A compressed sensing approach to block-iterative equalization: connections and applications to radar imaging reconstruction

The widespread of underdetermined systems has brought forth a variety of new algorithmic solutions, which capitalize on the Compressed Sensing (CS) of sparse data. While well known greedy or iterative threshold type of CS recursions take the form of an adaptive filter followed by a proximal operator, this is no different in spirit from the role of block iterative decision-feedback equalizers (BI-DFE), where structure is roughly exploited by the signal constellation slicer. By taking advantage of the intrinsic sparsity of signal modulations in a communications scenario, the concept of interblock interference (IBI) can be approached more cunningly in light of CS concepts, whereby the optimal feedback of detected symbols is devised adaptively. The new DFE takes the form of a more efficient re-estimation scheme, proposed under recursive-least-squares based adaptations. Whenever suitable, these recursions are derived under a reduced-complexity, widely-linear formulation, which further reduces the minimum-mean-square-error (MMSE) in comparison with traditional strictly-linear approaches. Besides maximizing system throughput, the new algorithms exhibit significantly higher performance when compared to existing methods. Our reasoning will also show that a properly formulated BI-DFE turns out to be a powerful CS algorithm itself. A new algorithm, referred to as CS-Block DFE (CS-BDFE) exhibits improved convergence and detection when compared to first order methods, thus outperforming the state-of-the-art Complex Approximate Message Passing (CAMP) recursions. The merits of the new recursions are illustrated under a novel 3D MIMO Radar formulation, where the CAMP algorithm is shown to fail with respect to important performance measures.A proliferação de sistemas sub-determinados trouxe a tona uma gama de novas soluções algorítmicas, baseadas no sensoriamento compressivo (CS) de dados esparsos. As recursões do tipo greedy e de limitação iterativa para CS se apresentam comumente como um filtro adaptativo seguido de um operador proximal, não muito diferente dos equalizadores de realimentação de decisão iterativos em blocos (BI-DFE), em que um decisor explora a estrutura do sinal de constelação. A partir da esparsidade intrínseca presente na modulação de sinais no contexto de comunicações, a interferência entre blocos (IBI) pode ser abordada utilizando-se o conceito de CS, onde a realimentação ótima de símbolos detectados é realizada de forma adaptativa. O novo DFE se apresenta como um esquema mais eficiente de reestimação, baseado na atualização por mínimos quadrados recursivos (RLS). Sempre que possível estas recursões são propostas via formulação linear no sentido amplo, o que reduz ainda mais o erro médio quadrático mínimo (MMSE) em comparação com abordagens tradicionais. Além de maximizar a taxa de transferência de informação, o novo algoritmo exibe um desempenho significativamente superior quando comparado aos métodos existentes. Também mostraremos que um equalizador BI-DFE formulado adequadamente se torna um poderoso algoritmo de CS. O novo algoritmo CS-BDFE apresenta convergência e detecção aprimoradas, quando comparado a métodos de primeira ordem, superando as recursões de Passagem de Mensagem Aproximada para Complexos (CAMP). Os méritos das novas recursões são ilustrados através de um modelo tridimensional para radares MIMO recentemente proposto, onde o algoritmo CAMP falha em aspectos importantes de medidas de desempenho

On robust and secure wireless communication system design using software-defined radios

This dissertation is composed of three parts: airborne multi input multi output (MIMO) communications, physical layer authentication, and software radio design for DARPA Spectrum Challenge. A common theme for the three distinct problems is the system perspective that we have adopted throughout this dissertation. Instead of considering isolated issues within these problems, we have provided a holistic design approach to the three problems and have implemented all three systems using the GNU Radio/USRP (Universal Software Radio Peripheral) platform. In the first part, we develop a MIMO communication system for airborne platforms. MIMO communication has long been considered to be suitable only for environment that is rich in scatterers. This, unfortunately is not the case for airborne platforms. However, this lack of scattering can be compensated by the large aperture of the airborne MIMO platform; this is corroborated by our careful analysis using real measurement data. Our analysis of the airborne MIMO channels leads to the development of a variable rate MIMO transceiver architecture. This architecture is numerically shown to improve the bit error rate (BER) over conventional transceiver architectures that are developed for rich scattering environments. A software radio based MIMO system is then implemented to demonstrate experimentally the efficacy of the developed architecture. In the second part, we develop a physical layer authentication scheme as a counter measure to primary user emulation attack (PUEA) in cognitive radio (CR) networks. In this attack, a malicious user emulates the signal characteristics of the primary user (PU) when it is silent which prevents unsuspecting secondary user (SU) from utilizing the network. The developed physical layer authentication is based on embedding cryptographic hash signatures, referred to as authentication tags, within PU\u27s signal constellations. The embedding is performed such that the legacy receivers are not affected. We analyze the scheme using the fast fading Rayleigh channel model and present an optimal scheme to embed signals in PU\u27s constellations which minimizes the tag BER. Experimental results are obtained that corroborate our theoretical claims, thereby establish that reliable authentication can be achieved without sacrificing signal quality at the primary receivers. In the final part, we describe in detail our design of software radios developed as part of the DARPA Spectrum Challenge (DSC), a year long competition that started in January 2013 and concluded in March 2014 with the final tournament held in Arlington, VA at the DARPA headquarter. DSC was comprised of two tournaments, competitive and cooperative. In the competitive mode two radio pairs, each composed of a transmitter and a receiver, are pitted against each other to transmit the most amount of data error-free while operating concurrently in the same frequency band. In the cooperative mode, three radio pairs have to share a frequency band in a cooperative manner wherein the goal is to maximize the throughput of all the three pairs. We describe the design of our software radio system that integrates some key technologies crucial in operating in an environment that does not allow user coordination and spectrum pre-planning, including: spectrum sensing, adaptive transmission both in spectrum utilization and transmission rate, opportunistic jamming, and sliding window feedback. The developed radio is robust in the presence of unknown interference and achieves the desired balance between throughput and reliability in an uncoordinated transmission environment

System capacity enhancement for 5G network and beyond

A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Doctor of PhilosophyThe demand for wireless digital data is dramatically increasing year over year. Wireless communication systems like Laptops, Smart phones, Tablets, Smart watch, Virtual Reality devices and so on are becoming an important part of people’s daily life. The number of mobile devices is increasing at a very fast speed as well as the requirements for mobile devices such as super high-resolution image/video, fast download speed, very short latency and high reliability, which raise challenges to the existing wireless communication networks. Unlike the previous four generation communication networks, the fifth-generation (5G) wireless communication network includes many technologies such as millimetre-wave communication, massive multiple-input multiple-output (MIMO), visual light communication (VLC), heterogeneous network (HetNet) and so forth. Although 5G has not been standardised yet, these above technologies have been studied in both academia and industry and the goal of the research is to enhance and improve the system capacity for 5G networks and beyond by studying some key problems and providing some effective solutions existing in the above technologies from system implementation and hardware impairments’ perspective. The key problems studied in this thesis include interference cancellation in HetNet, impairments calibration for massive MIMO, channel state estimation for VLC, and low latency parallel Turbo decoding technique. Firstly, inter-cell interference in HetNet is studied and a cell specific reference signal (CRS) interference cancellation method is proposed to mitigate the performance degrade in enhanced inter-cell interference coordination (eICIC). This method takes carrier frequency offset (CFO) and timing offset (TO) of the user’s received signal into account. By reconstructing the interfering signal and cancelling it afterwards, the capacity of HetNet is enhanced. Secondly, for massive MIMO systems, the radio frequency (RF) impairments of the hardware will degrade the beamforming performance. When operated in time duplex division (TDD) mode, a massive MIMO system relies on the reciprocity of the channel which can be broken by the transmitter and receiver RF impairments. Impairments calibration has been studied and a closed-loop reciprocity calibration method is proposed in this thesis. A test device (TD) is introduced in this calibration method that can estimate the transmitters’ impairments over-the-air and feed the results back to the base station via the Internet. The uplink pilots sent by the TD can assist the BS receivers’ impairment estimation. With both the uplink and downlink impairments estimates, the reciprocity calibration coefficients can be obtained. By computer simulation and lab experiment, the performance of the proposed method is evaluated. Channel coding is an essential part of a wireless communication system which helps fight with noise and get correct information delivery. Turbo codes is one of the most reliable codes that has been used in many standards such as WiMAX and LTE. However, the decoding process of turbo codes is time-consuming and the decoding latency should be improved to meet the requirement of the future network. A reverse interleave address generator is proposed that can reduce the decoding time and a low latency parallel turbo decoder has been implemented on a FPGA platform. The simulation and experiment results prove the effectiveness of the address generator and show that there is a trade-off between latency and throughput with a limited hardware resource. Apart from the above contributions, this thesis also investigated multi-user precoding for MIMO VLC systems. As a green and secure technology, VLC is achieving more and more attention and could become a part of 5G network especially for indoor communication. For indoor scenario, the MIMO VLC channel could be easily ill-conditioned. Hence, it is important to study the impact of the channel state to the precoding performance. A channel state estimation method is proposed based on the signal to interference noise ratio (SINR) of the users’ received signal. Simulation results show that it can enhance the capacity of the indoor MIMO VLC system

Multi-function RF for Situational Awareness

Radio frequency (RF) communications are an integral part of many situational awareness applications. Sensing data need to be processed in a timely manner, making it imperative to have a robust and reliable RF link for information dissemination. Moreover, there is an increasing need for exploiting RF communication signals directly for sensing, leading to the notion of multi-function RF. In the first part of this dissertation, we investigate the development of a robust Multiple-Input Multiple-Output (MIMO) communication system suitable for airborne platforms.Three majors challenges in realizing MIMO capacity gain in airborne environment are addressed: 1) antenna blockage due largely to the orientation of the antenna array; 2) the presence of unknown interference inherent to the intended application; 3) the lack of channel state information (CSI) at the transmitter. Built on the Diagonal Bell-Labs Layered Space-Time (D-BLAST) MIMO architecture, the system integrates three key design approaches: spatial spreading to counter antenna blockage; temporal spreading to mitigate signal to interference and noise ratio degradation due to intended or unintended interference; and a simple low rate feedback scheme to enable real time adaptation in the absence of full transmitter CSI. Extensive experiment studies using a fully functioning $4\times 4$ MIMO system validate the developed system. In the second part, ambient RF signals are exploited to extract situational awareness information directly. Using WiFi signals as an example, we demonstrate that the CSI obtained at the receiver contains rich information about the propagation environment. Two distinct learning systems are developed for occupancy detection using passive WiFi sensing. The first one is based on deep learning where a parallel convolutional neural network (CNN) architecture is designed to extract useful information from both magnitude and phase of the CSI. Pre-processing steps are carefully designed to preserve human motion induced channel variation while insulating against other impairments and post-processing is applied after CNN to infer presence information for instantaneous motion outputs. To alleviate the need of tedious training efforts involved in deep learning based system, a novel learning problem with contaminated sampling is formulated. This leads to a second learning system: a two-stage solution for motion detection using support vector machines (SVM). A one-class SVM model is first evaluated whose training data are from human free environment only. Decontamination of human presence data using the one-class SVM is done prior to motion detection through a two-class support vector classifier. Extensive experiments using commercial off-the-shelf WiFi devices are conducted for both systems. The results demonstrate that the learning based RF sensing provides a viable and promising alternative for occupancy detection as they are much more sensitive to human motion than passive infrared sensors which are widely deployed in commercial and residential buildings

Adaptive Baseband Pro cessing and Configurable Hardware for Wireless Communication

The world of information is literally at one’s fingertips, allowing access to previously unimaginable amounts of data, thanks to advances in wireless communication. The growing demand for high speed data has necessitated theuse of wider bandwidths, and wireless technologies such as Multiple-InputMultiple-Output (MIMO) have been adopted to increase spectral efficiency.These advanced communication technologies require sophisticated signal processing, often leading to higher power consumption and reduced battery life.Therefore, increasing energy efficiency of baseband hardware for MIMO signal processing has become extremely vital. High Quality of Service (QoS)requirements invariably lead to a larger number of computations and a higherpower dissipation. However, recognizing the dynamic nature of the wirelesscommunication medium in which only some channel scenarios require complexsignal processing, and that not all situations call for high data rates, allowsthe use of an adaptive channel aware signal processing strategy to provide adesired QoS. Information such as interference conditions, coherence bandwidthand Signal to Noise Ratio (SNR) can be used to reduce algorithmic computations in favorable channels. Hardware circuits which run these algorithmsneed flexibility and easy reconfigurability to switch between multiple designsfor different parameters. These parameters can be used to tune the operations of different components in a receiver based on feedback from the digitalbaseband. This dissertation focuses on the optimization of digital basebandcircuitry of receivers which use feedback to trade power and performance. Aco-optimization approach, where designs are optimized starting from the algorithmic stage through the hardware architectural stage to the final circuitimplementation is adopted to realize energy efficient digital baseband hardwarefor mobile 4G devices. These concepts are also extended to the next generation5G systems where the energy efficiency of the base station is improved.This work includes six papers that examine digital circuits in MIMO wireless receivers. Several key blocks in these receiver include analog circuits thathave residual non-linearities, leading to signal intermodulation and distortion.Paper-I introduces a digital technique to detect such non-linearities and calibrate analog circuits to improve signal quality. The concept of a digital nonlinearity tuning system developed in Paper-I is implemented and demonstratedin hardware. The performance of this implementation is tested with an analogchannel select filter, and results are presented in Paper-II. MIMO systems suchas the ones used in 4G, may employ QR Decomposition (QRD) processors tosimplify the implementation of tree search based signal detectors. However,the small form factor of the mobile device increases spatial correlation, whichis detrimental to signal multiplexing. Consequently, a QRD processor capableof handling high spatial correlation is presented in Paper-III. The algorithm and hardware implementation are optimized for carrier aggregation, which increases requirements on signal processing throughput, leading to higher powerdissipation. Paper-IV presents a method to perform channel-aware processingwith a simple interpolation strategy to adaptively reduce QRD computationcount. Channel properties such as coherence bandwidth and SNR are used toreduce multiplications by 40% to 80%. These concepts are extended to usetime domain correlation properties, and a full QRD processor for 4G systemsfabricated in 28 nm FD-SOI technology is presented in Paper-V. The designis implemented with a configurable architecture and measurements show thatcircuit tuning results in a highly energy efficient processor, requiring 0.2 nJ to1.3 nJ for each QRD. Finally, these adaptive channel-aware signal processingconcepts are examined in the scope of the next generation of communicationsystems. Massive MIMO systems increase spectral efficiency by using a largenumber of antennas at the base station. Consequently, the signal processingat the base station has a high computational count. Paper-VI presents a configurable detection scheme which reduces this complexity by using techniquessuch as selective user detection and interpolation based signal processing. Hardware is optimized for resource sharing, resulting in a highly reconfigurable andenergy efficient uplink signal detector

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

Offset Learning based Channel Estimation for Intelligent Reflecting Surface-Assisted Indoor Communication

The emerging intelligent reflecting surface (IRS) can significantly improve the system capacity, and it has been regarded as a promising technology for the beyond fifth-generation (B5G) communications. For IRS-assisted multiple input multiple output (MIMO) systems, accurate channel estimation is a critical challenge. This severely restricts practical applications, particularly for resource-limited indoor scenario as it contains numerous scatterers and parameters to be estimated, while the number of pilots is limited. Prior art tackles these issues and associated optimization using mathematical-based statistical approaches, but are difficult to solve as the number of scatterers increase. To estimate the indoor channels with an affordable piloting overhead, we propose an offset learning (OL)-based neural network for channel estimation. The proposed OL-based estimator can dynamically trace the channel state information (CSI) without any prior knowledge of the IRS-assisted channel structure as well as indoor statistics. In addition, inspired by the powerful learning capability of convolutional neural network (CNN), CNN-based inversion blocks are developed in the offset estimation module to build the offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor CSI with a lower complexity as compared to the benchmark schemes

Estimation and detection techniques for doubly-selective channels in wireless communications

A fundamental problem in communications is the estimation of the channel. The signal transmitted through a communications channel undergoes distortions so that it is often received in an unrecognizable form at the receiver. The receiver must expend significant signal processing effort in order to be able to decode the transmit signal from this received signal. This signal processing requires knowledge of how the channel distorts the transmit signal, i.e. channel knowledge. To maintain a reliable link, the channel must be estimated and tracked by the receiver. The estimation of the channel at the receiver often proceeds by transmission of a signal called the 'pilot' which is known a priori to the receiver. The receiver forms its estimate of the transmitted signal based on how this known signal is distorted by the channel, i.e. it estimates the channel from the received signal and the pilot. This design of the pilot is a function of the modulation, the type of training and the channel. [Continues.

Convergence of millimeter-wave and photonic interconnect systems for very-high-throughput digital communication applications

In the past, radio-frequency signals were commonly used for low-speed wireless electronic systems, and optical signals were used for multi-gigabit wired communication systems. However, as the emergence of new millimeter-wave technology introduces multi-gigabit transmission over a wireless radio-frequency channel, the borderline between radio-frequency and optical systems becomes blurred. As a result, there come ample opportunities to design and develop next-generation broadband systems to combine the advantages of these two technologies to overcome inherent limitations of various broadband end-to-end interconnect systems in signal generation, recovery, synchronization, and so on. For the transmission distances of a few centimeters to thousands of kilometers, the convergence of radio-frequency electronics and optics to build radio-over-fiber systems ushers in a new era of research for the upcoming very-high-throughput broadband services. Radio-over-fiber systems are believed to be the most promising solution to the backhaul transmission of the millimeter-wave wireless access networks, especially for the license-free, very-high-throughput 60-GHz band. Adopting radio-over-fiber systems in access or in-building networks can greatly extend the 60-GHz signal reach by using ultra-low loss optical fibers. However, such high frequency is difficult to generate in a straightforward way. In this dissertation, the novel techniques of homodyne and heterodyne optical-carrier suppressions for radio-over-fiber systems are investigated and various system architectures are designed to overcome these limitations of 60-GHz wireless access networks, bringing the popularization of multi-gigabit wireless networks to become closer to the reality. In addition to the advantages for the access networks, extremely high spectral efficiency, which is the most important parameter for long-haul networks, can be achieved by radio-over-fiber signal generation. As a result, the transmission performance of spectrally efficient radio-over-fiber signaling, including orthogonal frequency division multiplexing and orthogonal wavelength division multiplexing, is broadly and deeply investigated. On the other hand, radio-over-fiber is also used for the frequency synchronization that can resolve the performance limitation of wireless interconnect systems. A novel wireless interconnects assisted by radio-over-fiber subsystems is proposed in this dissertation. In conclusion, multiple advantageous facets of radio-over-fiber systems can be found in various levels of end-to-end interconnect systems. The rapid development of radio-over-fiber systems will quickly change the conventional appearance of modern communications.PhDCommittee Chair: Gee-Kung Chang; Committee Member: Bernard Kippelen; Committee Member: Shyh-Chiang Shen; Committee Member: Thomas K. Gaylord; Committee Member: Umakishore Ramachandra