlinear HPA and/or carrier frequency offset (CFO) are proposed. These proposed techniques are based on predicting the distortion introduced by the nonlinearity of HPA and/or CFO. Subsequently, techniques such as the PTS and SLM are invoked to minimise the distortion and BER. Three distortion metrics are adopted in this thesis: inter-modulation distortion (IMD), peak interference-to-carrier ratio (PICR) and distortion-to-signal power ratio (DSR). Monte Carlo simulations will confirm that the DSR and PICR are more reliable than the PAPR and IMD for selecting the coefficients of the PTS and SLM to minimise the BER. Furthermore, complexity analyses demonstrate that the proposed schemes offer significant complexity reduction when compared to standard PAPR-based methods. A closed form solution for accurate BER for the OFDM signals perturbed by both the HPA nonlinearity and CFO was derived. Good agreement between the simulation results and the theoretical analysis can be obtained for different HPA parameters and CFOs. Finally, efficient approaches to reduce the impact of nonlinear power amplifiers with respect to the BER of OFDM systems are proposed. These are approaches based on: the well-established PAPR schemes, a power amplifier model and a simple single point cross correlator. The optimum phase sequence within the proposed approaches is selected by maximising the correlation between the input and output of the power amplifier model. Simulation results have confirmed that the BER using the proposed approaches is almost identical to the DSR, while the complexity is reduced significantly for particular system configurations.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&