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

Polar Code Design for Irregular Multidimensional Constellations

Polar codes, ever since their introduction, have been shown to be very effective for various wireless communication channels. This, together with their relatively low implementation complexity, has made them an attractive coding scheme for wireless communications. Polar codes have been extensively studied for use with binary-input symmetric memoryless channels but little is known about their effectiveness in other channels. In this paper, a novel methodology for designing multilevel polar codes that works effectively with arbitrary multidimensional constellations is presented. In order for this multilevel design to function, a novel set merging algorithm, able to label such constellations, is proposed.We then compare the error rate performance of our design with that of existing schemes and show that we were able to obtain unprecedented results in many cases over the previously known best techniques at relatively low decoding complexity

Graph Neural Network-Enhanced Expectation Propagation Algorithm for MIMO Turbo Receivers

Deep neural networks (NNs) are considered a powerful tool for balancing the performance and complexity of multiple-input multiple-output (MIMO) receivers due to their accurate feature extraction, high parallelism, and excellent inference ability. Graph NNs (GNNs) have recently demonstrated outstanding capability in learning enhanced message passing rules and have shown success in overcoming the drawback of inaccurate Gaussian approximation of expectation propagation (EP)-based MIMO detectors. However, the application of the GNN-enhanced EP detector to MIMO turbo receivers is underexplored and non-trivial due to the requirement of extrinsic information for iterative processing. This paper proposes a GNN-enhanced EP algorithm for MIMO turbo receivers, which realizes the turbo principle of generating extrinsic information from the MIMO detector through a specially designed training procedure. Additionally, an edge pruning strategy is designed to eliminate redundant connections in the original fully connected model of the GNN utilizing the correlation information inherently from the EP algorithm. Edge pruning reduces the computational cost dramatically and enables the network to focus more attention on the weights that are vital for performance. Simulation results and complexity analysis indicate that the proposed MIMO turbo receiver outperforms the EP turbo approaches by over 1 dB at the bit error rate of $10^{-5}$, exhibits performance equivalent to state-of-the-art receivers with 2.5 times shorter running time, and adapts to various scenarios.Comment: 15 pages, 12 figures, 2 tables. This paper has been accepted for publication by the IEEE Transactions on Signal Processing. Copyright may be transferred without notice, after which this version may no longer be accessibl

Lattice sampling algorithms for communications

In this thesis, we investigate the problem of decoding for wireless communications from the perspective of lattice sampling. In particular, computationally efficient lattice sampling algorithms are exploited to enhance the system performance, which enjoys the system tradeoff between performance and complexity through the sample size. Based on this idea, several novel lattice sampling algorithms are presented in this thesis. First of all, in order to address the inherent issues in the random sampling, derandomized sampling algorithm is proposed. Specifically, by setting a probability threshold to sample candidates, the whole sampling procedure becomes deterministic, leading to considerable performance improvement and complexity reduction over to the randomized sampling. According to the analysis and optimization, the correct decoding radius is given with the optimized parameter setting. Moreover, the upper bound on the sample size, which corresponds to near-maximum likelihood (ML) performance, is also derived. After that, the proposed derandomized sampling algorithm is introduced into the soft-output decoding of MIMO bit-interleaved coded modulation (BICM) systems to further improve the decoding performance. According to the demonstration, we show that the derandomized sampling algorithm is able to achieve the near-maximum a posteriori (MAP) performance in the soft-output decoding. We then extend the well-known Markov Chain Monte Carlo methods into the samplings from lattice Gaussian distribution, which has emerged as a common theme in lattice coding and decoding, cryptography, mathematics. We firstly show that the statistical Gibbs sampling is capable to perform the lattice Gaussian sampling. Then, a more efficient algorithm referred to as Gibbs-Klein sampling is proposed, which samples multiple variables block by block using Klein’s algorithm. After that, for the sake of convergence rate, we introduce the conventional statistical Metropolis-Hastings (MH) sampling into lattice Gaussian distributions and three MH-based sampling algorithms are then proposed. The first one, named as MH multivariate sampling algorithm, is demonstrated to have a faster convergence rate than Gibbs-Klein sampling. Next, the symmetrical distribution generated by Klein’s algorithm is taken as the proposal distribution, which offers an efficient way to perform the Metropolis sampling over high-dimensional models. Finally, the independent Metropolis-Hastings-Klein (MHK) algorithm is proposed, where the Markov chain arising from it is proved to converge to the stationary distribution exponentially fast. Furthermore, its convergence rate can be explicitly calculated in terms of the theta series, making it possible to predict the exact mixing time of the underlying Markov chain.Open Acces

Analysis and design of physical-layer network coding for relay networks

Physical-layer network coding (PNC) is a technique to make use of interference in wireless transmissions to boost the system throughput. In a PNC employed relay network, the relay node directly recovers and transmits a linear combination of its received messages in the physical layer. It has been shown that PNC can achieve near information-capacity rates. PNC is a new information exchange scheme introduced in wireless transmission. In practice, transmitters and receivers need to be designed and optimized, to achieve fast and reliable information exchange. Thus, we would like to ask: How to design the PNC schemes to achieve fast and reliable information exchange? In this thesis, we address this question from the following works: Firstly, we studied channel-uncoded PNC in two-way relay fading channels with QPSK modulation. The computation error probability for computing network coded messages at the relay is derived. We then optimized the network coding functions at the relay to improve the error rate performance. We then worked on channel coded PNC. The codes we studied include classical binary code, modern codes, and lattice codes. We analyzed the distance spectra of channel-coded PNC schemes with classical binary codes, to derive upper bounds for error rates of computing network coded messages at the relay. We designed and optimized irregular repeat-accumulate coded PNC. We modified the conventional extrinsic information transfer chart in the optimization process to suit the superimposed signal received at the relay. We analyzed and designed Eisenstein integer based lattice coded PNC in multi-way relay fading channels, to derive error rate performance bounds of computing network coded messages. Finally we extended our work to multi-way relay channels. We proposed a opportunistic transmission scheme for a pair-wise transmission PNC in a single-input single-output multi-way relay channel, to improve the sum-rate at the relay. The error performance of computing network coded messages at the relay is also improved. We optimized the uplink/downlink channel usage for multi-input multi-output multi-way relay channels with PNC to maximize the degrees of freedom capacity. We also showed that the system sum-rate can be further improved by a proposed iterative optimization algorithm