FPGA based Uniform Channelizer Implementation

Channelizers are widely used in modern digital communication systems. Advanced uniform multirate channelization have been theoretically proved to be capable of reducing the computational load, with a better performance. Therefore, in this thesis, we implement these designs on a FPGA board for the sake of the comprehensive evaluation of resource usage, performance and frequency response. The uniform filter-banks are one of the most essential unit in channelization. The Generalised Discrete Fourier Transform Modulated Filter Bank (GDFT-FB), as an important variant of basic a DFT-FB, has been implemented in FPGA and demonstrated with a better computational saving rather than traditional schemes. Moreover the oversampling version is demonstrated to have a better frequency response with an acceptable amount of extra resources. On the other hand, frequency response masking (FRM) techniques is able to reduce the number of coefficients. Therefore, the full FRM GDFT-FB and alternative narrowband FRM GDFT-FB are both implemented in FPGA platform, in order to achieve a better performance and hardware efficiency

Channelization for Multi-Standard Software-Defined Radio Base Stations

As the number of radio standards increase and spectrum resources come under more pressure, it becomes ever less efficient to reserve bands of spectrum for exclusive use by a single radio standard. Therefore, this work focuses on channelization structures compatible with spectrum sharing among multiple wireless standards and dynamic spectrum allocation in particular. A channelizer extracts independent communication channels from a wideband signal, and is one of the most computationally expensive components in a communications receiver. This work specifically focuses on non-uniform channelizers suitable for multi-standard Software-Defined Radio (SDR) base stations in general and public mobile radio base stations in particular. A comprehensive evaluation of non-uniform channelizers (existing and developed during the course of this work) shows that parallel and recombined variants of the Generalised Discrete Fourier Transform Modulated Filter Bank (GDFT-FB) represent the best trade-off between computational load and flexibility for dynamic spectrum allocation. Nevertheless, for base station applications (with many channels) very high filter orders may be required, making the channelizers difficult to physically implement. To mitigate this problem, multi-stage filtering techniques are applied to the GDFT-FB. It is shown that these multi-stage designs can significantly reduce the filter orders and number of operations required by the GDFT-FB. An alternative approach, applying frequency response masking techniques to the GDFT-FB prototype filter design, leads to even bigger reductions in the number of coefficients, but computational load is only reduced for oversampled configurations and then not as much as for the multi-stage designs. Both techniques render the implementation of GDFT-FB based non-uniform channelizers more practical. Finally, channelization solutions for some real-world spectrum sharing use cases are developed before some final physical implementation issues are considered

Efficient channelization on a Graphics Processing Unit

We present an implementation of a channelizer (F-engine) running on a Graphics Processing Unit (GPU). While not the first GPU implementation of a channelizer, we have put significant effort into optimizing the implementation. We are able to process four antennas each with 2 Gsample/s, 10-bit dual-polarized input and 8-bit output, on a single commodity GPU. This fully utilizes the available PCIe bandwidth of the GPU. The system is not as optimized for a single high-bandwidth antenna, but handles 6.2 Gsample/s, limited by single-core CPU performance.Comment: Submitted to The Journal of Astronomical Telescopes, Instruments, and System

BASEBAND RADIO MODEM DESIGN USING GRAPHICS PROCESSING UNITS

A modern radio or wireless communications transceiver is programmed via software and firmware to change its functionalities at the baseband. However, the actual implementation of the radio circuits relies on dedicated hardware, and the design and implementation of such devices are time consuming and challenging. Due to the need for real-time operation, dedicated hardware is preferred in order to meet stringent requirements on throughput and latency. With increasing need for higher throughput and shorter latency, while supporting increasing bandwidth across a fragmented spectrum, dedicated subsystems are developed in order to service individual frequency bands and specifications. Such a dedicated-hardware-intensive approach leads to high resource costs, including costs due to multiple instantiations of mixers, filters, and samplers. Such increases in hardware requirements in turn increases device size, power consumption, weight, and financial cost. If it can meet the required real-time constraints, a more flexible and reconfigurable design approach, such as a software-based solution, is often more desirable over a dedicated hardware solution. However, significant challenges must be overcome in order to meet constraints on throughput and latency while servicing different frequency bands and bandwidths. Graphics processing unit (GPU) technology provides a promising class of platforms for addressing these challenges. GPUs, which were originally designed for rendering images and video sequences, have been adapted as general purpose high-throughput computation engines for a wide variety of application areas beyond their original target domains. Linear algebra and signal processing acceleration are examples of such application areas. In this thesis, we apply GPUs as software-based, baseband radios and demonstrate novel, software-based implementations of key subsystems in modern wireless transceivers. In our work, we develop novel implementation techniques that allow communication system designers to use GPUs as accelerators for baseband processing functions, including real-time filtering and signal transformations. More specifically, we apply GPUs to accelerate several computationally-intensive, frontend radio subsystems, including filtering, signal mixing, sample rate conversion, and synchronization. These are critical subsystems that must operate in real-time to reliably receive waveforms. The contributions of this thesis can be broadly organized into 3 major areas: (1) channelization, (2) arbitrary resampling, and (3) synchronization. 1. Channelization: a wideband signal is shared between different users and channels, and a channelizer is used to separate the components of the shared signal in the different channels. A channelizer is often used as a pre-processing step in selecting a specific channel-of-interest. A typical channelization process involves signal conversion, resampling, and filtering to reject adjacent channels. We investigate GPU acceleration for a particularly efficient form of channelizer called a polyphase filterbank channelizer, and demonstrate a real-time implementation of our novel channelizer design. 2. Arbitrary resampling: following a channelization process, a signal is often resampled to at least twice the data rate in order to further condition the signal. Since different communication standards require different resampling ratios, it is desirable for a resampling subsystem to support a variety of different ratios. We investigate optimized, GPU-based methods for resampling using polyphase filter structures that are mapped efficiently into GPU hardware. We investigate these GPU implementation techniques in the context of interpolation (integer-factor increases in sampling rate), decimation (integer-factor decreases in sampling rate), and rational resampling. Finally, we demonstrate an efficient implementation of arbitrary resampling using GPUs. This implementation exploits specialized hardware units within the GPU to enable efficient and accurate resampling processes involving arbitrary changes in sample rate. 3. Synchronization: incoming signals in a wireless communications transceiver must be synchronized in order to recover the transmitted data properly from complex channel effects such as thermal noise, fading, and multipath propagation. We investigate timing recovery in GPUs to accelerate the most computationally intensive part of the synchronization process, and correctly align the incoming data symbols in the receiver. Furthermore, we implement fully-parallel timing error detection to accelerate maximum likelihood estimation

Energy-efficient hardware architecture and vlsi implementation of a polyphase channelizer with applications to subband adaptive filtering

Abstract Polyphase channelizer is an important component of subband adaptive filtering systems. This paper presents an energy-efficient hardware architecture and VLSI implementation of polyphase channelizer, integrating algorithmic, architectural and circuit level design techniques. At algorithm level, low complexity polyphase channelizer architecture is derived using multirate signal processing approach. To reduce the computational complexity in polyphase filters, computation sharing differential coefficient (CSDC) method is effectively used as an architectural level technique. The main idea of CSDC is to combine the strength of augmented differential coefficient method and subexpression sharing. Efficient circuitlevel techniques: low power commutator implementation, dual-VDD scheme and novel level-converting flip-flop (LCFF), are also used to further reduce the power dissipation. The proposed polyphase channelizer consumes 352 mW power with throughput of 480 million samples per second (MSPS). A test chip has been fabricated in 0.18 μm CMOS technology and its functionality is verified. Chip measurement results show that the dual-VDD implementation achieves a total power saving of 2.7 X

Runtime Adaptation in Embedded Computing Systems using Markov Decision Processes

During the design and implementation of embedded computing systems (ECSs), engineers must make assumptions on how the system will be used after being built and deployed. Traditionally, these important decisions were made at design time for a fleet of ECSs prior to deployment. In contrast to this approach, this research explores and develops techniques to enable adaptation of ECSs at runtime to the environments and applications in which they operate. Adaptation is enabled such that the usage assumptions and performance optimization decisions can be made autonomously at runtime in the deployed system. This thesis utilizes Markov Decision Processes (MDPs), a powerful and well established mathematical framework used for decision making under uncertainty, to control computing systems at runtime. The resulting control is performed in ways that are more dynamic, robust and adaptable than alternatives in many scenarios. The techniques developed in this thesis are first applied to a reconfigurable embedded digital signal processing system. In this effort, several challenges are encountered and resolved using novel approaches. Through extensive simulations and a prototype implementation, the robustness of the adaptation is demonstrated in comparison with the prior state-of-the-art. The thesis continues by developing an efficient algorithm for conversion of MDP models to actionable control policies - a required step known as solving the MDP. The solver algorithm is developed in the context of ECSs that contain general purpose embedded GPUs (graphics processing units). The novel solver algorithm, Sparse Parallel Value Iteration (SPVI), makes use of the parallel processing capabilities provided by such GPUs, and also exploits the sparsity that typically exists in MDPs when used to model and control ECSs. To extend the applicability of the runtime adaptation techniques to smaller and more strictly resource constrained ECSs, another solver - Sparse Value Iteration (SVI) is developed for use on microcontrollers. The method is explored in a detailed case study involving a cellular (LTE-M) connected sensor that adapts to varying communications profiles. The case study reveals that the proposed adaptation framework outperforms a competing approach based on Reinforcement Learning (RL) in terms of robustness and adaptation, while consuming comparable resource requirements. Finally, the thesis concludes by analyzing the various logistical challenges that exist when deploying MDPs on ECSs. In response to these challenges, the thesis contributes an open source software package to the engineering community. The package contains libraries of MDP solvers, parsers, datasets and reference solutions, which provide a comprehensive infrastructure for exploring the trade-offs among existing embedded MDP techniques, and experimenting with novel approaches

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system