A Space Communications Study Final Report, Sep. 15, 1965 - Sep. 15, 1966

Reception of frequency modulated signals passed through deterministic and random time-varying channel

Engineering evaluations and studies. Volume 3: Exhibit C

High rate multiplexes asymmetry and jitter, data-dependent amplitude variations, and transition density are discussed

Ranging and tracking system for proximity operations, phase 1

A study task is reported which is directed towards developing a conceptual design of a small, lightweight range and range rate radar sensor system to meet NASA's requirements for accurate short-range and velocity measurements in an orbital environment. Within the context of the requirements, the short range implies system operation at 0 m to 1850 m (6000 ft) and accurate implies a range measurement to within 1 sigma accuracy of 0.20 m (0.67 ft) and a range rate (velocity) measurement to within 1 sigma accuracy of 0.01 m/sec (0.033 ft/sec)

A space communications study Final report, 15 Sep. 1966 - 15 Sep. 1967

Investigation of signal to noise ratios and signal transmission efficiency for space communication system

Hybrid receiver study

The results are presented of a 4 month study to design a hybrid analog/digital receiver for outer planet mission probe communication links. The scope of this study includes functional design of the receiver; comparisons between analog and digital processing; hardware tradeoffs for key components including frequency generators, A/D converters, and digital processors; development and simulation of the processing algorithms for acquisition, tracking, and demodulation; and detailed design of the receiver in order to determine its size, weight, power, reliability, and radiation hardness. In addition, an evaluation was made of the receiver's capabilities to perform accurate measurement of signal strength and frequency for radio science missions

End-to-End Deep Learning in Optical Fibre Communication Systems

Conventional communication systems consist of several signal processing blocks, each performing an individual task at the transmitter or receiver, e.g. coding, modulation, or equalisation. However, there is a lack of optimal, computationally feasible algorithms for nonlinear fibre communications as most techniques are based upon classical communication theory, assuming a linear or perturbed by a small nonlinearity channel. Consequently, the optimal end-to-end system performance cannot be achieved using transceivers with sub-optimum modules. Carefully chosen approximations are required to exploit the data transmission potential of optical fibres. In this thesis, novel transceiver designs tailored to the nonlinear dispersive fibre channel using the universal function approximator properties of artificial neural networks (ANNs) are proposed and experimentally verified. The fibre-optic system is implemented as an end-to-end ANN to allow transceiver optimisation over all channel constraints in a single deep learning process. While the work concentrates on highly nonlinear short-reach intensity modulation/direct detection (IM/DD) fibre links, the developed concepts are general and applicable to different models and systems. Found in many data centre, metro and access networks, the IM/DD links are severely impaired by the dispersion-induced inter-symbol interference and square-law photodetection, rendering the communication channel nonlinear with memory. First, a transceiver based on a simple feedforward ANN (FFNN) is investigated and a training method for robustness to link variations is proposed. An improved recurrent ANN-based design is developed next, addressing the FFNN limitations in handling the channel memory. The systems' performance is verified in first-in-field experiments, showing substantial increase in transmission distances and data rates compared to classical signal processing schemes. A novel algorithm for end-to-end optimisation using experimentally-collected data and generative adversarial networks is also developed, tailoring the transceiver to the specific properties of the transmission link. The research is a key milestone towards end-to-end optimised data transmission over nonlinear fibre systems

Distributed Digital Radios for Land Mobile Radio Applications

The main objective of this dissertation is to develop the second generation of Distributed Digital Radio (DDR) technology. A DDR II modem provides an integrated voice/data service platform, higher data rates and better throughput performance as compared to a DDR I modem. In order to improve the physical layer performance of DDR modems an analytical framework is first developed to model the Bit Error Rate (BER) performance of Orthogonal Frequency Division Multiplexing over Frequency Modulation (OFDM/FM) systems. The use of OFDM provides a spectrally efficient method of transmitting data over LMR channels. However, the high Peak-to-Average (PAR) of OFDM signals results in either a low Signal-to-Noise Ratio (SNR) at FM receiver or a high non-linear distortion of baseband signal in the FM transmitter. This dissertation presents an analytical framework to highlight the impact of high PAR of OFDM signal on OFDM/FM systems. A novel technique for reduction of PAR of OFDM called Linear Scaling Technique (LST) is developed. The use of LST mitigates the signal distortion occurring in OFDM over FM systems. Another important factor which affects the throughput of LMR networks is the Push-to-Talk (PTT) delay. A PTT delay refers to the delay between the instant when a PTT switch on a conventional LMR radio is keyed/unkeyed and a response is observed at the radio output. It can be separated into a Receive-To-Transmit Switch Interval (RTSI) or a Transmit-To-Receive Switch Interval (TRSI). This dissertation presents the typical RTSI delay values, distributions and their impact on throughput performance of LMR networks. An analytical model is developed to highlight the asymmetric throughput problem and the unintentional denial of service (UDOS) occurring in heterogeneous LMR networks consisting of radios with different PTT delay profiles. This information will be useful in performance and capacity planning of LMR networks in future

Multipath Mitigation Techniques for Satellite-Based Positioning Applications

This chapter addressed the challenges encountered by a GNSS signal due to multipath propagation. A wide range of correlation-based multipath mitigation techniques were discussed and the performance of some of these techniques were evaluated in terms of running average error and root-mean-square error. Among the analyzed multipath mitigation techniques, RSSML, in general, achieved the best multipath mitigation performance in moderate-to-high C/N0 scenarios (for example, 30 dB-Hz and onwards). The other techniques, such as PT(Diff2) and HRC showed good multipath mitigation performance only in high C/N0 scenarios (for example, 40 dB-Hz and onwards). The other new technique SBME offered slightly better multipath mitigation performance to the well-known nEML DLL at the cost of an additional correlator. However, as the GNSS research area is fast evolving with many potential applications, it remains a challenging topic for future research to investigate the feasibility of these multipath mitigation techniques with the multitude of signal modulations, spreading codes, and spectrum placements that are (or are to be) proposed.publishedVersionPeer reviewe

Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network

In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection approach using a cycle-consistent generative adversarial network (CycleGAN) is proposed for communication systems without any prior knowledge of underlying channel distributions. Specifically, we propose the CycleGAN detector by constructing a bidirectional loop of two modified least squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to model the transmission process, while the backward LS-GAN learns to detect the received signals. By optimizing the cycle-consistency of the transmitted and received signals through this loop, the proposed method is trained online and semi-supervisedly using both the pilots and the received payload data. As such, the demand on labelled training dataset is considerably controlled, and thus the overhead is effectively reduced. Numerical results show that the proposed CycleGAN detector achieves better performance in terms of both bit error-rate (BER) and achievable rate than existing semi-blind deep learning (DL) detection methods as well as conventional linear detectors, especially when considering signal distortion due to the nonlinearity of power amplifiers (PA) at the transmitter