Lattice Gaussian Sampling by Markov Chain Monte Carlo: Bounded Distance Decoding and Trapdoor Sampling

Sampling from the lattice Gaussian distribution plays an important role in various research fields. In this paper, the Markov chain Monte Carlo (MCMC)-based sampling technique is advanced in several fronts. Firstly, the spectral gap for the independent Metropolis-Hastings-Klein (MHK) algorithm is derived, which is then extended to Peikert's algorithm and rejection sampling; we show that independent MHK exhibits faster convergence. Then, the performance of bounded distance decoding using MCMC is analyzed, revealing a flexible trade-off between the decoding radius and complexity. MCMC is further applied to trapdoor sampling, again offering a trade-off between security and complexity. Finally, the independent multiple-try Metropolis-Klein (MTMK) algorithm is proposed to enhance the convergence rate. The proposed algorithms allow parallel implementation, which is beneficial for practical applications.Comment: submitted to Transaction on Information Theor

A Robust Low-Complexity MIMO Detector for Rank 4 LTE/LTE-A Systems

This paper deals with MIMO detection for rank 4 LTE systems. The paper revolves around a previously known detector [1, by Inkyu Lee, TCOM'2010] which we shall refer to as RCSMLD (Reduced-Constellation-Size-Maximum-Likelihood-Detector). However, a direct application of the scheme in [1, by Inkyu Lee, TCOM'2010] to LTE/LTE-A rank 4 test cases results in unsatisfactory performance. The first contribution of the paper is to introduce several modifications that can jointly be applied to the basic RCSMLD scheme which, taken together, result in excellent performance. Our second contribution is the development of a highly efficient hardware structure for RCSMLD that allows for an implementation with very few multiplications.Comment: Accepted for publication in PIMRC-2014, Washington DC, US

Gibbs sampling detection for large MIMO and MTC uplinks with adaptive modulation

Wireless networks beyond 5G will mostly be serving myriads of sensors and other machine-type communications (MTC), with each device having different requirements in respect to latency, error rate, energy consumption, spectral efficiency or other specifications. Multiple-input multiple-output (MIMO) systems remain a central technology towards 6G, and in cases where massive antenna arrays or cell-free networks are not possible to deploy and only moderately large antenna arrays are allowed, the detection problem at the base-station cannot rely on zero-forcing or matched filters and more complex detection schemes have to be used. The main challenge is to find low complexity, hardware feasible methods that are able to attain near optimal performance. Randomized algorithms based on Gibbs sampling (GS) were proven to perform very close to the optimal detection, even for moderately large antenna arrays, while yielding an acceptable number of operations. However, their performance is highly dependent on the chosen “temperature” parameter (TP). In this paper, we propose and study an optimized variant of the GS method, denoted by triple mixed GS, and where three distinct values for the TP are considered. The method exhibits faster convergence rates than the existing ones in the literature, hence requiring fewer iterations to achieve a target bit error rate. The proposed detector is suitable for symmetric large MIMO systems, however the proposed fixed complexity detector is highly suitable to spectrally efficient adaptively modulated MIMO (AM-MIMO) systems where different types of devices upload information at different bit rates or have different requirements regarding spectral efficiency. The proposed receiver is shown to attain quasi-optimal performance in both scenarios.info:eu-repo/semantics/publishedVersio

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed

Iterative Receiver Techniques for Data-Driven Channel Estimation and Interference Mitigation in Wireless Communications

Wireless mobile communications were initially a way for people to communicate through low data rate voice call connections. As data enabled devices allow users the ability to do much more with their mobile devices, so to will the demand for more reliable and pervasive wireless data. This is being addressed by so-called 4th generation wireless systems based on orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) antenna systems. Mobile wireless customers are becoming more demanding and expecting to have a great user experience over high speed broadband access at any time and anywhere, both indoor and outdoor. However, these promising improvements cannot be realized without an e±cient design of the receiver. Recently, receivers utilizing iterative detection and decoding have changed the fundamental receiver design paradigm from traditional separated parameter estimation and data detection blocks to an integrated iterative parameter estimator and data detection unit. Motivated by this iterative data driven approach, we develop low complexity iterative receivers with improved sensitivity compared to the conventional receivers, this brings potential benefits for the wireless communication system, such as improving the overall system throughput, increasing the macro cell coverage, and reducing the cost of the equipments in both the base station and mobile terminal. It is a challenge to design receivers that have good performance in a highly dynamic mobile wireless environment. One of the challenges is to minimize overhead reference signal energy (preamble, pilot symbols) without compromising the performance. We investigate this problem, and develop an iterative receiver with enhanced data-driven channel estimation. We discuss practical realizations of the iterative receiver for SISO-OFDM system. We utilize the channel estimation from soft decoded data (the a priori information) through frequency-domain combining and time-domain combining strategies in parallel with limited pilot signals. We analyze the performance and complexity of the iterative receiver, and show that the receiver's sensitivity can be improved even with this low complexity solution. Hence, seamless communications can be achieved with better macro cell coverage and mobility without compromising the overall system performance. Another challenge is that a massive amount of interference caused by MIMO transmission (spatial multiplexing MIMO) reduces the performance of the channel estimation, and further degrades data detection performance. We extend the iterative channel estimation from SISO systems to MIMO systems, and work with linear detection methods to perform joint interference mitigation and channel estimation. We further show the robustness of the iterative receivers in both indoor and outdoor environment compared to the conventional receiver approach. Finally, we develop low complexity iterative spatial multiplexed MIMO receivers for nonlinear methods based on two known techniques, that is, the Sphere Decoder (SD) method and the Markov Chain Monte Carlo (MCMC) method. These methods have superior performance, however, they typically demand a substantial increase in computational complexity, which is not favorable in practical realizations. We investigate and show for the first time how to utilize the a priori information in these methods to achieve performance enhancement while simultaneously substantially reducing the computational complexity. In our modified sphere decoder method, we introduce a new accumulated a priori metric in the tree node enumeration process. We show how we can improve the performance by obtaining the reliable tree node candidate from the joint Maximum Likelihood (ML) metric and an approximated a priori metric. We also show how we can improve the convergence speed of the sphere decoder (i.e., reduce the com- plexity) by selecting the node with the highest a priori probability as the starting node in the enumeration process. In our modified MCMC method, the a priori information is utilized for the firrst time to qualify the reliably decoded bits from the entire signal space. Two new robust MCMC methods are developed to deal with the unreliable bits by using the reliably decoded bit information to cancel the interference that they generate. We show through complexity analysis and performance comparison that these new techniques have improved performance compared to the conventional approaches, and further complexity reduction can be obtained with the assistance of the a priori information. Therefore, the complexity and performance tradeoff of these nonlinear methods can be optimized for practical realizations

Doctor of Philosophy

dissertationThe continuous growth of wireless communication use has largely exhausted the limited spectrum available. Methods to improve spectral efficiency are in high demand and will continue to be for the foreseeable future. Several technologies have the potential to make large improvements to spectral efficiency and the total capacity of networks including massive multiple-input multiple-output (MIMO), cognitive radio, and spatial-multiplexing MIMO. Of these, spatial-multiplexing MIMO has the largest near-term potential as it has already been adopted in the WiFi, WiMAX, and LTE standards. Although transmitting independent MIMO streams is cheap and easy, with a mere linear increase in cost with streams, receiving MIMO is difficult since the optimal methods have exponentially increasing cost and power consumption. Suboptimal MIMO detectors such as K-Best have a drastically reduced complexity compared to optimal methods but still have an undesirable exponentially increasing cost with data-rate. The Markov Chain Monte Carlo (MCMC) detector has been proposed as a near-optimal method with polynomial cost, but it has a history of unusual performance issues which have hindered its adoption. In this dissertation, we introduce a revised derivation of the bitwise MCMC MIMO detector. The new approach resolves the previously reported high SNR stalling problem of MCMC without the need for hybridization with another detector method or adding heuristic temperature scaling terms. Another common problem with MCMC algorithms is an unknown convergence time making predictable fixed-length implementations problematic. When an insufficient number of iterations is used on a slowly converging example, the output LLRs can be unstable and overconfident, therefore, we develop a method to identify rare, slowly converging runs and mitigate their degrading effects on the soft-output information. This improves forward-error-correcting code performance and removes a symptomatic error floor in bit-error-rates. Next, pseudo-convergence is identified with a novel way to visualize the internal behavior of the Gibbs sampler. An effective and efficient pseudo-convergence detection and escape strategy is suggested. Finally, the new excited MCMC (X-MCMC) detector is shown to have near maximum-a-posteriori (MAP) performance even with challenging, realistic, highly-correlated channels at the maximum MIMO sizes and modulation rates supported by the 802.11ac WiFi specification, 8x8 256 QAM. Further, the new excited MCMC (X-MCMC) detector is demonstrated on an 8-antenna MIMO testbed with the 802.11ac WiFi protocol, confirming its high performance. Finally, a VLSI implementation of the X-MCMC detector is presented which retains the near-optimal performance of the floating-point algorithm while having one of the lowest complexities found in the near-optimal MIMO detector literature

Doctor of Philosophy

dissertationMultiple-input and multiple-output (MIMO) technique has emerged as a key feature for future generations of wireless communication systems. It increases the channel capacity proportionate to the minimum number of transmit and receive antennas. This dissertation addresses the receiver design for high-rate MIMO communications in at fading environments. The emphasis of the thesis is on the cases where channel state information (CSI) is not available and thus, clever channel estimation algorithms have to be developed to bene t from the maximum available channel capacity. The thesis makes four distinct novel contributions. First, we note that the conventional MCMC-MIMO detector presented in the prior work may deteriorate as SNR increases. We suggest and show through computer simulations that this problem to a great extent can be solved by initializing the MCMC detector with regulated states which are found through linear detectors. We also introduce the novel concept of staged-MCMC in a turbo receiver, where we start the detection process at a lower complexity and increase complexity only if the data could not be correctly detected in the present stage of data detection. Second, we note that in high-rate MIMO communications, joint data detection and channel estimation poses new challenges when a turbo loop is used to improve the quality of the estimated channel and the detected data. Erroneous detected data may propagate in the turbo loop and, thus, degrade the performance of the receiver signi cantly. This is referred to as error propagation. We propose a novel receiver that decorrelates channel estimation and the detected data to avoid the detrimental e ect of error propagation. Third, the dissertation studies joint channel estimation and MIMO detection over a continuously time-varying channel and proposes a new dual-layer channel estimator to overcome the complexity of optimal channel estimators. The proposed dual-layer channel estimator reduces the complexity of the MIMO detector with optimal channel estimator by an order of magnitude at a cost of a negligible performance degradation, on the order of 0.1 to 0.2 dB. The fourth contribution of this dissertation is to note that the Wiener ltering techniques that are discussed in this dissertation and elsewhere in the literature assume that channel (time-varying) statistics are available. We propose a new method that estimates such statistics using the coarse channel estimates obtained through pilot symbols. The dissertation also makes an additional contribution revealing di erences between the MCMC-MIMO and LMMSE-MIMO detectors. We nd that under the realistic condition where CSI has to be estimated, hence the available channel estimate will be noisy, the MCMC-MIMO detector outperforms the LMMSE-MIMO detector with a signi cant margin