Cyclostationary Algorithm for Signal Analysis in Cognitive 4G Networks with Spectral Sensing and Resource Allocation

Cognitive Radio (CR) effectively involved in the management of spectrum to perform improved data transmission. CR system actively engaged in the data sensing, learning and dynamic adjustment of radio spectrum parameters with management of unused spectrum in the signal. The spectrum sensing is indispensable in the CR for the management of Primary Users (PUs) and Secondary users (SUs) without any interference. Spectrum sensing is considered as the effective adaptive signal processing model to evaluate the computational complexity model for the signal transmission through Matched filtering, Waveform and Cyclostationary based Energy sensing model. Cyclostationary based model is effective for the energy based sensing model based on unique characteristics with estimation of available channel in the spectrum to extract the received signal in the PU signal. Cyclostationary based model uses the spectrum availability without any periodic property to extract the noise features. This paper developed a Adaptive Cross Score Cyclostationary (ACSCS) to evaluate the spectrum sensing in the CR network. The developed ACSCS model uses the computational complexity with estimation of Signal-to-Interference-and-Noise Ratio (SINR) elimination of cost function. ACSCS model uses the Adaptive Least square Spectral Self-Coherence Restoral (SCORE) with the Adaptive Cross Score (ACS) to overcome the issues in CR. With the derived ACSCS algorithm minimizes the computational complexity based on cost function compared with the ACS algorithm. To minimize the computational complexity pipeline triangular array based Gram-Schmidt Orthogonalization (GSO) structure for the optimization of network. The simulation performance analysis with the ACSCS scheme uses the Rician Multipath Fading channel to estimate detection probability to sense the Receiver Operating Characteristics, detection probability and probability of false alarm using Maximum Likelihood (ML) detector. The ACSC model uses the Square-law combining (SLC) with the moment generation function in the multipath fading channel for the channel sensing with reduced computational complexity. The simulation analysis expressed that ACSC scheme achieves the maximal detection probability value of 1. The analysis expressed that proposed ACSC scheme achieves the improved channel estimation in the 4G communication environment

On detection of OFDM signals for cognitive radio applications

As the requirement for wireless telecommunications services continues to grow, it has become increasingly important to ensure that the Radio Frequency (RF) spectrum is managed efficiently. As a result of the current spectrum allocation policy, it has been found that portions of RF spectrum belonging to licensed users are often severely underutilised, at particular times and geographical locations. Awareness of this problem has led to the development of Dynamic Spectrum Access (DSA) and Cognitive Radio (CR) as possible solutions. In one variation of the shared-use model for DSA, it is proposed that the inefficient use of licensed spectrum could be overcome by enabling unlicensed users to opportunistically access the spectrum when the licensed user is not transmitting. In order for an unlicensed device to make decisions, it must be aware of its own RF environment and, therefore, it has been proposed that DSA could been abled using CR. One approach that has be identified to allow the CR to gain information about its operating environment is spectrum sensing. An interesting solution that has been identified for spectrum sensing is cyclostationary detection. This property refers to the inherent periodic nature of the second order statistics of many communications signals. One of the most common modulation formats in use today is Orthogonal Frequency Division Multiplexing (OFDM), which exhibits cyclostationarity due to the addition of a Cyclic Prefix (CP). This thesis examines several statistical tests for cyclostationarity in OFDM signals that may be used for spectrum sensing in DSA and CR. In particular, focus is placed on statistical tests that rely on estimation of the Cyclic Autocorrelation Function (CAF). Based on splitting the CAF into two complex component functions, several new statistical tests are introduced and are shown to lead to an improvement in detection performance when compared to the existing algorithms. The performance of each new algorithm is assessed in Additive White Gaussian Noise (AWGN), impulsive noise and when subjected to impairments such as multipath fading and Carrier Frequency Offset (CFO). Finally, each algorithm is targeted for Field Programmable Gate Array (FPGA) implementation using a Xilinx 7 series device. In order to keep resource costs to a minimum, it is suggested that the new algorithms are implemented on the FPGA using hardware sharing, and a simple mathematical re-arrangement of certain tests statistics is proposed to circumvent a costly division operation.As the requirement for wireless telecommunications services continues to grow, it has become increasingly important to ensure that the Radio Frequency (RF) spectrum is managed efficiently. As a result of the current spectrum allocation policy, it has been found that portions of RF spectrum belonging to licensed users are often severely underutilised, at particular times and geographical locations. Awareness of this problem has led to the development of Dynamic Spectrum Access (DSA) and Cognitive Radio (CR) as possible solutions. In one variation of the shared-use model for DSA, it is proposed that the inefficient use of licensed spectrum could be overcome by enabling unlicensed users to opportunistically access the spectrum when the licensed user is not transmitting. In order for an unlicensed device to make decisions, it must be aware of its own RF environment and, therefore, it has been proposed that DSA could been abled using CR. One approach that has be identified to allow the CR to gain information about its operating environment is spectrum sensing. An interesting solution that has been identified for spectrum sensing is cyclostationary detection. This property refers to the inherent periodic nature of the second order statistics of many communications signals. One of the most common modulation formats in use today is Orthogonal Frequency Division Multiplexing (OFDM), which exhibits cyclostationarity due to the addition of a Cyclic Prefix (CP). This thesis examines several statistical tests for cyclostationarity in OFDM signals that may be used for spectrum sensing in DSA and CR. In particular, focus is placed on statistical tests that rely on estimation of the Cyclic Autocorrelation Function (CAF). Based on splitting the CAF into two complex component functions, several new statistical tests are introduced and are shown to lead to an improvement in detection performance when compared to the existing algorithms. The performance of each new algorithm is assessed in Additive White Gaussian Noise (AWGN), impulsive noise and when subjected to impairments such as multipath fading and Carrier Frequency Offset (CFO). Finally, each algorithm is targeted for Field Programmable Gate Array (FPGA) implementation using a Xilinx 7 series device. In order to keep resource costs to a minimum, it is suggested that the new algorithms are implemented on the FPGA using hardware sharing, and a simple mathematical re-arrangement of certain tests statistics is proposed to circumvent a costly division operation

Spectrum Optimisation in Wireless Communication Systems: Technology Evaluation, System Design and Practical Implementation

Two key technology enablers for next generation networks are examined in this thesis, namely Cognitive Radio (CR) and Spectrally Efficient Frequency Division Multiplexing (SEFDM). The first part proposes the use of traffic prediction in CR systems to improve the Quality of Service (QoS) for CR users. A framework is presented which allows CR users to capture a frequency slot in an idle licensed channel occupied by primary users. This is achieved by using CR to sense and select target spectrum bands combined with traffic prediction to determine the optimum channel-sensing order. The latter part of this thesis considers the design, practical implementation and performance evaluation of SEFDM. The key challenge that arises in SEFDM is the self-created interference which complicates the design of receiver architectures. Previous work has focused on the development of sophisticated detection algorithms, however, these suffer from an impractical computational complexity. Consequently, the aim of this work is two-fold; first, to reduce the complexity of existing algorithms to make them better-suited for application in the real world; second, to develop hardware prototypes to assess the feasibility of employing SEFDM in practical systems. The impact of oversampling and fixed-point effects on the performance of SEFDM is initially determined, followed by the design and implementation of linear detection techniques using Field Programmable Gate Arrays (FPGAs). The performance of these FPGA based linear receivers is evaluated in terms of throughput, resource utilisation and Bit Error Rate (BER). Finally, variants of the Sphere Decoding (SD) algorithm are investigated to ameliorate the error performance of SEFDM systems with targeted reduction in complexity. The Fixed SD (FSD) algorithm is implemented on a Digital Signal Processor (DSP) to measure its computational complexity. Modified sorting and decomposition strategies are then applied to this FSD algorithm offering trade-offs between execution speed and BER

Design of Intellectual Property-Based Hardware Blocks Integrable with Embedded RISC Processors

The main focus of this thesis is to research methods, architecture, and implementation of hardware acceleration for a Reduced Instruction Set Computer (RISC) platform. The target platform is a single-core general-purpose embedded processor (the COFFEE core) which was developed by our group at Tampere University of Technology. The COFFEE core alone cannot meet the requirements of the modern applications due to the lack of several components of which the Memory Management Unit (MMU) is one of the prominent ones. Since the MMU is one of the main requirements of today’s processors, COFFEE with no MMU was not able to run an operating system. In the design of the MMU, we employed two additional micro-Translation-Lookaside Buffers (TLBs) to speed up the translation process, as well as minimizing congestions of the data/instruction address translations with a unified TLB. The MMU is tightly-coupled with the COFFEE RISC core through the Peripheral Control Block (PCB) interface of the core. The hardware implementation, alongside some optimization techniques and post synthesis results are presented, as well.Another intention of this work is to prepare a reconfigurable platform to send and receive data packets of the next generation wireless communications. Hence, we will further discuss a recently emerged wireless modulation technique known as Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM), a promising technique to alleviate spectrum scarcity problem. However, one of the primary concerns in such systems is the synchronization. To that end, we developed a reconfigurable hardware component to perform as a synchronizer. The developed module exploits Partial Reconfiguration (PR) feature in order to reconfigure itself. Eventually, we will come up with several architectural choices for systems with different limiting factors such as power consumption, operating frequency, and silicon area. The synchronizer can be loosely-coupled via one of the available co-processor slots of the target processor, the COFFEE RISC core.In addition, we are willing to improve the versatility of the COFFEE core even in industrial use cases. Hence, we developed a reconfigurable hardware component capable of operating in the Controller Area Network (CAN) protocol. In the first step of this implementation, we mainly concentrate on receiving, decoding, and extracting the data segment of a CAN-based packet. Moreover, this hardware block can reconfigure itself on-the-fly to operate on different data frames. More details regarding hardware implementation issues, as well as post synthesis results are also presented. The CAN module is loosely-coupled with the COFFEE RISC processor through one of the available co-processor block

SMARAD - Centre of Excellence in Smart Radios and Wireless Research - Activity Report 2008 - 2010

Centre of Excellence in Smart Radios and Wireless Research (SMARAD), originally established with the name Smart and Novel Radios Research Unit, is aiming at world-class research and education in Future radio and antenna systems, Cognitive radio, Millimetre wave and THz techniques, Sensors, and Materials and energy, using its expertise in RF, microwave and millimetre wave engineering, in integrated circuit design for multi-standard radios as well as in wireless communications. SMARAD has the Centre of Excellence in Research status from the Academy of Finland since 2002 (2002-2007 and 2008-2013). Currently SMARAD consists of five research groups from three departments, namely the Department of Radio Science and Engineering, Department of Micro and Nanosciences, and Department of Signal Processing and Acoustics, all within the Aalto University School of Electrical Engineering. The total number of employees within the research unit is about 100 including 8 professors, about 30 senior scientists and about 40 graduate students and several undergraduate students working on their Master thesis. The relevance of SMARAD to the Finnish society is very high considering the high national income from exports of telecommunications and electronics products. The unit conducts basic research but at the same time maintains close co-operation with industry. Novel ideas are applied in design of new communication circuits and platforms, transmission techniques and antenna structures. SMARAD has a well-established network of co-operating partners in industry, research institutes and academia worldwide. It coordinates a few EU projects. The funding sources of SMARAD are diverse including the Academy of Finland, EU, ESA, Tekes, and Finnish and foreign telecommunications and semiconductor industry. As a byproduct of this research SMARAD provides highest-level education and supervision to graduate students in the areas of radio engineering, circuit design and communications through Aalto University and Finnish graduate schools such as Graduate School in Electronics, Telecommunications and Automation (GETA). During years 2008 – 2010, 21 doctor degrees were awarded to the students of SMARAD. In the same period, the SMARAD researchers published 141 refereed journal articles and 333 conference papers

Low-power adaptive control scheme using switching activity measurement method for reconfigurable analog-to-digital converters

Power consumption is a critical issue for portable devices. The ever-increasing demand for multimode wireless applications and the growing concerns towards power-aware green technology make dynamically reconfigurable hardware an attractive solution for overcoming the power issue. This is due to its advantages of flexibility, reusability, and adaptability. During the last decade, reconfigurable analog-to-digital converters (ReADCs) have been used to support multimode wireless applications. With the ability to adaptively scale the power consumption according to different operation modes, reconfigurable devices utilise the power supply efficiently. This can prolong battery life and reduce unnecessary heat emission to the environment. However, current adaptive mechanisms for ReADCs rely upon external control signals generated using digital signal processors (DSPs) in the baseband. This thesis aims to provide a single-chip solution for real-time and low-power ReADC implementations that can adaptively change the converter resolution according to signal variations without the need of the baseband processing. Specifically, the thesis focuses on the analysis, design and implementation of a low-power digital controller unit for ReADCs. In this study, the following two important reconfigurability issues are investigated: i) the detection mechanism for an adaptive implementation, and ii) the measure of power and area overheads that are introduced by the adaptive control modules. This thesis outlines four main achievements to address these issues. The first achievement is the development of the switching activity measurement (SWAM) method to detect different signal components based upon the observation of the output of an ADC. The second achievement is a proposed adaptive algorithm for ReADCs to dynamically adjust the resolution depending upon the variations in the input signal. The third achievement is an ASIC implementation of the adaptive control module for ReADCs. The module achieves low reconfiguration overheads in terms of area and power compared with the main analog part of a ReADC. The fourth achievement is the development of a low-power noise detection module using a conventional ADC for signal improvement. Taken together, the findings from this study demonstrate the potential use of switching activity information of an ADC to adaptively control the circuits, and simultaneously expanding the functionality of the ADC in electronic systems

Partial Discharge Detection and localization Using Software Defined Radio in the future smart grid

Partial discharge (PD) occurs if a high voltage is applied to insulation that contains voids. PD is one of the predominant factors to be controlled to ensure reliability and undisrupted functions of power generators, motors, Gas Insulated Switchgear (GIS) and grid connected power distribution equipment. PD can degrade insulation and if left untreated can cause catastrophic insulation failure. However, PD pulse monitoring and detection can save cost and life prior to plant failure. PD is detected using traditional methods such as galvanic contact methods or UHF PD detection methods. Recently, an alternative method for PD detection and monitoring using wireless technology has become possible. Software Defined Radio has opened new opportunities to detect and monitor PD activity. This research makes use of SDR technology for PD detection and monitoring. The main advantages of SDR technology are that it is cost-effective and it is relatively immune against environmental noise. This is because the noise at electrical power stations is from around a few KHz to a few MHz and this is well below the SDR frequency range and PD frequency band (50-800 MHz). However, noise or interference also exists in the PD frequency band. These interferences are narrow band and mainly from FM, TV broadcasting and mobile telephony signals whose frequencies are well known, thus these interferences can be possibly processed and removed. In this research two SDR products (Realtek software defined radio RTL-SDR/Universal software radio peripheral USRP N200) are used to detect PD signals emitted by a PD source that was located at a distance of 1 m in case of RTL-SDR device while in case of USRP N200 the PD source was located at a distance of 3 m. These PD signals once received by an SDR device are recorded and processed offline in order to localize the PD source. The detected PD signal was around 20 dB above background noise in case of the RTL-SDR device and 25 dB above background noise in case of using the USRP N200. Selecting the appropriate SDR device depends on factors such as high sensitivity and selectivity. Furthermore, although USRP N200 is more expensive than RTL-SDR dongles, USRP N200 was preferred over RTL-SDR as it demonstrates higher sensitivity and overall better results. PD detection using SDR devices was conducted in the frequency domain. These result were validated using a high-end costly device, i.e. spectrum analyzer. Generally, SDR devices demonstrate satisfactory results when compared to spectrum analyzers. Considering that spectrum analyzers cost around £10,000, while a USRP N200 SRD device costs less than £1000, SDR technology seems to be cost-effective. Following PD detection, PD localization was performed using USRP N200 results, and a localization algorithm based on Received Signal Strength (RSS) was adopted. The localization result was within a 1.3-meter accuracy and this can be considered as a relatively good result. In addition, and for the purpose of evaluating the proposed scheme, more experiments were conducted using another system that is based on radiometric sensors which is WSN PD system. The estimated error was 1m in case of using the SDR-USRP N200 system and 0.8 m in case of using the WSN PD system. Results of both systems were very satisfactory, although some results at the corners of the detection grid were not good and the error was higher than 3 meters due to the fact that the RSS algorithm performs poorly at corners. These experiments were used to validate both systems for PD detection and localization in industrial environments

Development of Universal Analyzer for LoRaWAN Using Software-Defined Radio

Práce se zabývá vývojem analyzátoru pro příjem LoRaWAN rámců ve frekvenční oblasti 868 MHz za použití softwarově definovaného rádia pro účely ladění a analýzy zachycené komunikace. Cílem práce je vyvinout software, který by byl schopen pro standardně používané konfigurace přijmout LoRaWAN rámce, demodulovat je a nakonec dekódovat, aniž by byla známá jediná informace o přijatém signálu. Za tímhle účelem byly vyvinuty metody pro detekci šířky pásma a činitele rozprostření, které dosahují vysoké úspěšnosti. Pro zrekonstruování dat byl implementován demodulátor a dekódér, kdy výstup je zprostředkován do softwaru Wireshark. V práci je hojně využíváno pokročilých prostředků pro digitální zpracování signálu jako například FFT a STFT, pracuje se též s analytickým signálem reprezentujícím průběh okamžitého kmitočtu přijatého signálu. Implementovaný software je podroben testu o několika krocích, kde je ověřena jeho funkčnost. V závěru je celé řešení zhodnoceno včetně jeho nedostatků, čímž se otevírají možné směry pro budoucí zkoumání.The thesis deals with the development of an analyzer for the reception of LoRaWAN frames in the 868 MHz frequency band using a software defined radio for the purposes of debugging and analysis of captured communication. The goal of the thesis is to develop software that would be able to receive LoRaWAN frames for standard configurations, demodulate them and finally decode them without knowing any preliminary information about received signal. For this purpose, methods for Bandwidth determination and Spreading Factor determination have been developed. Both methods achieved high success rates. To reconstruct the data, a demodulator and a decoder were implemented. The output of the decoder is passed into the Wireshark software. Advanced tools for digital signal processing such as FFT and STFT are extensively used in the work and an analytical signal representing the instantaneous frequency of the received signal is also used during signal processing. The implemented software is subjected to a test of several steps, where its functionality is verified. In the conclusion, the entire solution is evaluated, including its drawbacks, opening up possible directions for future research.440 - Katedra telekomunikační technikyvýborn