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

From the conventional MIMO to massive MIMO systems: performance analysis and energy efficiency optimization

The main topic of this thesis is based on multiple-input multiple-output (MIMO) wireless communications, which is a novel technology that has attracted great interest in the last twenty years. Conventional MIMO systems using up to eight antennas play a vital role in the urban cellular network, where the deployment of multiple antennas have significantly enhanced the throughput without taking extra spectrum or power resources. The massive MIMO systems “scales” up the benefits that offered by the conventional MIMO systems. Using sixty four or more antennas at the BS not only improves the spectrum efficiency significantly, but also provides additional link robustness. It is considered as a key technology in the fifth generation of mobile communication technology standards network, and the design of new algorithms for these two systems is the basis of the research in this thesis. Firstly, at the receiver side of the conventional MIMO systems, a general framework of bit error rate (BER) approximation for the detection algorithms is proposed, which aims to support an adaptive modulation scheme. The main idea is to utilize a simplified BER approximation scheme, which is based on the union bound of the maximum-likelihood detector (MLD), whereby the bit error rate (BER) performance of the detector for the varying channel qualities can be efficiently predicted. The K-best detector is utilized in the thesis because its quasi- MLD performance and the parallel computational structure. The simulation results have clearly shown the adaptive K-best algorithm, by applying the simplified approximation method, has much reduced computational complexity while still maintaining a promising BER performance. Secondly, in terms of the uplink channel estimation for the massive MIMO systems with the time-division-duplex operation, the performance of the Grassmannian line packing (GLP) based uplink pilot codebook design is investigated. It aims to eliminate the pilot contamination effect in order to increase the downlink achievable rate. In the case of a limited channel coherence interval, the uplink codebook design can be treated as a line packing problem in a Grassmannian manifold. The closed-form analytical expressions of downlink achievable rate for both the single-cell and multi-cell systems are proposed, which are intended for performance analysis and optimization. The numerical results validate the proposed analytical expressions and the rate gains by using the GLP-based uplink codebook design. Finally, the study is extended to the energy efficiency (EE) of the massive MIMO system, as the reduction carbon emissions from the information and communication technology is a long-term target for the researchers. An effective framework of maximizing the EE for the massive MIMO systems is proposed in this thesis. The optimization starts from the maximization of the minimum user rate, which is aiming to increase the quality-of-service and provide a feasible constraint for the EE maximization problem. Secondly, the EE problem is a non-concave problem and can not be solved directly, so the combination of fractional programming and the successive concave approximation based algorithm are proposed to find a good suboptimal solution. It has been shown that the proposed optimization algorithm provides a significant EE improvement compared to a baseline case

Intelligent and Efficient Ultra-Dense Heterogeneous Networks for 5G and Beyond

Ultra-dense heterogeneous network (HetNet), in which densified small cells overlaying the conventional macro-cells, is a promising technique for the fifth-generation (5G) mobile network. The dense and multi-tier network architecture is able to support the extensive data traffic and diverse quality of service (QoS) but meanwhile arises several challenges especially on the interference coordination and resource management. In this thesis, three novel network schemes are proposed to achieve intelligent and efficient operation based on the deep learning-enabled network awareness. Both optimization and deep learning methods are developed to achieve intelligent and efficient resource allocation in these proposed network schemes. To improve the cost and energy efficiency of ultra-dense HetNets, a hotspot prediction based virtual small cell (VSC) network is proposed. A VSC is formed only when the traffic volume and user density are extremely high. We leverage the feature extraction capabilities of deep learning techniques and exploit a long-short term memory (LSTM) neural network to predict potential hotspots and form VSC. Large-scale antenna array enabled hybrid beamforming is also adaptively adjusted for highly directional transmission to cover these VSCs. Within each VSC, one user equipment (UE) is selected as a cell head (CH), which collects the intra-cell traffic using the unlicensed band and relays the aggregated traffic to the macro-cell base station (MBS) in the licensed band. The inter-cell interference can thus be reduced, and the spectrum efficiency can be improved. Numerical results show that proposed VSCs can reduce $55\%$ power consumption in comparison with traditional small cells. In addition to the smart VSCs deployment, a novel multi-dimensional intelligent multiple access (MD-IMA) scheme is also proposed to achieve stringent and diverse QoS of emerging 5G applications with disparate resource constraints. Multiple access (MA) schemes in multi-dimensional resources are adaptively scheduled to accommodate dynamic QoS requirements and network states. The MD-IMA learns the integrated-quality-of-system-experience (I-QoSE) by monitoring and predicting QoS through the LSTM neural network. The resource allocation in the MD-IMA scheme is formulated as an optimization problem to maximize the I-QoSE as well as minimize the non-orthogonality (NO) in view of implementation constraints. In order to solve this problem, both model-based optimization algorithms and model-free deep reinforcement learning (DRL) approaches are utilized. Simulation results demonstrate that the achievable I-QoSE gain of MD-IMA over traditional MA is $15\%$ - $18\%$. In the final part of the thesis, a Software-Defined Networking (SDN) enabled 5G-vehicle ad hoc networks (VANET) is designed to support the growing vehicle-generated data traffic. In this integrated architecture, to reduce the signaling overhead, vehicles are clustered under the coordination of SDN and one vehicle in each cluster is selected as a gateway to aggregate intra-cluster traffic. To ensure the capacity of the trunk-link between the gateway and macro base station, a Non-orthogonal Multiplexed Modulation (NOMM) scheme is proposed to split aggregated data stream into multi-layers and use sparse spreading code to partially superpose the modulated symbols on several resource blocks. The simulation results show that the energy efficiency performance of proposed NOMM is around 1.5-2 times than that of the typical orthogonal transmission scheme

Combined Time, Frecuency and Space Diversity in Multimedia Mobile Broadcasting Systems

El uso combinado de diversidad en el dominio temporal, frecuencial y espacial constituye una valiosa herramienta para mejorar la recepción de servicios de difusión móviles. Gracias a la mejora conseguida por las técnicas de diversidad es posible extender la cobertura de los servicios móviles además de reducir la infraestructura de red. La presente tesis investiga el uso de técnicas de diversidad para la provisión de servicios móviles en la familia europea de sistemas de difusión terrestres estandarizada por el prpoyecto DVB (Digital Video Broadcasting). Esto incluye la primera y segunda generación de sistemas DVB-T (Terrestrial), DVB-NGH (Handheld), y DVB-T2 (Terrestrial 2nd generation), así como el sistema de siguiente generación DVB-NGH. No obstante, el estudio llevado a cabo en la tesis es genérico y puede aplicarse a futuras evoluciones de estándares como el japonés ISDB-T o el americano ATSC. Las investigaciones realizadas dentro del contexto de DVB-T, DVB-H y DVBT2 tienen como objetivo la transmisión simultánea de servicios fijos y móviles en redes terrestres. Esta Convergencia puede facilitar la introducción de servicios móviles de TB debido a la reutilización de espectro, contenido e infraestructura. De acuerdo a los resultados, la incorporación de entrelazado temporal en la capa física para diversidad temporal, y de single-input multiple-output (SIMO) para diversidad espacial, son esenciales para el rendimiento de sistemas móviles de difusión. A pesar de que las técnicas upper later FEC (UL-FEC) pueden propocionar diversidad temporal en sistemas de primera generación como DVB-T y DVB-H, requieren la transmisión de paridad adicional y no son útiles para la recepción estática. El análisis en t�ñerminos de link budjget revela que las técnicas de diversidad noson suficientes para facilitar la provision de servicios móviles en redes DVB-T y DVB-T2 planificadas para recepción fija. Sin embargo, el uso de diversidad en redes planificadas para recepción portableGozálvez Serrano, D. (2012). Combined Time, Frecuency and Space Diversity in Multimedia Mobile Broadcasting Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16273Palanci

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an Outlook

peer reviewedWhen fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-Accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learning-based localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems

Interference cancellation and Resource Allocation approaches for Device-to-Device Communications

Network assisted Device-to-Device (D2D) communication as an underlay to cellular spectrum has attracted much attention in mobile network standards for local area connectivity as a means to improve the cellular spectrum utilization and to reduce the energy consumption of User Equipments (UEs). The D2D communication uses resources of the underlying mobile network which results in different interference scenarios. These include interference from cellular to D2D link, D2D to cellular link and interference among D2D links when multiple D2D links share common resources. In this thesis, an orthogonal precoding interference cancellation method is initially presented to reduce the cellular to D2D and D2D to cellular interferences when the cellular channel resources are being shared by a single D2D link. Three different scenarios have been considered when establishing a D2D communication along with a Base Station-to-UE communication. The proposed method is analytically evaluated in comparison with the conventional precoding matrix allocation method in terms of ergodic capacity. This method is then extended for a cluster based multi-link D2D scenario where interference between D2D pairs also exists in addition to the other two interference scenarios. In this work, cluster denotes a group of devices locally communicating through multi-link D2D communications sharing the same radio resources of the Cluster Head. Performance of the proposed method is evaluated and compared for different resource sharing modes. The analyses illustrate the importance of cluster head in each cluster to save the battery life of devices in that cluster. The outage probability is considered as a performance evaluation matrix for guaranteeing QoS constrain of communication links. Hence, the mathematical expressions for outage probability of the proposed method for single-link and multi-link D2D communications are presented and compared with an existing interference cancellation technique. To execute the cluster based interference cancellation approach, a three-step resource allocation scheme is then proposed. It first performs a mode selection procedure to choose the transmission mode of each UEs. Then a clustering scheme is developed to group the links that can share a common resource to improve the spectral efficiency. For the selection of suitable cellular UEs for each cluster whose resource can be shared, a cluster head selection algorithm is also developed. Maximal residual energy and minimal transmit power have been considered as parameters for the cluster head selection scheme. Finally, the expression for maximum number of links that the radio resource of shared UE can support is analytically derived. The performance of the proposed scheme is evaluated using a WINNER II A1 indoor office model. The performance of D2D communication practically gets limited due to large distance and/or poor channel conditions between the D2D transmitter and receiver. To overcome these issues, a relay-assisted D2D communication is introduced in this thesis where a device relaying is an additional transmission mode along with the existing cellular and D2D transmission modes. A transmission mode assignment algorithm based on the Hungarian algorithm is then proposed to improve the overall system throughput. The proposed algorithm tries to solve two problems: a suitable transmission mode selection for each scheduled transmissions and a device selection for relaying communication between user equipments in the relay transmission mode. Simulation results showed that our proposed algorithm improves the system performance in terms of the overall system throughput and D2D data rate in comparison with traditional D2D communication schemes

Proceedings of the Mobile Satellite Conference

A satellite-based mobile communications system provides voice and data communications to mobile users over a vast geographic area. The technical and service characteristics of mobile satellite systems (MSSs) are presented and form an in-depth view of the current MSS status at the system and subsystem levels. Major emphasis is placed on developments, current and future, in the following critical MSS technology areas: vehicle antennas, networking, modulation and coding, speech compression, channel characterization, space segment technology and MSS experiments. Also, the mobile satellite communications needs of government agencies are addressed, as is the MSS potential to fulfill them

SIMULATING SEISMIC WAVE PROPAGATION IN TWO-DIMENSIONAL MEDIA USING DISCONTINUOUS SPECTRAL ELEMENT METHODS

We introduce a discontinuous spectral element method for simulating seismic wave in 2- dimensional elastic media. The methods combine the flexibility of a discontinuous finite element method with the accuracy of a spectral method. The elastodynamic equations are discretized using high-degree of Lagrange interpolants and integration over an element is accomplished based upon the Gauss-Lobatto-Legendre integration rule. This combination of discretization and integration results in a diagonal mass matrix and the use of discontinuous finite element method makes the calculation can be done locally in each element. Thus, the algorithm is simplified drastically. We validated the results of one-dimensional problem by comparing them with finite-difference time-domain method and exact solution. The comparisons show excellent agreement