Towards Fully Optimized BICM Transceivers

Bit-interleaved coded modulation (BICM) transceivers often use equally spaced constellations and a random interleaver. In this paper, we propose a new BICM design, which considers hierarchical (nonequally spaced) constellations, a bit-level multiplexer, and multiple interleavers. It is shown that this new scheme increases the degrees of freedom that can be exploited in order to improve its performance. Analytical bounds on the bit error rate (BER) of the system in terms of the constellation parameters and the multiplexing rules are developed for the additive white Gaussian Noise (AWGN) and Nakagami-$m$ fading channels. These bounds are then used to design the BICM transceiver. Numerical results show that, compared to conventional BICM designs, and for a target BER of $10^{-6}$, gains up to 3 dB in the AWGN channel are obtained. For fading channels, the gains depend on the fading parameter, and reach 2 dB for a target BER of $10^{-7}$ and $m=5$.Comment: Submitted to the IEEE Transactions on Communication

Four-Dimensional Coded Modulation with Bit-wise Decoders for Future Optical Communications

Coded modulation (CM) is the combination of forward error correction (FEC) and multilevel constellations. Coherent optical communication systems result in a four-dimensional (4D) signal space, which naturally leads to 4D-CM transceivers. A practically attractive design paradigm is to use a bit-wise decoder, where the detection process is (suboptimally) separated into two steps: soft-decision demapping followed by binary decoding. In this paper, bit-wise decoders are studied from an information-theoretic viewpoint. 4D constellations with up to 4096 constellation points are considered. Metrics to predict the post-FEC bit-error rate (BER) of bit-wise decoders are analyzed. The mutual information is shown to fail at predicting the post- FEC BER of bit-wise decoders and the so-called generalized mutual information is shown to be a much more robust metric. For the suboptimal scheme under consideration, it is also shown that constellations that transmit and receive information in each polarization and quadrature independently (e.g., PM-QPSK, PM- 16QAM, and PM-64QAM) outperform the best 4D constellations designed for uncoded transmission. Theoretical gains are as high as 4 dB, which are then validated via numerical simulations of low-density parity check codes

Optimization of a Coded-Modulation System with Shaped Constellation

Conventional communication systems transmit signals that are selected from a signal constellation with uniform probability. However, information-theoretic results suggest that performance may be improved by shaping the constellation such that lower-energy signals are selected more frequently than higher-energy signals. This dissertation presents an energy efficient approach for shaping the constellations used by coded-modulation systems. The focus is on designing shaping techniques for systems that use a combination of amplitude phase shift keying (APSK) and low-density parity check (LDPC) coding. Such a combination is typical of modern satellite communications, such as the system used by the DVB-S2 standard.;The system implementation requires that a subset of the bits at the output of the LDPC encoder are passed through a nonlinear shaping encoder whose output bits are more likely to be a zero than a one. The constellation is partitioned into a plurality of sub-constellations, each with a different average signal energy, and the shaping bits are used to select the sub-constellation. An iterative receiver exchanges soft information among the demodulator, LDPC decoder, and shaping decoder. Parameters associated with the modulation and shaping code are optimized with respect to information rate, while the design of the LDPC code is optimized for the shaped modulation with the assistance of extrinsic-information transfer (EXIT) charts. The rule for labeling the constellation with bits is optimized using a novel hybrid cost function and a binary switching algorithm.;Simulation results show that the combination of constellation shaping, LDPC code optimization, and optimized bit labeling can achieve a gain in excess of 1 dB in an additive white Gaussian noise (AWGN) channel at a rate of 3 bits/symbol compared with a system that adheres directly to the DVB-S2 standard

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

Near-capacity MIMOs using iterative detection

In this thesis, Multiple-Input Multiple-Output (MIMO) techniques designed for transmission over narrowband Rayleigh fading channels are investigated. Specifically, in order to providea diversity gain while eliminating the complexity of MIMO channel estimation, a Differential Space-Time Spreading (DSTS) scheme is designed that employs non-coherent detection. Additionally, in order to maximise the coding advantage of DSTS, it is combined with Sphere Packing (SP) modulation. The related capacity analysis shows that the DSTS-SP scheme exhibits a higher capacity than its counterpart dispensing with SP. Furthermore, in order to attain additional performance gains, the DSTS system invokes iterative detection, where the outer code is constituted by a Recursive Systematic Convolutional (RSC) code, while the inner code is a SP demapper in one of the prototype systems investigated, while the other scheme employs a Unity Rate Code (URC) as its inner code in order to eliminate the error floor exhibited by the system dispensing with URC. EXIT charts are used to analyse the convergence behaviour of the iteratively detected schemes and a novel technique is proposed for computing the maximum achievable rate of the system based on EXIT charts. Explicitly, the four-antenna-aided DSTSSP system employing no URC precoding attains a coding gain of 12 dB at a BER of 10-5 and performs within 1.82 dB from the maximum achievable rate limit. By contrast, the URC aidedprecoded system operates within 0.92 dB from the same limit.On the other hand, in order to maximise the DSTS system’s throughput, an adaptive DSTSSP scheme is proposed that exploits the advantages of differential encoding, iterative decoding as well as SP modulation. The achievable integrity and bit rate enhancements of the system are determined by the following factors: the specific MIMO configuration used for transmitting data from the four antennas, the spreading factor used and the RSC encoder’s code rate.Additionally, multi-functional MIMO techniques are designed to provide diversity gains, multiplexing gains and beamforming gains by combining the benefits of space-time codes, VBLASTand beamforming. First, a system employing Nt=4 transmit Antenna Arrays (AA) with LAA number of elements per AA and Nr=4 receive antennas is proposed, which is referred to as a Layered Steered Space-Time Code (LSSTC). Three iteratively detected near-capacity LSSTC-SP receiver structures are proposed, which differ in the number of inner iterations employed between the inner decoder and the SP demapper as well as in the choice of the outer code, which is either an RSC code or an Irregular Convolutional Code (IrCC). The three systems are capable of operating within 0.9, 0.4 and 0.6 dB from the maximum achievable rate limit of the system. A comparison between the three iteratively-detected schemes reveals that a carefully designed two-stage iterative detection scheme is capable of operating sufficiently close to capacity at a lower complexity, when compared to a three-stage system employing a RSC or a two-stage system using an IrCC as an outer code. On the other hand, in order to allow the LSSTC scheme to employ less receive antennas than transmit antennas, while still accommodating multiple users, a Layered Steered Space-Time Spreading (LSSTS) scheme is proposed that combines the benefits of space-time spreading, V-BLAST, beamforming and generalised MC DS-CDMA. Furthermore, iteratively detected LSSTS schemes are presented and an LLR post-processing technique is proposed in order to improve the attainable performance of the iteratively detected LSSTS system.Finally, a distributed turbo coding scheme is proposed that combines the benefits of turbo coding and cooperative communication, where iterative detection is employed by exchanging extrinsic information between the decoders of different single-antenna-aided users. Specifically, the effect of the errors induced in the first phase of cooperation, where the two users exchange their data, on the performance of the uplink in studied, while considering different fading channel characteristics

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems