Cognitive Radio Platform Development for Interoperability

Efficient and effective broadband wireless technologies are required to support the foreseeable need to process and exchange large amounts of data. In this Special Issue of the Springer Journal of Signal Processing Systems various disciplines of such efficiency and effectiveness in the context of Software-Defined Radio (SDR) technologies are discussed. At first, a selection of articles focuses on “spectral efficiency”. Since frequency spectrum is known to be scarce and limited, it becomes a resource of increasing value whose utilization must be handled with care. The rapidly growing demand for higher data throughputs in wireless communication does not allow inefficient use of spectrum anymore. Two promising approaches to tackle the “spectral efficiency” issue are Cognitive Radio (CR) and Dynamic Spectrum Access (DSA). A Cognitive Radio is capable of learning and adapting to its transmission environment. Taking e.g. spectrum sensing results into account, the decisions for accessing the scarce frequency spectrum in the most flexible and effective manner are made. CRs are typically based on SDR technology. Thus, the first set of articles puts emphasis on topics in the areas of Cognitive Radio, Cognitive Radio Networks, and Spectrum Sensing. The second selection of articles addresses the problems of “energy efficiency” and “implementation efficiency”. Especially in wireless communications these types of efficiencies are highly important to increase battery time of modern SDRbased platforms. The architectures of such SDRs are typically based on heterogeneous multi-processor hardware platforms hosting General Purpose Processors (GPP), Digital Signal Processors (DSP), Field Programmable Gate Arrays (FPGA) and sometimes even more specific processing elements. Each type of processor has a specific advantage in one or the other performance criterion like energy consumption, flexibility, processing time, throughput, waveform portability, components reusability etc. Whenever a new waveform shall be implemented on an SDR, the system designer has to take the decision for the best possible trade-off between all of these, partly conflicting, criteria under the given circumstances. For instance, the portability of waveform components can be improved by fulfilling the constraints of the Software Communications Architecture (SCA) specification and by using common Application Programming Interfaces (API). Thus, the second set of articles puts particular emphasis on topics in the areas of SDR and SCA Implementations.

Software Defined Radios (SDRs) are wireless communication systems in which many of the radios' important functional elements, including filtering, frequency translation and modulation/demodulation, are implemented in software via Digital Signal Processing (DSP) techniques using embedded processors and Field Programmable Gate Arrays (FPGA) as described by Harris and Lowdermilk in 2010 [1]. SDRs differ from traditional radios that typically employ custom silicon to implement such functions. SDRs exhibit several advantages over traditional radios, including re-configurability and reduced development costs and time-to-market since they do not require custom silicon. SDRs also enjoy long life cycles, since they can readily be adapted to meet future needs. Cognitive Radios (CRs) are a special class of SDRs that employ adaptive DSP technologies to implement spectral management. This enables CRs to deliver improved effective bandwidth, quality of service, and enhanced security, which have become increasingly important in modern commercial and military wireless communications applications.

The system of air-ground communications is one of the most fundamental elements of air traffic control in the National Airspace System (NAS). The current air-ground voice communications systems are using the more than 50-year-old analog voice transmission technology in the licensed air traffic management VHF spectrum band. The limited spectrum is allocated statically based on air traffic control organization and geographic locations. With the increasing use of data applications for air-ground communications, the demand to effectively use the limited spectrum has increased. There are several proposed approaches to solve the projected saturated spectrum in the future. However, these approaches do not address the current practice of static spectrum allocation, which this author anticipates will be a major bottleneck for effective use of the limited spectrum. The current static channel assignment creates inefficient use of the limited spectrum. Regardless of whether a channel is used, it is permanently assigned to the particular geographical areas and organizations. This prevents other users from using the channels when the channels are idle. Also, this static allocation of the spectrum constrains the reassignment of channels and creates a long transition period for moving the existing analog system to a new digital system. The emerging Cognitive Radio (CR) technology provides the opportunity to address the static allocation of spectrum issue and offer a more flexible transition approach for updating the legacy air-ground radio system. The emerging CR technology provides the sensing of surrounding environment, allowing the radio to adapt to the environment accordingly. Built on software-defined radio (SDR) technology, CR is able to employ these features with the cognitive engine and the aid of several sensors. The cognitive engine carries out these tasks by obtaining all available information from sources such as sensors, protocol layers, a policy engine, and its own hardware, and then interprets, reasons, and makes the optimum decision to adapt. Integrated with ground radio stations and centralized management systems, the CR can dynamically use the available channels based on its actual location, environment condition, and, therefore, maximize the use of the limited spectrum.

A software-defined radio (SDR) is a radio communication system where the major part of its functionality is implemented by means of software in a personal computer or embedded system. Such a design paradigm has the major advantage of producing devices that can receive and transmit widely different radio protocols based solely on the software used. This flexibility opens several application opportunities in hospital environments, where a large number of wired and wireless electronic devices must coexist in confined areas like operating rooms and intensive care units. This paper outlines some possible applications in the 2360-2500 MHz frequency band. These applications include the integration of wireless medical devices in a common communication platform for seamless interoperability, and cognitive radio (CR) for body area networks (BANs) and wireless sensor networks (WSNs) for medical environmental surveillance. The description of a proof-of-concept CR prototype is also presented.

Cognitive radio (CR) technology is a potential solution to the spectrum scarcity problem. In CR systems, the availability of the licensed spectrum portion is monitored by the spectrum sensing process and strategies are applied to use this licensed portion without interfering with active primary users (PU). CR provides a flexible system that secondary users (SU) can make decisions about the spectrum usage at any time by simply configuring corresponding transmission parameters. However, implementation of CR systems can be a challenging task due to characteristic difficulties of the wireless channels introduced by fading. Especially spectrum sensing process is affected by changing channel conditions, which should be considered in order to create a high performance CR system. Robust spectrum sensing is essential for a CR system due to its vital role in the efficient usage of the spectrum. Therefore, several algorithms that are proposed for this issue should contain suitable properties considering realistic channel conditions. Implementation of these algorithms can be realized through software defined radios (SDRs). SDR is a core component of the CR technology and it allows a practical development process with modification on the software rather than hardware. Thus, SDR based approaches to CR problems are quite effective. In this chapter, the state of the art of CR systems are explained in detail by highlighting essential components of the existing studies. Effective approaches to the implementation using SDR systems are given. Moreover, an energy detection based spectrum sensing implementation for 2.4 GHz ISM band is given as an implementation example and channel based spectrum usage is analyzed by using SDR tools LabVIEW and NI USRP-2921 hardware in real-time.

Global Navigation Satellite Systems (GNSS) have become an integral part of modern life, supporting various applications, from precise positioning and navigation to timing and synchronization. However, GNSS signals are vulnerable to natural interferences including various atmospheric disturbances, with ionospheric scintillations being a significant challenge. Ionospheric scintillations, caused by irregularities in the Earth’s ionosphere, introduce rapid fluctuations in the amplitude and phase of GNSS signals. These fluctuations can severely degrade the accuracy and reliability of GNSS receivers, leading to positioning errors and navigation failures. Hence, it is crucial to develop effective mitigation strategies. One of the promising approaches to mitigate ionospheric scintillations is the utilization of Software Defined Radio (SDR) technology in GNSS receivers. SDR allows for real-time adaptation to changing signal conditions, enabling the receiver to detect scintillations and adjust its signal processing accordingly. This adaptability enhances the receiver’s stability against ionospheric disturbances, ensuring more robust and accurate positioning and navigation. In this paper, preliminary results of GNSS SDR (Make: iP-Solutions, Japan) installed at Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram (Guntur) (16.44° N, 80.62° E) are presented. Amplitude scintillation index (S4) variations for different PRNs and subsequent positioning results are interpreted from April to September 2023. The results are compared and validated with those of the co-located Novatel GNSS receiver and NAVIC receiver. Most of the S4 variations correlate well with the S4 values from the Novatel and NAVIC receivers. S4 observations from the Septentrio receiver at Daytona Beach (Florida) are also presented. The results of SDR will be extended further for the development of scintillation mitigation algorithms. We plan to install an SDR and employ similar mitigation strategy at this location in the near future.

In the past, radio systems were designed to communicate using one or two wave forms. As a result, two groups of people with different types of traditional radio were not able to communicate due to incompatibility. The need to communicate with people using different types of equipment can only be solved by using software defined radio (SDR). SDR is a wireless device that works with any communication system, be it a cellular phone, a pager, a WI-FI transceiver an AM or FM radio, a satellite communications etc. In both hardware and software Digital Signal Processing (DSP) techniques are used to design a real time system. For SDR applications involving high complexity, it becomes impossible to stay with single sampling rate and so altering the sampling rate at different stages is required for low cost DSP hardware. Thus variable sampling rates i.e. Multirate Digital Signal Processing is required.

Orthogonal frequency division multiplexing (OFDM) is a modulation and multiplexing technique widely used in communication systems since it guarantees reliable communications on noisy wireless channels. However, OFDM implementation is a very challenging task due to the high peak-to-average power ratio (PAPR) value. In this work, a PAPR reduction technique is applied, namely clipping technique. This technique is evaluated in a real OFDM system, which is implemented in software defined radio (SDR). We present and demonstrate that is possible to increase the range and coverage through the efficient use of the transmission power when the PAPR reduction technique is applied in the SDR technology. The results show that when a clipping technique with a threshold of 60% is used, the power required is reduced in 33.75%. Additionally, the reduction percentage could be transformed into 9.58% of increase in range and 63.07% in coverage.

In this thesis, we describe a method for uniquely identifying a specific radio among nominally similar devices using a combination of software defined radio (SDR) sensing capability and machine learning (ML) techniques. Our approach of radio fingerprinting applies ML over raw I/Q samples without specifically selecting features of interest. It distinguishes devices using only the transmitter hardware-induced signal modifications that serve as a unique signature for a particular device. No higher level decoding, feature engineering, or protocol knowledge is needed, further mitigating challenges of ID spoofing and coexistence of multiple protocols in a shared spectrum. Advances in SDR technology allows unprecedented control on the entire processing chain, allowing modification of each functional block as well as sampling the changes in the input waveform. We first demonstrate RF impairments by modifying the operational blocks in a typical wireless communications processing chain in a simulation study. We then generate over-the-air dataset compiled from an experimental testbed of SDRs such as B210 and X310 and train the data using an optimized deep convolutional neural network (CNN) architecture that gives good classification accuracy. We describe the parallel processing needs and choice of several hyper parameters to enable efficient training of the CNN model. We then compare the performance quantitatively with alternate techniques such as support vector machines and logistic regression. Overall our results show that we can achieve up to 90-99% experimental accuracy at transmitter-receiver distances varying between 2-50 feet over a noisy, multi-path wireless channel--Author's abstract

With recent advances in semiconductor processing technology and the development of reconfigurable devices, high bit-rate software-defined radio (SDR) has become practical for commercial applications. This paper proposes an SDR receiver platform based on a new substrate integrated waveguide six-port structure. This SDR receiver platform operates from 22 to 26 GHz and it is designed to be robust, low cost, and suitable for different communication schemes. In this study, the receiver is demonstrated to support quadrature phase-shift keying and 16 quadrature amplitude modulation schemes. System-level simulation is made and prototype circuits are fabricated to evaluate the system performance. It is found that the combination of SDR and six-port technology can provide a great flexibility in system configuration, a significant reduction in system development cost, and also a high potential for software reuse. The proposed receiver shows a possible application of universal direct demodulator for future SDR terminals in various wireless communication systems.

The increase in the consumer demand and the exponential growth for wireless systems, which enables consumer to communicate in any place by means of information, has in turn led to the emergence of many portable wireless communication products. The present research works primarily targets to integrate as much as signal processing applications in a single portable device. Since integration through software applications compromises system speed, integration through hardware will be the better compliment. Software Defined Radio (SDR) Technology yields to achieve this small form factor system while keeping power consumption under the limit. SDR enables soft changeable system functionality, such as receiver demodulation technique .In this implementation two type modulation techniques are used, ASK and FSK. The flexibility of changing the receiver functionality in runtime is usually attained by FPGA. However, using a complete FPGA for reconfiguration of a particular functionality is not an efficient method in terms of power consumption and switching time. We proposed a SDR architecture using a recent advancement in FPGAs, called Partial Reconfiguration (PR). PR helps to change certain portion of FPGA, while the rest keeps functioning. It also reduces the total hardware usage and hence the power. The different demodulation technique and other signal processing application from an external memory unit can be loaded into FPGA PR modules while the other parts of FPGA doing a constant data processing.

Software Defined Radio (SDR) or Software Radio is one of the most important technologies for the modern wireless communication system. SDR is a radio which can tune to any frequency band, implement different modulation and demodulation schemes and different standards in the same device by using reconfigurable hardware and powerful software. SDR provides flexible, upgradeable, multi-standard and longer lifetime radio equipment for both the military and for civilian wireless communications infrastructure. This paper provides a detailed analysis of SDR hardware and its operation focusing on analog front end and digital front end. The paper also highlights the benefits of SDR. However, it was expected to implement SDR since the third generation of wireless communication but many practical challenges that SDR faces, causing SDR evolution slower. This paper also discusses some of the current issues impacting the SDR technology and investigates how they are being resolved.

The Software-Defined Radio (SDR) concept was originally developed by the combined efforts of various research groups in the private and government organizations of the United States (US) in 1970s–1980s. The important ones to mention are the US Department of Defense Laboratory and a team at the Garland, Texas Division of E-Systems Inc. In 1991, Joe Mitola independently reinvented the term ‘Software Radio’ (SR) in cooperation with E-Systems as a plan to build a true software-based GSM transceiver (Mitola, Telesystems Conference, 1992). The SR platform essentially processes almost all the transceiver algorithms as software for a processor. This includes nearly all layers of transmission. However, an optimal implementation of physical layer is always challenging due to an enormous amount of mathematical computation. Over the period of time, many developmental changes occurred and an interesting feature of cognition was added to existing SDR platforms, thereby inventing the term ‘Cognitive Radio’. The main idea was to reduce over-sampling by the analog to digital converter, reduce on-chip processing and to target only the spectrum of interest. This book also touches the CR feature in the large SDR field in some of its selected chapters. Since, the very first few articles of J. Mitola, there has a been a tremendous amount of research work conducted in industry and academia. The evolution in SDRs is continuous with time and provides a number of excellent opportunities to researcher for exploration and to come up with their findings. The present day SDR implementations are such that the designers are focused mostly on the design of hardware and software, their interfacing and optimizations for varying architectural choices. It includes multiple cases of application-specific general-purpose acceleration platforms that are scalable, homogeneous and heterogeneous in nature while providing multiple programmable cores on a single chip computing system.

Software-defined radio (SDR) is widely used in wireless research because it allows communication algorithms to be rapidly developed, tested, and validated over the air (OTA) using flexible hardware and software platforms, enabling rapid prototyping across the physical (PHY) and medium access control (MAC) layers. As wireless environments become increasingly dynamic and adversarial, artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to enhance adaptability and resilience in SDR-based systems, particularly in cognitive radio (CR) and wireless security. However, a gap remains in the literature: few surveys link AI/ML methods with their specific SDR implementations and provide insights into what works or does not work in practice. This survey addresses that gap by focusing on two broad domains: (i) CR, where we trace the evolution from classical SDR prototypes (energy/cyclostationary/eigenvalue sensing, spectrum mobility, MAC design) to AI-enabled approaches (shallow learning, deep networks, and reinforcement learning for access and handoff); and (ii) cybersecurity for SDR-enabled radios, where we synthesize jamming, spoofing, and eavesdropping studies from classical toolchains to AI/ML-based detection and mitigation. We compile representative SDR testbeds and toolkits, providing a structured taxonomy that emphasizes what AI/ML can alleviate (such as manual thresholding and moderate hardware drift) and what remains challenging (including wideband real-time scanning on commodity SDRs, resilience to correlated noise and impairments, as well as the burdens of dataset management and retraining). The outcome is a practical roadmap for AI in SDR, guiding researchers and practitioners from initial concepts to reproducible, hardware-supported experiments.

In this paper, the deployment of a radio monitoring system using software-defined radio (SDR) technologies is addressed. The main advantage of using software-defined radio is its reconfigurable ability and flexibility to set the communication parameters when monitoring radio signals. We propose a feasible architecture of spectrum monitoring in which a control center is built using GNU Radio platform; some remote monitoring stations are built with SDR devices. At each remote station, an SDR dongle embedded with Realtek RTL2832U chip and a USRP (universal software radio peripheral) device are used to measure radio signals. Through TCP/IP network, measurement results or complex sampled data from the SDR devices can be sent to the monitoring center. Some key measurement tools for radio monitoring such as spectrum analyzer and audio/video player are also implemented in the proposed system. The major benefit of the proposed architecture is the cost-effective and flexible platform as compared with traditional spectrum monitoring systems. Moreover, both the radio signals and demodulated audio/video content can be remotely monitored at the control center.