Superposition coded modulation with peak-power limitation

We apply clipping to superposition coded modulation (SCM) systems to reduce the peak-to-average power ratio (PAPR) of the transmitted signal. The impact on performance is investigated by evaluating the mutual information driven by the induced peak-power-limited input signals. It is shown that the rate loss is marginal for moderate clipping thresholds if optimal encoding/decoding is used. This fact is confirmed in examples where capacityapproaching component codes are used together with the maximum a posteriori probability (MAP) detection. In order to reduce the detection complexity of SCM with a large number of layers, we develop a suboptimal soft compensation (SC) method that is combined with soft-input soft-output (SISO) decoding algorithms in an iterative manner. A variety of simulation results for additive white Gaussian noise (AWGN) and fading channels are presented. It is shown that with the proposed method, the effect of clipping can be efficiently compensated and a good tradeoff between PAPR and bit-error rate (BER) can be achieved. Comparisons with other coded modulation schemes demonstrate that SCM offers significant advantages for high-rate transmissions over fading channels

Machine Learning in Digital Signal Processing for Optical Transmission Systems

The future demand for digital information will exceed the capabilities of current optical communication systems, which are approaching their limits due to component and fiber intrinsic non-linear effects. Machine learning methods are promising to find new ways of leverage the available resources and to explore new solutions. Although, some of the machine learning methods such as adaptive non-linear filtering and probabilistic modeling are not novel in the field of telecommunication, enhanced powerful architecture designs together with increasing computing power make it possible to tackle more complex problems today. The methods presented in this work apply machine learning on optical communication systems with two main contributions. First, an unsupervised learning algorithm with embedded additive white Gaussian noise (AWGN) channel and appropriate power constraint is trained end-to-end, learning a geometric constellation shape for lowest bit-error rates over amplified and unamplified links. Second, supervised machine learning methods, especially deep neural networks with and without internal cyclical connections, are investigated to combat linear and non-linear inter-symbol interference (ISI) as well as colored noise effects introduced by the components and the fiber. On high-bandwidth coherent optical transmission setups their performances and complexities are experimentally evaluated and benchmarked against conventional digital signal processing (DSP) approaches. This thesis shows how machine learning can be applied to optical communication systems. In particular, it is demonstrated that machine learning is a viable designing and DSP tool to increase the capabilities of optical communication systems

Self-interference cancellation for full-duplex MIMO transceivers

PhD ThesisIn recent years, there has been enormous interest in utilizing the full-duplex (FD) technique with multiple-input multiple-output (MIMO) systems to complement the evolution of fifth generation technologies. Transmission and reception using FD-MIMO occur simultaneously over the same frequency band and multiple antennas are employed in both sides. The motivation for employing FD-MIMO is the rapidly increasing demand on frequency resources, and also FD has the ability to improve spectral efficiency and channel capacity by a factor of two compared to the conventional half-duplex technique. Additionally, MIMO can enhance the diversity gain and enable FD to acquire further degrees of freedom in mitigating the self-interference (SI). The latter is one of the key challenges degrading the performance of systems operating in FD mode due to local transmission which involves larger power level than the signals of interest coming from distance sources that are significantly more attenuated due to path loss propagation phenomena. Various approaches can be used for self-interference cancellation (SIC) to tackle SI by combining passive suppression with the analogue and digital cancellation techniques. Moreover, active SIC techniques using special domain suppression based on zero-forcing and null-space projection (NSP) can be exploited for this purpose too. The main contributions of this thesis can be summarized as follows. Maximum-ratio combining with NSP are jointly exploited in order to increase the signal-to-noise ratio (SNR) of the desired path and mitigate the undesired loop path, respectively, for an equalize-and-forward (EF) relay using FD-MIMO. Additionally, an end-to-end performance analysis of the proposed system is obtained in the presence of imperfect channel state information by formulating mathematically the exact closed-form solutions for the signal-to-interference-plus-noise ratio (SINR) distribution, outage probability, and average symbol-error rate for uncoded M-ary phase-shift keying over Rayleigh fading channels and in the presence of additive white Gaussian noise (AWGN). The coefficients of the EF-relay are designed to attain the minimum mean-square error (MMSE) between the transmission symbols. Comparison of the results obtained with relevant state-of-the-art techniques suggests significant improvements in the SINR figures and system capacity. Furthermore, iterative detection and decoding (IDD) are proposed to mitigate the residual self-interference (SI) remaining after applying passive suppression along with two stages of SI cancellation (SIC) filters in the analogue and digital domains for coded FD bi-directional transceiver based multiple antennas. IDD comprises an adaptive MMSE filter with log-likelihood ratio demapping, while the soft-in soft-out decoder utilizes the maximum a posteriori (MAP) algorithm. The proposed system’s performance is evaluated in the presence of AWGN over non-selective (flat) Rayleigh fading single-input multiple-output (SIMO) and MIMO channels. However, the results of the analyses can be applied to multi-path channels if orthogonal frequency division multiplexing is utilised with a proper length of cyclic prefix in order to tackle the channels’ frequency-selectivity and delay spread. Simulation results are presented to demonstrate the bit-error rate (BER) performance as a function of the SNR, revealing a close match to the SI-free case for the proposed system. Furthermore, the results are validated by deriving a tight upper bound on the performance of rate-1=2 convolutional codes for FD-SIMO and FD-MIMO systems for different modulation schemes under the same conditions, which asymptotically exhibits close agreement with the simulated BER performance.Ministry of Higher Education and Scientific Research (MoHESR), and the University of Mosul and to the Iraqi Cultural Attache in London for providing financial support for my PhD scholarship